Level of Commitment to Engineering Education
in a Polytechnic in Singapore
KWEK Siew Wee
Master of Science Bachelor of Science (Magna Cum Laude)
Postgraduate Diploma of Teaching in Higher Education
This thesis is presented in partial fulfilment for the degree of
Doctor of Education at
The University of Western Australia
2018
ii
THESIS DECLARATION
I, Kwek Siew Wee, certify that:
This thesis has been substantially accomplished during enrolment in the degree.
This thesis does not contain material which has been accepted for the award of any
other degree or diploma in my name, in any university or other tertiary institution.
No part of this work will, in the future, be used in a submission in my name, for any other
degree or diploma in any university or other tertiary institution without the prior
approval of The University of Western Australia and where applicable, any partner
institution responsible for the joint‐award of this degree.
This thesis does not contain any material previously published or written by another
person, except where due reference has been made in the text.
The work(s) are not in any way a violation or infringement of any copyright, trademark,
patent, or other rights whatsoever of any person.
The research involving human data reported in this thesis was assessed and approved
by The University of Western Australia Human Research Ethics Committee. Approval
#: RA/4/1/6442.
This thesis does not contain work that I have published, nor work under review for
publication.
Signature:
Date: 18 June 2018
iii
ABSTRACT
Understanding more about students’ perceptions of social and psychological aspects of
the self and of the learning environments in classrooms may lead to higher level of
commitment to engineering education. Guided by four research questions, the purpose
of this study was to investigate the relationship between students’ prior schooling, self‐
efficacy, perception of their learning environment, situational interest generated in the
classrooms, and their academic performance as independent variables, and level of
commitment, as dependent variable, among engineering students during the first year
of their studies at a polytechnic in Singapore.
The study used measuring instruments from the literature, modified to suit the context
of Singapore, to measure the variables in the study: perception of the learning
environment (student cohesiveness, lecturer support, involvement, investigation, task
orientation, cooperation and equity), self‐efficacy, situational interest (triggered
situational interest and maintained situational interest), and level of commitment.
Academic performance was measured by the final scores obtained by students in two
academic modules: Engineering Mathematics and Introduction Engineering (based on
100 marks). Prior schooling was measured using students’ educational background prior
to admitting into polytechnic education (‘O’ Level, ITE, or foreign). Data were collected
from 402 first year students. Correlation and regression were used to examine the
relationships between the variables, to investigate the factors that contribute to
students’ level of commitment to engineering education, and to examine the change in
their level of commitment over the first year of studies.
Multiple linear regression showed that approximately 39% of the variance in the level
of commitment can be accounted for using these independent variables, and that the
most important independent variables in predicting level of commitment are self‐
efficacy, followed by equity and task orientation. No statistically relationship between
students’ level of commitment and prior schooling was found. The findings also showed
that perceptions of the learning environment were positively related to self‐efficacy,
situational interest, and level of commitment. However, there was no statistically
significant relationship between perception of the learning environment and academic
performance. There was also a small drop in students’ level of commitment as they
iv
progressed from the beginning (M = 32.41, SD = 4.79) to the end of their first year of
studies (M = 31.75, SD = 4.65), t(401) = 2.91, p = 0.004. The findings can be used to
direct further research into the factors and experiences that influence level of
commitment to engineering education, and they also point toward practices to address
the gaps.
v
TABLE OF CONTENTS
Chapter 1: Introduction and Overview ......................................................................................... 1
1.1 Context of the Study ......................................................................................................... 1
1.2 Problem Statement and Statement of Purpose ............................................................... 3
1.3 Conceptual Framework ..................................................................................................... 7
1.3.1 Reasons for Choosing the Variables ...................................................................... 8
1.3.2 Description of the Variables .................................................................................. 8
1.4 Research Questions ........................................................................................................ 12
1.5 Significance of Study ....................................................................................................... 13
1.6 Overview of Thesis Chapters .......................................................................................... 14
Chapter 2: Literature Review ...................................................................................................... 16
2.1 History of Polytechnic Education in Singapore ............................................................... 16
2.2 Reform of Engineering Education ................................................................................... 19
2.3 Defining Retention, Persistence and Commitment ........................................................ 21
2.4 Measures for Level of Commitment ............................................................................... 23
2.5 Theories and Models Related to Level of Commitment ................................................. 25
2.6 Cultural Perspective on Level of Commitment ............................................................... 29
2.7 First Year Experience and Level of Commitment ............................................................ 30
2.8 Factors Contributing to Students’ Level of Commitment ............................................... 31
2.9 Prior Schooling ................................................................................................................ 32
2.10 Self‐efficacy ..................................................................................................................... 34
2.11 Learning Environment ..................................................................................................... 38
2.12 Situational Interest ......................................................................................................... 41
2.13 Academic Performance ................................................................................................... 44
2.14 Chapter Summary ........................................................................................................... 45
Chapter 3: Methods .................................................................................................................... 47
3.1 Introduction .................................................................................................................... 47
3.2 Specific Research Questions ........................................................................................... 47
3.3 Background and Selection of the Sample ....................................................................... 49
3.4 Selection of Instruments ................................................................................................. 50
3.4.1 The Self‐Efficacy for Broad Academic Milestone Survey ................................... 51
3.4.2 What Is Happening In this Class Survey ............................................................. 52
3.4.3 Situational Interest Survey................................................................................. 52
3.4.4 College Persistence Questionnaire .................................................................... 53
3.5 Other Measures .............................................................................................................. 54
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3.5.1 Prior Schooling ................................................................................................... 54
3.5.2 Academic Performance ...................................................................................... 55
3.6 Assembling the Instruments ........................................................................................... 55
3.7 Data Collection ................................................................................................................ 56
3.8 Data Analysis ................................................................................................................... 60
3.9 Chapter Summary ........................................................................................................... 61
Chapter 4: Results ....................................................................................................................... 62
4.1 Introduction .................................................................................................................... 62
4.2 Psychometric Investigation of the Situational Interest Scale ......................................... 66
4.3 Psychometric Investigation of the Learning Environment Scale .................................... 69
4.4 Psychometric Investigation of Self‐efficacy Scale and Level of Commitment Scale ....... 70
4.5 Relationships between Perception of the Learning Environment, students’ Self‐Efficacy,
Situational Interest, Academic Performance and Level of Commitment ....................... 73
4.6 Joint Relationships between Learning Environment, Self‐efficacy, Situational Interest,
Academic Performance, and Prior Schooling as Independent Variables and Level of
Commitment as Dependent Variable ............................................................................. 77
4.7 Changes in Students’ Level of Commitment between Time 1 and Time 2 ..................... 79
4.8 Summary ......................................................................................................................... 79
Chapter 5: Summary, Discussion, Implications and Recommendations..................................... 82
5.1 Summary of the Study ..................................................................................................... 82
5.2 Summary of Results ........................................................................................................ 84
5.2.1 Research Question 1: Are the instruments (situational interest scale, learning
environment scale, self‐efficacy scale, and level of commitment scale) valid
and reliable when they are used with first year engineering students in a
polytechnic in Singapore? .................................................................................. 84
5.2.2 Research Question 2: What are the relationships between students’
perception of the learning environment, self‐efficacy, situational interest,
academic performance, and level of commitment? .......................................... 85
5.2.3 Research Question 3: What is the joint relationship between students’
perception of the learning environment, self‐efficacy, situational interest,
academic performance, prior schooling as independent variables and level of
commitment as dependent variable? ................................................................ 86
5.2.4 Research Question 4: What change is there in students’ level of commitment
to engineering education between time 1 and time 2? .................................... 86
5.3 Discussion ......................................................................................................................... 87
5.3.1 Research Question 1: Are the instruments (situational interest scale, learning
environment scale, self‐efficacy scale, and level of commitment scale) valid
and reliable when they were used with first year engineering students in a
polytechnic in Singapore? .................................................................................. 87
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5.3.2 Research Question 2: What are the relationships between students’ perception
of the learning environment, self‐efficacy, situational interest, academic
performance, and level of commitment? .......................................................... 87
5.3.3 Research Question 3: What is the joint relationship between students’
perception of the learning environment, self‐efficacy, situational interest,
academic performance, prior schooling as independent variables and level of
commitment as dependent variable? ................................................................ 90
5.3.4 Research Question 4: What change is there in students’ level of commitment to
engineering education between time 1 and time 2? ........................................ 92
5.4 Implications ....................................................................................................................... 93
5.4.1 Measure of Situational Interest, Learning Environment, Self‐efficacy and Level
of Commitment for Polytechnic Students ......................................................... 93
5.4.2 Strategies to Increase Students’ Self‐Efficacy in Engineering ............................. 94
5.4.3 Strategies to Increase Students’ Situational Interest in Engineering .................. 95
5.4.4 Policy to support students during first year of engineering education ............... 96
5.5 Recommendations for Future Research ......................................................................... 97
References .................................................................................................................................. 99
Appendices ................................................................................................................................ 122
Appendix A: Comparison of the Wording of the Original versus the Modified and Extracted
Version of Self‐Efficacy for Broad Academic Milestone Survey .................................... 122
Appendix B: Comparison of the wording of the original and modified Version of ‘What Is
Happening In this Class?’ (WIHIC) Instrument .............................................................. 124
Appendix C: Comparison of the Wording of the Original and Modified Version of Situational
Interest Survey .............................................................................................................. 128
Appendix D: Comparison of the Wording and Likert Scale Type of the Original and Modified
Version of College Persistence Questionnaire .............................................................. 130
Appendix E: Situational Interest, Learning environment, Self‐efficacy, and Persistence
Questionnaires .............................................................................................................. 132
Appendix F: Results of Analyses using Subsamples .............................................................. 143
Appendix G: Factor loading for Learning Environment Scale ............................................... 157
viii
ACKNOWLEDGEMENTS
I would like to acknowledge my advisor, Emeritus Professor Keith Punch, and thank him
for his support and direction throughout my journey. I especially appreciate his
generous and thorough feedback on my many thesis drafts.
I would like to thank the management of Nanyang Polytechnic for sponsoring me for this
Doctor of Education program. Particularly, I would like to thank Mr. Edward Ho, Deputy
Principal (Development) of Nanyang Polytechnic, for his support in my application for
the sponsorship. I would like to thank Ms. Lim Siew Eng, Deputy Director (Electronics),
and Dr Francis Fung, the former Deputy Director (Infocomm), of School of Engineering
at Nanyang Polytechnic, for their continued support and encouragement on this
research.
Finally, I would like to thank my family and friends for their unending love and support
throughout my journey.
Level of commitment to engineering education in a polytechnic in Singapore
1
Chapter 1: Introduction and Overview
Singapore is concerned about the drop in the number of students joining polytechnic
engineering education and persisting to pursue a diploma or having a career in the field
of engineering. This is because the polytechnics train the pool of middle‐level engineers
that are required by the industry to sustain the economic growth of Singapore. It is
therefore important to understand the potential causes that may contribute to the drop
and examine strategies for increasing retention of students in the engineering
disciplines. Particularly, students’ perceptions of social and psychological aspects of the
self and of the learning environment in the classrooms are important factors that
required closer examination. Therefore, the purpose of this study is to examine the
relationships between students’ perception of their learning environment, the
situational interest generated in the classrooms, their self‐efficacy, academic
performance, students’ prior learning experiences on the one hand, and their level of
commitment to engineering education on the other hand during the first year of studies
at a polytechnic in Singapore. This general purpose leads to the research questions
shown in section 1.4 of this chapter.
In this introductory chapter, some issues pertaining to the scope and relevance of the
research topic are discussed. The chapter also describes the context of the study, the
problem statement, the conceptual framework, the research questions and the
significance of this study.
1.1 Context of the Study
Polytechnic education, a unique feature in the Singapore education system, absorbs
more than 40 per cent of secondary school leavers every year and provides a three‐year
practice‐oriented and industry‐relevant curriculum to tertiary students leading to a
diploma qualification. The polytechnics are unique in that they train the critical middle‐
level worker for business and industry. In order to ensure polytechnic graduates have
good career and academic progression prospects, the government of Singapore has
implemented a series of initiatives in recent years. For example, a new Singapore
Institute of Technology was set up in 2009 to provide an improved upgrading pathway
Level of commitment to engineering education in a polytechnic in Singapore
2
for polytechnic graduates to obtain industry‐relevant degrees. In addition, an Applied
Study in Polytechnics and Institute of Technical Education Review Committee was set up
by the Singapore government in 2013 to examine and propose strategies to strengthen
the applied education pathway in the polytechnics and Institute of Technical Education
(ASPIRE Report, 2014). The outcome of this review has led to the introduction of the
‘Enhanced Internships’ for enrolled students and the ‘Earn and Learn’ programme for
fresh polytechnic graduates which allows them to learn through structured on‐the‐job
training and institution‐based training.
As outlined by the former Prime Minister Goh Chok Tong in 1997, engineering is a major
driver for sustainable social and economic development in Singapore but one of the
challenges facing polytechnic education was getting more bright students to take up
engineering (Ministry of Information & The Arts, 1997). A similar message was echoed
by the current Prime Minister Lee Hsien Loong in his visit to Silicon Valley, USA, in 2016,
that Singapore needs to reposition its conception and importance of engineering to the
Singapore economy (Au Yong, 2016). However, despite the call and efforts in attracting
students to take up engineering, the five polytechnics in Singapore have experienced a
decline in the enrolment in engineering courses, and engineering sciences is one of the
disciplines that fails to attract academically good students to apply for the courses.
Table 1 shows the enrolment in polytechnic diploma courses in engineering sciences
from 2005 to 2014 while Table 2 shows the last aggregate score, aggregate score of the
last student posted to the course, from 2008 to 2014. The aggregate score is the sum
of student’s score of English, two best scores of relevant subject and two best scores of
any subjects in the GCE ‘O’ Level examination (best score is one and the passing score is
six).
The situation remains largely unchanged since 1997. In Prime Minister Lee Hsien
Loong's National Day Rally Speech in 2014, he said:
We should help polytechnic and Institute of Technical Education graduates get
into jobs for which they were trained and have the right skills because very often,
they do courses, they come up well‐qualified in engineering or in building
management or in design or in childcare and then, they go and do something
which is not related. (Prime Minister Speeches and Interviews, 2014).
Level of commitment to engineering education in a polytechnic in Singapore
3
In 2016, the Singapore government announced a twenty percent increase in salary for
fresh graduates who joined the Singapore Public Service as engineers to further boost
engineering capability and talent in Singapore and to attract students to join engineering
as a profession (Ng, 2016).
Table 1
Enrolment in Polytechnic Diploma Courses in Engineering Sciences
Year 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
% of
Students
Enrolled in
Engineering
Sciences
40.1 39.3 37.9 36.1 34.2 32.6 31.5 31.0 30.4 28.9
Note. Calculated from Table 22.5 based on total number of students enrolled in polytechnics, Yearbook of Statistics (Department of Statistics, Singapore, 2005 ‐ 2014).
Table 2
Last Aggregate Score
Discipline 2008 2009 2010 2011 2012 2013 2014
Applied Sciences 16.7 16.1 15.5 15.0 15.1 14.7 14.8
Built Environment 17.7 17.0 16.0 15.3 16.0 15.7 16.1
Business &
Management
14.3 14.3 14.6 14.4 15.0 15.5 16.3
Engineering 19.9 20.0 19.8 19.4 20.1 19.8 20.1
Health Sciences 24.0 21.0 21.0 21.4 22.1 21.2 21.1
Humanities 12.2 12.5 12.7 12.4 12.3 12.6 12.4
Information &
Digital Technologies
19.6 19.4 19.6 19.0 19.7 20.0 19.9
Maritime Studies 16.7 17.3 16.7 16.3 16.7 16.3 16.6
Media & Design 18.0 17.7 17.4 17.0 17.4 17.5 17.6
Note. Extracted from Polytechnic Joint Admission Exercises website over the past 7 years (http://www.polytechnic.edu.sg/polyguide/JAE.html).
1.2 Problem Statement and Statement of Purpose
Similar to the data reported in Table 1, the number of students who are enrolled in the
engineering courses at the polytechnic used in this study has declined over the years,
from 35.2% of total number of students who are enrolled into the polytechnic in 2005
to 27% in 2014. The average graduation rate within three years is 75%, the average
Level of commitment to engineering education in a polytechnic in Singapore
4
graduation rate beyond three years is 12.9% and the average attrition rate hovers
around 15% over the years. Among those who drop out of the engineering courses, an
average of 6.3% are withdrawal cases (due to personal reasons), an average of 5% are
removal cases (due to poor academic performance) and the remaining 3.7% are
transferred cases (transfer to disciplines other than engineering). Among the
withdrawal cases, 81.5% withdrew during their first year of study. The two main reasons
that students quoted when they withdrew from the course were ‘lost interest in the
course’ and ‘unable to cope with the course’.
Although the 15% attrition rate in the engineering courses at the polytechnic in this
study is lower than what is reported in the research study where 30 to 40 percent of the
engineering cohort departed from these fields (Micomonaco & Sticklen, 2010), there is
still a concern among the leaders in government, education and industry about the
shortage of qualified engineers in Singapore. The moderate rate of attrition can be
explained from a cultural perspective. According to Hofstede’s five dimensional analysis
of Singapore culture (Hofstede, n.d.), Singaporeans have a strong traditional Asian value
of family ties and adhere to a hierarchical relationship in society, as a result of Confucian
teaching. Students generally show respect to their parents and tend to conform to their
parents’ wishes regarding course of study or career. Therefore students may well
commit to obtain a diploma in engineering but may not commit to pursue an
engineering degree or advance to an engineering career.
Like many research findings (Jones, Paretti, Hein, & Knott, 2010; Kiser & Price, 2008;
Meadows, Fowler, & Knutilla, 2012), the polytechnic in this study experiences
considerable attrition during the first year of studies (81.5% of the withdrawal cases).
As mentioned earlier, students who withdrew from the engineering courses quoted ‘loss
of interest in the course’ or ‘unable to cope with the workload in the course’ as the two
main reasons for withdrawal. It is thus important for polytechnic lecturers to be aware
of the potential hurdles that can affect student achievement in the first year as research
has shown that persistence within a major beyond the first year is an important
predictor of commitment to graduation in that major (Pascarella & Terenzini, 2005).
Another observable demographic change is in the composition of engineering students
at the polytechnic in this study. There are generally three streams of students who are
Level of commitment to engineering education in a polytechnic in Singapore
5
admitted to polytechnic education in Singapore: students who obtained the Singapore‐
Cambridge General Certificate of Education Ordinary Level (‘O’ Level) qualification after
attending four years of secondary school education in Singapore; students who
completed four years of secondary school education and obtained a certificate after
attending two to three years of technical course at the Institution of Technical Education
(ITE); and foreign students who obtained equivalent qualification from a foreign country
and meet the entry requirements for admission into the polytechnic. Noticeably, the
number of students with ‘O’ level qualification has increased from 58.4% in 2005 to 62.9%
in 2014 and the number of students with ITE qualification has also increased from 21.4%
in 2005 to 27.9% in 2014. On the other hand, the number of students with equivalent
qualification from foreign countries has dropped from 20.2% in 2005 to 9.2% in 2014.
This is mainly due to the change of government policy in recruiting international
students. Table 3 shows the educational background of students who are enrolled in
engineering courses that are offered at the polytechnic under this study from 2005 to
2014.
Table 3 Types of students enrolled in Engineering Courses
Types of
Students
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Student with
GCE ‘O’ Level
qualification
(%)
58.4 59.2 57.8 56.5 73.4 73.3 77 64.9 68.6 62.9
Student with
ITE
certification
(%)
21.4 26.1 22 24.8 13.2 13.1 15.3 28.1 28.2 27.9
Student with
equivalent
qualification
from foreign
country (%)
20.2 14.7 20.2 18.7 13.4 13.6 7.7 8 3.2 9.2
Note. Calculated based on the input provided by the Registrar Office of the polytechinc under this study.
Level of commitment to engineering education in a polytechnic in Singapore
6
In addition, among students with ‘O’ level qualification, about half of them have poorer
academic performance. Many of these students choose to join an engineering course
not because they have an interest in engineering, but because of parental pressure. To
these students, joining an engineering course may be the only academic pathway that
will give them an opportunity to earn a diploma. Students with ITE qualification, on the
other hand, are generally weak in quantitative and analytical skills but strong in hands‐
on activities. This is due to the training that they received at the vocational institute
prior to joining the polytechnic (Albright, 2006). As for the foreign students, most of
them who join the engineering courses have a vested interest in engineering and they
are more motivated to do well in their studies (Koh, et al., 2010).
The personal characteristics brought to the learning situation by this diverse group of
students pose a great challenge for polytechnic lecturers to create a learning
environment that caters to every learner’s needs. Students’ stigma consciousness of
their educational background may also influence their perceived abilities to complete
specific tasks (Hollis‐Sawyer & Saywer, 2008) and academic performance (Brown & Pinel,
2003). It is thus important for polytechnic lecturers to understand students’ prior
schooling as research has shown that students’ backgrounds and characteristics
influence their level of commitment (Pascarella & Terenzini, 2005).
While there is extensive research on students’ persistence in engineering education,
most of these studies were conducted in the context of engineering education outside
of Singapore where they examined the factors that affected the interest, retention and
persistence of engineering students. These studies point to the misalignment of student
expectations, identities, and values with their understanding of the engineering field as
a cause of students not continuing in their engineering education (Eris et al., 2010;
Matusovich, Streveler, & Miller, 2010; Meadows et al., 2012; Sheppard et al., 2010).
Other studies show that pre‐college characteristics, learning communities and
environments are important indicators that help to address the issue of why students
do choose or remain in engineering (Jacobs, 2005). In addition, studies have also
indicated that the first year experiences in engineering education have great impact on
students’ perception of the engineering profession (Brannan & Wankat, 2005; Mena,
Zappe, & Litzinger, 2013; Meyers et al., 2008).
Level of commitment to engineering education in a polytechnic in Singapore
7
In Singapore, similar research findings at the institutes of higher learning are lacking,
especially at the polytechnic level. The studies on polytechnic education in Singapore
focus more on teaching strategies and models, student motivation, attitudes and
achievement, students’ prior knowledge in mathematics and academic performance (Liu
& Chye, 2008; Rajalingam, 2011; Rotgans, 2009; Wang, Liu, & Chye, 2010). From the
research studies that have been done on polytechnic engineering education in
Singapore, we still have an inadequate understanding of the factors that affect students’
level of commitment to engineering education.
It is important then to conduct a study to understand why students are attracted to
disciplines other than engineering, why students continue or leave the engineering
program and how students’ level of commitment to engineering education change
during their first year of studies. Therefore, the purpose of this study is to examine the
relationships between students’ perceptions of their learning environments, the
situational interest generated in the classrooms, students’ self‐efficacy, students’
academic performance, students’ prior learning experiences, and students’ level of
commitment to engineering education during the first year of studies at a polytechnic
in Singapore.
1.3 Conceptual Framework
According to Postlethwaite (2005):
An exploratory study is undertaken in situations where there is a lack of theoretical understanding about the phenomena being investigated so that key variables, their relationships, and their causal linkages, are the subject of conjecture. (p. 2)
In this study, the relationships between five independent variables, prior schooling, self‐
efficacy, perceptions of learning environment, situational interest, academic
performance, and one dependent variable, level of commitment were explored using
quantitative surveys. Although extensive research has been conducted on students’
persistence in engineering education, little is known about students’ level of
commitment to engineering education at the polytechnic level in Singapore. In this
section, reasons for choosing the different variables are given, and each variable is
briefly described.
Level of commitment to engineering education in a polytechnic in Singapore
8
1.3.1 Reasons for Choosing the Variables
There are several theories that informed this study – theory of student departure (Tinto,
1975, 1993, 2006), model of student attrition (Bean, 1980, 2005), theory of student
involvement (Astin, 1993), and student retention model (Veenstra, Dey, & Herrin, 2009).
Although some of these theories are now some 40 years old, they have demonstrated
their usefulness in research, and have also been updated over the years. These theories
are reviewed in greater detail in chapter 2. While these theories explain students’ level
of commitment differently, the basic concept is the same. That is, institutions must
focus on students’ pre‐college characteristics, their classroom experiences during their
first year of study and the inter‐relationships among these variables, in order to support
students’ academic success and commitment beyond first year.
Geisinger and Raman (2013) conducted a comprehensive review of 50 studies over the
last five decades to identify factors that contribute to students’ decision to leave
engineering education. Six broad factors were identified because of this review:
classroom and academic climate, grades and conceptual understanding, self‐efficacy
and self‐confidence, high school preparation, interest and career goals, and race and
gender. They also highlighted that evidence from review of these fifty studies suggested
that engineering students’ commitment to engineering education could be increased if
one or more of these six factors were addressed.
Adapting from the theories and the six broad factors described above, and based on the
context of this study, the conceptual framework for this study is to examine the
relationships between six variables – students’ prior schooling, self‐efficacy, perceptions
of learning environment, situational interest, and academic performance – as
independent variables, and level of commitment as dependent variable. Each of these
six variables is discussed in detail below.
1.3.2 Description of the Variables
Prior Schooling
Pre‐college academic performance and education experiences can affect the
persistence of freshmen (Allen, Robbins, Casillas, & Oh, 2008). As suggested by Bean’s
model of student attrition (Bean, 1980, 2005) and Tinto’s theory of student departure
Level of commitment to engineering education in a polytechnic in Singapore
9
(Tinto, 1975,1993, 2006), pre‐college characteristics (e.g. family support and pre‐college
academic achievement) significantly predicted students’ decisions to commit to and
graduate from college. These characteristics influenced how students perceived their
academic and social experiences and the extent to which they felt integrated into the
institution.
According to the Veenstra’s student retention model (Veenstra et al., 2009), the most
dominant pre‐college characteristics as predictors for first year engineering students’
academic success and commitment were their quantitative skills and confidence in
quantitative skills. Many empirical studies conducted over the years also showed that
having adequate preparation in mathematics and sciences and good academic results
prior to joining the college significantly predicted students’ commitment to engineering
education (Kokkelenberg & Sinha, 2010; Suresh, 2006; Tyson, 2011; Zhang, Anderson,
Ohland, Carter, & Thorndyke, 2004).
As discussed earlier, students in the polytechnic in this study are admitted with ‘O’ Level
qualification, ITE qualification or equivalent qualification from foreign countries. The
foreign students are generally perceived as high achievers, the ‘O’ Level students as
average achievers, while the ITE students are generally perceived as low achievers in
Singapore education system. Past research has indicated social stigma consciousness
and studies have shown that past learning experiences negatively impacted students’
perceived abilities to complete specific tasks, their academic performance and level of
commitment (Brown & Pinel, 2003; Chemers, Hu, & Garcia, 2001; Chen & Usher, 2013;
Pajares & Urdan, 2006; Rotgans, 2009).
Self‐Efficacy
Self‐efficacy refers to one’s belief that he or she can successfully engage in and complete
a given task (Bandura, 1977). How students form beliefs about their self‐efficacy is a
complex process of self‐appraisal which includes selecting, weighting, and integrating
information from multiples sources and it is very much influenced by the students’
cultural background ‐ the family, the school, the workplace, and the community
(Oettingen, 1995).
Level of commitment to engineering education in a polytechnic in Singapore
10
Research has shown that self‐efficacy is highly correlated with choice of and
commitment to engineering programs (Lent, Brown, & Sheu, 1986; Lent et al., 2001).
Students with low self‐efficacy are less likely to commit to engineering education, as
they are more likely to be discouraged when faced with challenging tasks or failures
(Lent R. , Brown, Schmidt, Brenner, & Treistman, 2003). However, students are likely to
have higher self‐efficacy if instructors make themselves available and accessible to
students (Vogt, 2008). Research has also shown that the central role that self‐efficacy
plays in students’ retention in engineering programs may be better understood in
relation to students’ perceptions of their environments (Byars‐Winston, Estrada,
Howard, Davis, & Zalapa, 2010).
Perceptions of Learning Environment
There are many reasons why students leave engineering education. These reasons
include inadequate teaching (Fleming, Engerman, & Williams, 2006), lack of instructors’
guidance and academic support (Suresh, 2006; Haag, Hubele, Garcia, & McBeath, 2007),
lack of personal attention from instructors (Haag et al., 2007). Other reasons include a
mismatch between how the way engineering was taught and the way students learn
(Bernold, Spurlin, & Anson, 2007), lack of engagement with the learning environment
and communities (Fleming et al., 2006), lack of sense of belonging (Marra, Bogue, Shen,
& Rodgers, 2007) and identification with the field of engineering (Stevens, O'Conner,
Garrison, Jocuns, & Amos, 2008).
Tinto’s theory of student departure (1975, 1993, 2006) states that for students to persist,
they need integration into the academic systems and social systems. The theory further
suggests that if integration is to occur, it must occur in the classroom, because the
classroom functions as a gateway for student involvement in the academic and social
communities of a college (Demaris & Kritsonis, 2008). Similarly, Astin’s theory of student
involvement describes that student learning is enhanced when students are actively
involved in learning and when they are placed in situations in which they have to share
learning in some positive, connected manner (Astin, 1987; Pascarella & Terenzini, 2005).
Research has shown that there is an established relationship between learning
environment, level of interest and student achievement (Linnenbrink‐Garcia et al. 2010;
Tobin & Fraser, 1998) and results of studies conducted over the past 20 years have
Level of commitment to engineering education in a polytechnic in Singapore
11
provided convincing evidence that students learn better when they perceive the
learning environment more positively (Fraser, 1994, 2002, 2007, 2012; Tobin & Fraser,
1998). In other words, the instructors have a great deal of control over the learning
environment and may be able to develop lesson plans in order to increase students’
academic performance and commitment.
Situational Interest
Situational interest refers to the accumulation of experiences in the classroom rather
than at a single time. This definition provides a better understanding of how situational
interest may develop into individual interest (Linnenbrink‐Garcia, et al., 2010).
Situational interest has a positive effect on extrinsic motivation, whereas personal
interest has a positive long‐term effect on intrinsic motivation (Schraw & Lehman, 2009).
Students’ interest increases if they perceive that they can successfully complete a task
(Zeldin, Petrokubi, & MacNeil, 2008). If students perceive that they possess the
necessary technical knowledge and skills to be successful in an engineering course, they
are more likely to commit to engineering (Jolly, Campbell, & Perlman, 2004). Even for
low achievers, high interest was reported to have a compensatory effect in which high
interest compensated for lower achievement and lower ability (Renninger, 2000).
Research has shown that students leave engineering due to lack of interest in the field
of engineering (Leuwerke, Robbins, Sawyer, & Hovland, 2004). Further evidence
suggests that students have vague ideas about what an engineer does prior to entering
college and these ideas often remain vague upon arrival, thus leading to the departure
of some students (Marra et al., 2007). The resources, activities, and encouragement or
support offered by the learning environments are thus important in increasing students’
interest in engineering and creating continuity for students to remain in engineering
(Baker & Pomerantz, 2001).
Academic Performance
Many research studies have identified that academic performance is significantly related
to students’ commitment to education (Hagedorn, Moon, Cypers, Maxwell, & Lester,
2006; Kiser & Price, 2008; Leuwerke et al., 2004; Tyson, Lee, Borman, & Hanson, 2007).
Studies also indicate that low academic performances lead students to leave
Level of commitment to engineering education in a polytechnic in Singapore
12
engineering education (French, Immekus, & Oakes, 2005; Leuwerke et al., 2004) or drive
students to other disciplines with less stringent grading policies (Suresh, 2006).
Interestingly, other studies show that students’ decision to leave engineering education
is often unrelated to their ability to succeed in the engineering curriculum but reflects
other problem areas such as lack of interest in the field of engineering (Ohland, Zhang,
Thorndyke, & Anderson, 2004; Pascarella & Terenzini, 2005).
On the other hand, evidence has also shown that academic performance does not
significantly predict student’s commitment to engineering (Marra, Rodgers, Shen, &
Bogue, 2012; Seymour and Hewitt, 1997; Veenstra et al., 2009). Students remain in
engineering even in the event of poor academic performance if they are immersed in a
learning environment with strong staff support and a sense of belonging to the learning
community (Braxton, Hirschy, & McClendon, 2004).
Level of Commitment
Degree commitment and institutional commitment are two types of commitment that
influence students’ decision to stay or leave engineering education. Degree
commitment refers to the extent to which the student is committed to earning a degree
while institutional commitment refers to the extent to which a student is attached to
the college or university (Bean, 2005). Studies show that degree commitment has a
positive and significant effect on whether students remain or leave engineering
education (Perna & Titus, 2005; Robbins et al., 2004). Similarly, institutional
commitment has a positive and significant effect on students’ intent to persist at the
end of their first year of college (Braxton & Lee, 2005; Robbins et al., 2004; Strauss &
Volkwein, 2004).
1.4 Research Questions
The purpose of this study is to examine the relationships between students’ perception
of their learning environments, the situational interest generated in the classrooms,
their self‐efficacy, academic performance, students’ prior learning experiences, and
level of commitment to engineering education during the first year of studies at a
polytechnic in Singapore. The general research questions are:
Level of commitment to engineering education in a polytechnic in Singapore
13
1. What are the psychometric characteristics of the instruments: situational interest,
learning environment, self‐efficacy, and level of commitment, when used with first
year engineering students in a polytechnic in Singapore?
2. What are the relationships between all of the variables in this study ‐ students’
perception of the learning environment, self‐efficacy, situational interest, academic
performance, and level of commitment over their first years of studies?
3. What is the joint relationship between students’ perception of the learning
environment, self‐efficacy, situational interest, academic performance, prior
schooling as independent variables and level of commitment as dependent variable?
4. What change is there in students’ level of commitment to engineering education
over their first year of studies?
The specific research questions that follow from these general research questions can
be found in chapter 3.
1.5 Significance of Study
The reasons why polytechnic students in Singapore are not committed to pursue
engineering studies or careers are not very well understood. While some have claimed
that other disciplines provide an easier education pathway and more glamorous job
opportunities for students, the literatures that are reviewed in chapter 2 of this study
does not reveal research that has investigated the variables that influence polytechnic
students’ commitment to engineering education.
The focus of this study is to examine the nature and impact of polytechnic students’
prior schooling, self‐efficacy, perception of their learning environment, situational
interest generated in the classrooms, and their academic performance as independent
variables, and their level of commitment to engineering education as dependent
variable, during their first‐year of studies. It is hoped that the results of this study may
provide polytechnic lecturers with better insights. With these findings, polytechnic
lecturers can make the appropriate changes in curriculum, teaching methods, classroom
organisation, and the nature of the relationships and communication between lecturers
and students so as to make the engineering courses more appealing to students, and
better in addressing the needs of the learners.
Level of commitment to engineering education in a polytechnic in Singapore
14
This knowledge may also enable planners, administrators, and polytechnic lecturers to
better attract, and support the success of, students in the engineering disciplines.
Knowledge of critical factors that affect success and commitment can mean a higher
rate of engineering diploma completion and career entry. The same results may be used
to identify interventions that are effective in increasing freshman retention in the
engineering education.
In addition, the results of this study may add an additional cross‐cultural dimension on
how values, from the east‐Asian perspective where culture has strong influences over
values and expectancies, impact students’ decisions on whether to commit to
engineering education or careers in Singapore (Ho, Holmes, & Cooper, 2004; Otsuka &
Smith, 2005; Kim & Park 2008; Hwang, 2008).
1.6 Overview of Thesis Chapters
This thesis consists of five chapters. This chapter has introduced the study by presenting
background information and context, and has included the problem statement,
conceptual framework and research questions. Chapter 2 provides a review of the
literature that is relevant to the study. It provides a historical perspective on the
research studies that were done on level of commitment in engineering education, and
the types of instrument that were used to measure level of commitment. This chapter
also deals with past research that involved prior schooling, self‐efficacy, perceptions of
learning environment, situational interest, academic performance, and level of
commitment to engineering education, as well as the relationships among these
variables. Chapter 3 describes the methods used in this study. It deals with the specific
research questions and how the sample was selected. It also includes a description of
the selection, adoption, modification, and assembling of the final data collection
instrument, and the details in administering the survey questionnaire over two time
periods during the first year of studies. Details on how the data are collected and
analysed are also discussed. Chapter 4 reports and discusses the results of the analyses.
These findings can then be used to answer each of the specific research questions. The
chapter begins by discussing the psychometric characteristics of the instruments that
are used in this study. It examines the correlations between the different variables and
investigates the extent to which the independent variables account for the variance in
Level of commitment to engineering education in a polytechnic in Singapore
15
the dependent variable, and determines the predictors for students’ level of
commitment using multiple regression analysis. Then, the difference in level of
commitment at time 1 and time 2 is computed and reported. Finally, chapter 5 provides
a summary of results in terms of the research questions. It also presents the conclusions,
and gives a discussion of the implications of this study, together with recommendations
for future research.
Level of commitment to engineering education in a polytechnic in Singapore
16
Chapter 2: Literature Review
As discussed in Chapter 1, the purpose of this study is to examine the relationships
between five independent variables and students’ level of commitment to engineering
education, as dependent variable, during the first year of studies at a polytechnic in
Singapore. These five independent variables include students’ perception of their
learning environment, the situational interest generated in the classrooms, their self‐
efficacy, academic performance, and students’ prior learning experiences.
This chapter provides a review of the recent literature that is relevant to the study. It
begins by providing a historical perspective on polytechnic education in Singapore in
section 2.1 and a review on the reform of engineering education in section 2.2. This is
followed by defining retention, persistence and level of commitment in section 2.3 and
reviewing past research studies that were done on the instruments used to measure
level of commitment in section 2.4. Many researchers have developed theories related
to level of commitment, particularly for the first year students, and these are discussed
in section 2.5. As this study was conducted in Singapore context, literature on college
retention and persistence from cross‐cultural perspectives, in particular the East Asian
perspective, is reviewed and discussed in section 2.6. Studies have examined the
relationship between first year experience and level of commitment and these are
discussed in section 2.7. Studies have also examined factors contributing to students’
commitment to engineering education and the results of these studies are discussed in
section 2.8. Finally, this chapter also reviews research studies that were done on the
instruments used to examine factors related to prior schooling, self‐efficacy, perceived
learning environment, situational interest, academic performance, and their
relationships with students’ level of commitment in engineering education. These
discussions are found in sections 2.9 to 2.13.
2.1 History of Polytechnic Education in Singapore
Education has always been at the top of the agenda for Singapore government. A unique
feature of the Singapore education system is its robust and broad‐based polytechnic
education. The polytechnics absorb around 40 per cent of secondary school leavers
every year and provide practice‐oriented and industry‐relevant curriculum to students
Level of commitment to engineering education in a polytechnic in Singapore
17
leading to a diploma qualification. A typical secondary school leaver will have
completed six years of primary school education, four years of secondary school
education and passed Singapore‐Cambridge General Education examination, i.e. GCE ‘O’
level qualification. The aim is to equip students with the knowledge and skills to prepare
for employment in business and industry, as well as to prepare them for further
education. The polytechnics are unique in that they train the critical middle‐level
workers for business and industry (The Graduate Employment Survey, 2012).
The development of polytechnic education is tightly linked to Singapore’s economic
development and can be broadly divided into four stages if traced back to independence
in 1965 (Goh & Gopinathan, 2008):
Stage 1 (1965‐1978)
This stage was termed as ‘survival‐driven’ education. In the early sixties, the economy
in Singapore required mostly unskilled and semi‐skilled workers to work in the labour
intensive manufacturing industries. During this period, Singapore Polytechnic was
established in 1954 to train students for the industries. In order to respond to the high
demand of technicians from manufacturing sectors, various company‐based training
centres were set up by Singapore Economic Development Board in the seventies to
provide necessary skill‐intensive training (Goh & Gopinathan, 2008).
Stage 2 (1978‐1997)
This stage was termed as the ‘efficiency‐driven’ education where Singapore shifted from
a labour‐intensive industrialized country to a capital‐intensive economy. A new goal was
established in the eighties to position Singapore as a business centre for the financial,
educational, lifestyle, medical, information technology and software sectors. There was
a shortage of labour at three critical levels, namely skilled labour, the qualified
technicians and engineering personnel, and management trained in modern technology
and techniques. The Singapore Economic Development Board adopted the strategy of
learning from the best in the world by working with foreign governments like France,
Germany, and Japan, to set up institutes of technology to provide technology transfer
to its workforce. Three additional polytechnics were later set up to meet the demand
of industries during this period; Ngee Ann Polytechnic was established in 1981, Temasek
Level of commitment to engineering education in a polytechnic in Singapore
18
Polytechnic was established in 1990 while Nanyang Polytechnic was established in 1992
(Goh & Gopinathan, 2008). The percentage of students who graduated from an
engineering discipline comprised more than 60% of the total number of graduates from
all courses offered in the four polytechnics (Toh, 2012).
Stage 3 (1997 – 2011)
This stage was termed as ‘ability‐driven’ education. With increasing regional and
international competition, the Singapore economy shifted its focus from manufacturing
to services industry, biomedical sciences, chemicals, electronics and engineering. This
resulted in a diversification of Singapore economic structure, with companies moving up
the value chain and intensifying their use of technology while the service sector became
the engine for growth. After the year 2000, Singapore increased its focus on knowledge
and innovation‐intensive activities, especially research and development that focused
on environmental and water technology, biomedical sciences and interactive and digital
media.
The education system was reformed to be ‘ability‐driven’ with a focus on innovation,
creativity and entrepreneurship. Two major national initiatives were launched –
‘Thinking Schools, Learning Nation’ to develop a culture of deep thinking and learning,
and ‘Teach Less, Learn More’ to develop teachers as reflective practitioners (Low &
Joseph, 2011). In tandem with the national initiatives and economic growth, polytechnic
education was expanded in order to stay relevant and keep pace with the educational
and economic change. The mission of polytechnics was not only to provide a wider
range of specialisations to meet market demand, but also to strive for excellence in
research and development programme. The fifth polytechnic, Republic Polytechnic was
established in 2003 during this period (Goh & Gopinathan, 2008). With the changes in
Singapore’s diversified economy structure, there was a steady decrease in the
percentage of students who graduated from an engineering discipline, from 56.63% in
1997 to 31.94% in 2011 (Yearbook of Singapore Statistics, 2012).
Stage 4 (2011 to present)
This stage is termed as ‘student‐centric, values‐driven’ education to deliver a more
holistic and values‐driven education. It aims to instil deep values in students, build deep
Level of commitment to engineering education in a polytechnic in Singapore
19
foundations for learning, provide broad, inclusive and holistic education and encourage
learning for life (Heng, 2011). To keep in tune with the changing economy, an Applied
Study in Polytechnics and ITE Review committee was set up in 2013 to enhance the
education and job prospects for students from the Institute of Technical Education and
polytechnics. The committee released a report in 2014 and made ten recommendations.
These recommendations included establishing education and career guidance to
provide a structured curriculum to help students to understand their strengths better,
to uncover their passions and aspirations for the future, and to develop multiple
pathways to support students at the post‐secondary and tertiary levels to develop their
knowledge and skills in their areas of interest (ASPIRE Report, 2014). This report led to
SkillsFuture, which is a national movement to enable all Singaporeans to develop to their
fullest potential throughout life, regardless of their starting point. There are many
programmes and initiatives under SkillsFuture. Those that are targeted at polytechnic
students include education and career guidance to help polytechnic students to make
informed post‐polytechnic education and career choices, enhanced internships to help
students to make better career choices through real‐world exposure to the industries,
and earn‐and‐learn programmes for fresh polytechnic graduates to learn through
structured on‐the‐job training and institution‐based training (Shanmugaratnam, 2015).
A further decrease in the percentage of students graduating from an engineering
discipline was seen during this period, from 31.94% in 2011 to 28.86% in 2014 (Yearbook
of Singapore Statistics, 2015).
With the historical background of the development of polytechnic engineering
education in Singapore and the declining rate of students graduating from an
engineering discipline, the next section reviews some of the measures that institutions
worldwide, including Singapore, have embarked on to reform engineering education.
2.2 Reform of Engineering Education
The purpose of engineering education is to train students to become successful
engineers who are equipped with the necessary technical expertise and social
awareness, and who are able to find innovative solutions to problems in today’s
environment – an environment that is increasingly based on technologically complex
and sustainable products, processes, and systems. However, engineering education
Level of commitment to engineering education in a polytechnic in Singapore
20
was transformed from a practice‐oriented education in 1950s to a theoretical and
scientific oriented education in the 1980s, with a strong emphasis on technical
fundamentals. This shift resulted in an unintended consequence of producing
engineering graduates who lack the personal, interpersonal, and process, product and
system building skills, as observed by the industry (Crawley, Malmqvist, Ostlund, &
Brodeur, 2007). Indeed, the landscape is different today and nontechnical, professional,
and leadership factors are equally or more important than the technical fundamentals
in order to train students to be successful 21st‐century professional engineers
(Kirschenman, 2011).
To respond to the change and call by industries, many initiatives sprouted from all
around the world to reform engineering education in order to attract and train students
to be future engineers with the desired attributes. Some initiatives included the
worldwide ‘Conceive‐Design‐Implement‐Operate Initiative’ (Crawley et al., 2007),
‘Educating the Engineer of 2020’ in USA (National Academy of Engineering, 2005), ‘Re‐
engineering Engineering Education’ in Europe (Borri & Maffioli, 2007), and the
‘Engineering for the future’ in Australia (King, 2008). Despite the effort in reforming the
engineering education, many countries faced the issues of apparent decline in interest,
enrolment and commitment of young people in engineering, science and technology
(UNESCO, 2010).
In the study conducted by UNESCO in 2010, important issues and challenges faced by
engineering education were lack of public awareness and understanding of engineering,
and declining interest and enrolment of students in engineering courses. The study
highlighted a few factors contributing to such issues and challenges that included
perceptions that engineering and engineering courses were boring and hard, that the
salaries earned by engineers were low compared to other professions, and that
engineering was contributing to the problem of environmental degradation and climate
change, rather than being part of the solution. The study also emphasized that
engineering education could be made more interesting and attract more students using
activity‐based learning, project‐based learning and problem‐based learning, just‐in‐time
approaches and hands‐on application.
Level of commitment to engineering education in a polytechnic in Singapore
21
The same trend of decline in interest, enrolment and commitment in engineering is seen
in Singapore. As reported earlier, the percentage of engineering graduates decreased
approximately from 60% in 1965 to 30% in 2014. In order to boost the engineering
capabilities and develop a thriving engineering ecosystem, the Singapore government
launched a few initiatives. These include the Smart Nation vision in 2014, the
SkillsFuture initiatives in 2015 which allows the industry to work even more closely with
the polytechnics and universities, and a 20 percent pay rise for fresh graduates joining
the public service as engineers in 2016 (Ng, 2016).
The polytechnics and universities in Singapore have also embarked on several initiatives
to reform engineering education. For example, the worldwide ‘Conceive‐Design‐
Implement‐Operate Initiative’, problem‐based learning, project‐based learning, and
design thinking are used to further re‐design the engineering curriculum with the aim of
providing integrated learning experiences for students in the engineering at the
polytechnics. At the university level, examples include the innovative Renaissance
Engineering Programme at Nanyang Technological University that has a multi‐
disciplinary approach bridging engineering, business and the arts. Other examples
include the 4D Curriculum at Singapore University of Technology and Design, which
provides students with multi‐disciplinary learning experiences that encompass the four
pillar in architecture and sustainable design, engineering product design, engineering
systems and design, and information systems technology and design.
The impact of all the initiatives and reform in engineering education has yet to be
measured and the polytechnics continue to face the challenges in having students to
commit to an engineering education and a career in engineering.
2.3 Defining Retention, Persistence and Commitment
The terms retention and persistence are frequently used interchangeably in research
studies. It is common to define retention as an institutional‐level measure of success,
and persistence as a student‐level measure of success (Hagedorn, 2005). From the
literature, there are many definitions of retention. For example, retention is defined as
the percentage of students in a specific cohort who are retained (Reason, 2009). Other
examples include the ability of an institution to retain a student from admission to
graduation (Berger, Ramirez, & Lyons, 2012), or the ability of an institution to retain a
Level of commitment to engineering education in a polytechnic in Singapore
22
student from one semester to the next in a given year, or from one year to the next
(Reason, 2009).
Persistence, on the other hand, refers to student behaviour – the desire, and action of
a student to stay within the system of higher education from first year to degree
attainment. Specifically, persistence is the student‐initiated decision to re‐enrol, making
measurable satisfactory progress through the educational pipeline (Berger et al., 2012;
Mortenson, 2012; Burrus et al., 2013). The definition of persistence is also complicated
because students may leave for one semester for health reasons and then resume
studies in the following semester (Voigt & Hundrieser, 2008). However, there are no
universally accepted definitions of retention and persistence. These two terms may be
closely related but they are not always synonymous (Berger et al., 2012). For example,
a student may successfully persist based on a set of defined goals, but he or she may not
be retained to graduation (Reason, 2009). The definition of these two terms makes
comparison of the results of research studies difficult (Van Stolk, Tiessen, Clift, & Levitt,
2007).
Recent studies reveal a trend in aligning organizational commitment to predict student
retention or persistence (Hogan, 2012). There are three forms of organizational
commitment: affective, normative, and continuance commitment (Meyer & Allen, 1991).
Affective commitment refers to the emotional attachments an individual has to an
organisation; normative commitment refers to a sense of obligation an individual
experiences to remain in an organisation; and continuance commitment refers to the
perceived costs associated with an individual leaving an organisation. Affective
commitment is found to be most strongly associated with job performance and
behaviour, followed by normative commitment and continuance commitment (Meyer,
Stanley, Herscovitch, & Topolnytsky, 2002).
In education settings, affective commitment is defined as the emotional attachment
students have to the institution (Hogan, 2012). Affective commitment further defines
the students’ academic goal or purpose and the students’ behaviour in response to their
levels of commitment resulting in the outcome of staying or leaving the institution.
Normative commitment, on the other hand, is defined as the sense of obligation
students feel because administrators, staff, and instructors had given the students so
Level of commitment to engineering education in a polytechnic in Singapore
23
much while they are with the institution (Hogan, 2012). Lastly, continuance
commitment is defined as the perceived cost students have when they make
comparisons and sense that the financial and emotional costs of leaving the institution
to pursue other opportunities are greater than the cost of staying (Hogan, 2012). Many
studies found the affective commitment to be strongly related to students’ decision to
remain in, or leave, the institution (Bean, 2005; Hogan, 2012; Larkin, Brasel, & Pines,
2013; McNally & Irving, 2010).
This study focuses on affective commitment in education settings and the term, ‘level
of commitment’, is used throughout the rest of the chapters. There are two reasons for
choosing this term – first, ‘level of commitment’ is clearly defined and the complexities
in defining retention and persistence can be avoided; second, commitment, especially
institutional commitment, is found to be a strong predictor of a student staying in or
leaving, the institution (Woosley & Miller, 2009).
2.4 Measures for Level of Commitment
Currently, there are few measures that address student commitment (McNally & Irving,
2010) and it is often measured by a single item on surveys (Hogan, 2012). In 1993, Meyer,
Allen and Smith developed a three‐factor model (affective commitment, normative
commitment, and continuous commitment) to measure organizational commitment
and described how it can be applied to multiple domains. In an organisation, the level
of commitment can influence the level of turnover. Similarly, in education, the level of
commitment can influence the dropout rate (McNally & Irving, 2010). In 2010, Mc Nally
and Irving conducted a study with a sample of 287 undergraduate business students and
found some evidences that the adapted three‐factor model could be used in educational
setting. Similar results were found in the study conducted by Hogan on 362
undergraduate students (2012) where Hogan concluded that the study supported the
viability of using the adapted three‐factor model in educational setting. However, little
evidence was found when the three‐factor model was applied to 230 undergraduate
students in the study conducted by Davis (2014) and to 250 undergraduate students in
the study conducted by Freeman‐Butler (2014).
Thus, instead of using the three‐factor model to measure students’ level of commitment,
this study used and combined the two sub‐scales from the College Persistence
Level of commitment to engineering education in a polytechnic in Singapore
24
Questionnaire, developed by Davidson and his colleagues in 2009. These two subscales
are institutional commitment and degree commitment. Institutional commitment
refers to the extent to which students are confident in and satisfied with their
institutions, whereas degree commitment is the level of importance they attach to
earning a degree. The key items in institutional commitment are students’ intentions
to re‐enrol and to earn a degree from the institution, their confidence in having selected
the right institution, and their thoughts of continuing or stopping. The key items in
degree commitment are students’ intentions to finish the degree, estimates of the
likelihood that a degree will be achieved, and their commitment to earn the degree
(Davidson et al., 2009). Gore (2010) used the questionnaire on 701 undergraduate
students to predict whether freshmen returned for the sophomore year and the findings
demonstrated the validity of the questionnaire. Garrison (2014) conducted a study on
283 first‐semester freshmen to predict which students returned for their sophomore
year and found that institutional commitment was a statistically significant predictor of
enrolment status. Table 4 provides a sample item as adapted from the description
provided by Davidson and colleagues (2009).
Table 4
Sample item in the College Persistence Questionnaire.
Scale Description Sample Item
The degree to which:
Level of
Commitment
Students are confident in and
satisfied with their
institutions, as well as the
level of importance they
attach to earning a degree
At this moment in time, how
certain are you that you will
earn a polytechnic diploma?
The next three sections review theories and models related to students’ level of
commitment, examine students’ level of commitment from a cultural perspective, and
discuss the importance of first year experiences in post‐secondary education.
Level of commitment to engineering education in a polytechnic in Singapore
25
2.5 Theories and Models Related to Level of Commitment
Many theories and models related to level of commitment are found in the literature.
The three most comprehensive and discussed theories are: Tinto’s theory of student
departure, Bean’s model of student attrition and Astin’s theory of student involvement.
Although each of these theories is now some 40 years old, they have demonstrated their
usefulness in research, and have been updated over the years. These theories or models
examine the process of student development and highlight the importance of students’
background and individual characteristics, and how students’ experiences in the
learning environment affect their decision to commit to stay in, or depart from, the
institution.
Tinto’s Theory of Student Departure
Tinto’s theory of student integration (1975), formulated more than 40 years ago,
suggested that students’ level of commitment was linked to both social integration and
academic integration. Social integration refers to students fitting into a social group of
the learning environment, and having the necessary support systems to develop a
positive experience in that environment. Academic integration, on the other hand,
refers to student acceptance of academic expectations and measures of academic
success such as passing grades and academic goal commitment. The model proposed
that the level of success a student had in his or her social and academic integration,
influenced their level of commitment to academic and career goals (Tinto, 1975).
Tinto’s model focused on pre‐college characteristics which included family background
(social status, education of the parents and community size), skills and abilities
(intellectual and social skills, financial resources, motivations and political preference),
prior schooling (educational preparation and experience), and academic integration
(course grade), as well as social integration (relationships with students and discussion
with faculty).
The model was validated by the empirical evidence from many studies (Barnett, 2006;
Hurtado et al., 2007; Meeuwisse, Severiens, & Born, 2010; Pascarella & Terenzini, 2005;
Tinto, 2006), showing that it was a conceptually useful framework for research related
to level of commitment.
Level of commitment to engineering education in a polytechnic in Singapore
26
In 2006, Tinto expanded his theory to include psychological and organizational
constructs such as student expectations, peer relationships, faculty‐student interactions,
and so on. However, the model also received considerable criticism. In particular, the
model’s emphasis on integration that excluded external factors such as finances and
supports from friends and family. These external factors could impact students’ level of
commitment. Others questioned the reliability of the model for non‐traditional
students and minority students (Burrus et al., 2013; R. Longwell‐Grice & H. Longwell‐
Grice, 2008; Rovai, 2003).
Bean’s Model of Student Attrition
Bean’s model of student attrition (1980) emphasized the importance of interaction with
faculty. It also proposed that student’ background characteristics, such as prior
academic performance and socioeconomic status, determined students’ commitment
to stay or depart from the institution. In 1995, Bean posited that there was a strong
relationship among beliefs, attitudes, behaviours, and intentions. His research found
students’ attitudes about the institution influenced their behaviour and decisions to
leave, and behaviour was a strong indication of level of commitment. He also suggested
that the social psychological factors as well as the external environmental influences
contributed to students’ level of commitment (Eaton & Bean, 1995).
Bean (2005) identified nine factors that contributed to student attrition. These nine
factors include institutional environment factors, student demographic characteristics,
commitment, academic preparation and success factors, psychosocial and study skills
factors, integration and fit, student finances, environment pull factors and intentions.
Bean’s model was validated on traditional student populations and non‐traditional ones
(Johnson, Wasserman, Yildirim, & Yonai, 2014; Pascarella & Terenzini, 2005; Rovai,
2003).
Astin’s Theory of Student Involvement
Astin’s theory of student involvement (1984) took a different approach from that of
Tinto in looking at the process of college student retention and development. The
theory revolved around the impact of student involvement on student outcomes in
college, and his essential assertion was that students must be actively engaged in their
Level of commitment to engineering education in a polytechnic in Singapore
27
surroundings in order to learn and grow in college. The types of involvement include
academic involvement, extracurricular involvement, peer interaction, and interaction
with faculty and staff.
In short, Astin proposed that students, who were involved devoted significant energy to
academic work, spent time on campus, participated actively in student organizations
and activities, and interacted with faculty. On the other hand, uninvolved students
neglected their studies, spent little time on campus, abstained from extracurricular
activities, and rarely initiated contact with faculty or other students (Astin, 1984; Roos,
2012).
Many research studies validated Astin’s theory and found that these types of
involvement were positively correlated with favourable development outcomes and
increased level of commitment (Astin, 1993, 1999; Kuh, 2003; Pascarella & Ternzini,
2005).
In summary, Tinto’s theory of student departure highlighted that students’ goals and
external commitments were key factors in their success and commitment and that they
needed to excel both academically and socially in the learning environment. Bean’s
model of student attrition, on the other hand, advocated that students enter the
institution with unique characteristics over which they had little control. The
interactions that occurred between students and the institution could be in many forms
and on multiple occasions. However, these interactions did not automatically integrate
students into the learning environment. The students themselves determined the
extent to which they belonged during these interactions. Similarly, Astin’s theory of
student involvement also highlighted that institutions had little control over their inputs,
but institutions had a great deal of control over the learning environment into which
they placed their students.
More recently, Veenstra and colleagues (2009) argued that while the above three
theories were comprehensive, they did not differentiate between the liberal arts
programs and the STEM programs which included the engineering programs. They also
highlighted that engineering education was considered uniquely different from the
other disciplines as engineering education focused heavily on mathematics and sciences
with an emphasis on analytical thinking, whereas there were fewer or almost no
Level of commitment to engineering education in a polytechnic in Singapore
28
mathematics and science courses in other disciplines, thus leading to different issues in
level of commitment.
Veenstra and colleagues (2009) concluded that a model of engineering student
retention was needed to specifically address the characteristics of an engineering
education, especially for first year engineering students. In this model developed by
Veenstra and colleagues (2009), the overall academic preparedness and quantitative
skills developed prior to joining institutions of higher education were important for
students’ level of commitment in the first year of engineering studies. In addition,
students’ attitudes towards engineering and confidence in mathematics, science and
computers also contributed to their level of commitment in the first year of engineering
studies. The model was applied to the 2004 and 2005 freshman classes at the University
of Michigan and the results showed that high school academic achievement,
quantitative skills, commitment to career and education goals, and confidence in
quantitative skills predicted students commitment to an engineering major in the first
year of engineering studies (Veenstra et al., 2009).
Other researchers in recent studies also echoed similar views. Van den Bogaard (2012)
did a comprehensive review of literature of nearly 100 publications on engineering
education and concluded that while there were similarities between research into
engineering and non‐engineering students’ level of commitment, there were differences
in the predictor variables for the engineering students. Thus, engineering education
required subtly different approaches to research and interventions (Van den Bogaard,
2012). Xu (2016) also argued that the course work in STEM majors was more demanding
than in non‐STEM majors, and thus factors that influenced students’ commitment to
stay or depart were different in these two fields.
Contrary to the argument above that engineering was different from other disciplines,
there were studies which aligned with those of Seymour and Hewitt (1997), who found
that students who chose to leave a STEM major were not ‘different kinds of people’ from
those who chose to stay in a STEM major. In 2008, Ohland and colleagues conducted a
study comparing students who were committed to engineering studies and students
who were committed to non‐engineering studies in the USA, and found that there were
no significant differences in how these two groups of students rated the quality of their
Level of commitment to engineering education in a polytechnic in Singapore
29
education and their level of commitment. In another study conducted by Araque,
Roldan and Salguero (2009), comparing students who decided to leave the arts,
humanities and computer engineering of a university in southern Spain, they found
similar factors that explained their departure from the three disciplines. That is,
students with weak educational strategies and without a high level of commitment to
achieve their goals had low academic performance and low success rates and this led to
their decision to leave their disciplines.
As this study was conducted in the Singapore context, the above theories and models,
as well as factors that contribute to students’ level of commitment to engineering
education may be understood in different ways and carry a different weight. The next
section reviews literature that has examined level of commitment from cross‐cultural
perspectives, in particular from, the East Asian perspective.
2.6 Cultural Perspective on Level of Commitment
Otsuka and Smith (2005) posited that culture had strong influence on the predictors for
achievement and motivation, such as ability beliefs, self‐concept and self‐efficacy. How
one valued education, how one socialized and interacted with one another, how one
learned, and how one interpreted achievement and commitment were all different from
culture to culture. Otsuka and Smith (2005) concluded that there were limitations of
the expectancy‐value model of achievement and motivation in East Asian beliefs
although it was a well‐established theory in Western psychology. Contrary to the typical
western students’ view that ability‐related beliefs were the most important predictors
of achievement, East Asians strongly believed that effort and hard work were more
important than ability‐related beliefs in order to predict their educational choices
(Otsuka & Smith, 2005).
Ho and colleagues (2004) found that for East Asians, perseverance and effort led to
success and the motivation for success was related to the obligation to the family. Starr
(2012) advocates the same view that a key factor in scholastic achievement for East
Asian students was parental involvement, especially mothers, who were deeply involved
in this process. Similarly, Kim and Park (2008) argued that for East Asians, the emphasis
was on collective individual values. Effort is believed to lead to success and ability can
be acquired through persistent effort (Kim & Park, 2008; Hwang, 2008; Starr, 2012).
Level of commitment to engineering education in a polytechnic in Singapore
30
In Singapore, traditional Asian values of family ties, filial piety and interdependence
remain paramount to the average Singaporean (Chao & Tseng, 2002). Often, students
do not openly challenge the teacher, they generally show respect to their parents and
tend to conform to their parent’s wishes regarding course of study or career. According
to Fong and Yuen (2016), the emphasis of always showing humility and modesty placed
by parents and teachers on children, could condition students to downplay their own
competence when they were asked to rate themselves in interviews or questionnaires.
It is therefore important to take note of Singaporean cultural perspectives when
designing and creating a survey instrument to examine factors that affect students’
perception, motivation, achievement and commitment in engineering education. It is
also equally important when making hypotheses, interpretations and generalization of
phenomena based in different cultures.
2.7 First Year Experience and Level of Commitment
Research showed that the highest loss of students occurred during the first year of post‐
secondary education. Students left the structure of a classroom based school system
that they were familiar with when they entered post‐secondary education. As they
started to adapt to a new post‐secondary education structure, they might feel
overwhelmed especially during the first year of their post‐secondary education. These
challenges included adapting to the new environment, creating a new network of friends,
and dealing with long hours of project work and higher workload due to the demands
of the engineering curriculum (Seymour & Hewitt, 1997; Nelson & Kift, 2005; Sheppard,
Macatangay, Colby, & Sullivan, 2009). All these factors may impact a student’s decision
to remain in or leave the institution during the first year of studies.
Research also showed that initial success influenced academic success in engineering
(Mendez, Buskirk, Lohr and Haag, 2008). The existence of initial success might later
influence students’ decision to stay in, or leave, the institution (Nelson & Kift, 2005;
Krause, Hartley, James, & McInnis, 2005; Baik, Naylor, & Arkoudis, 2015). Therefore, it
is important for the institution to determine factors that influence first year students’
level of commitment and how the institution can influence these experiences (Van den
Bogaard, 2012).
Level of commitment to engineering education in a polytechnic in Singapore
31
2.8 Factors Contributing to Students’ Level of Commitment
The seminal work by Seymour and Hewitt (1997) described multiple factors that
influenced undergraduate students’ decision to stay in, or leave, STEM. From the
qualitative data collected through interviewing students who left STEM, there are many
reasons provided by students switching out of STEM. The top seven reasons, in rank
order, were: loss of interest in the STEM disciplines; growing interest in other disciplines;
poor teaching by STEM faculty; overwhelmed by the pace and load of STEM curriculum;
choosing STEM for reasons that proved inappropriate; inadequacies in the provision of
advising or counselling; and insufficient high school preparation (Seymour & Hewitt,
1997). Although there have been widespread efforts over the past 15 to 20 years to
address these factors, there are still gaps in knowing how these factors influence
students’ commitment in engineering education. Several comprehensive reviews of
empirical studies that were conducted in recent years to examine factors that
contributed to students’ commitment to engineering education are described below.
Veenstra and colleagues (2009) reviewed 56 empirical studies by comparing the pre‐
college characteristics of engineering and non‐engineering students that contributed to
students’ academic success and their decision to remain in, or leave, the institution.
They found that these characteristics included high school academic achievement,
quantitative skills, study habits, commitment to career and educational goals,
confidence in quantitative skills, commitment to enrol college, financial needs, family
support, and social engagement. Van den Bogaard (2012) reviewed 33 empirical studies
which included engineering and non‐engineering degree programmes and concluded
that there were a few variables that seemed to matter when conducting studies on
students’ success in degree completion. These variables included students’ ability,
student background, student disposition, student behaviour, education attributes and
education climate. Jiang and Freeman (2011) reviewed 22 empirical studies and found
that both high school GPA and mathematics scores from standard examinations like SAT
could partially predict engineering undergraduates’ academic success and level of
commitment in college. Failing key courses that were necessary to majoring in a
particular field, college GPA, and freshman GPA in college, as well as loss of interest
could affect engineering students’ level of commitment. Geisinger and Raman (2013)
Level of commitment to engineering education in a polytechnic in Singapore
32
reviewed 50 empirical studies on student attrition from engineering programs and
identified six broad factors that influenced students’ decision to stay or leave
engineering. These six factors were classroom and academic climate, grades and
conceptual understanding, self‐efficacy and self‐confidence, high school preparation,
interest and career goals, and race and gender. Kuley, Maw and Fonstad (2015)
conducted a comprehensive review on 45 empirical studies in engineering discipline and
identified three distinct categories of factors that influenced students’ decision to leave,
or stay in, engineering: college level factors, instructor level factors, and student level
factors.
In summary, there are many factors contributing to students’ level of commitment to
engineering education. However, none of these studies fully explains why some
students commit to stay while others decide to leave engineering education. Some of
the common factors from these reviews include students’ previous academic experience,
self‐efficacy, academic and social engagement with teachers and peers, and first year
academic performance (Allen & Robbins, 2010; Allen et al., 2008; Pascarella & Terenzini,
2005; Westrick, Robbins, Radunzel, & Schmidt, 2015).
The next five sections outline factors that were included in this study. These factors are
relevant to the context of the Singapore polytechnic education system: prior schooling,
self‐efficacy, learning environment, situational interest, and academic performance.
Each of these factors is reviewed separately, along with relevant literature supporting
or questioning their importance to students’ level of commitment to polytechnic
engineering education in Singapore, as well as the instruments used to measure these
factors, where appropriate.
2.9 Prior Schooling
Empirical studies consistently showed strong support for the relationship between
students’ pre‐college academic ability and level of commitment, whether in engineering
education or non‐engineering education (Allen et al., 2008; Mattern & Patterson, 2009;
Richardson, Abraham, & Bond, 2012). Similarly, many studies also supported the
relationship between students’ pre‐college performance and level of commitment
(Allen et al., 2008; DeBerard, Spielmans, & Julka, 2004; French et al., 2005; Porchea,
Allen, Robbins, & Phelps, 2010; Robbins et al., 2004). Some studies noted that having
Level of commitment to engineering education in a polytechnic in Singapore
33
adequate mathematics and science preparation prior to joining the post‐secondary
education could predict students’ commitment to engineering programs (Kokkelenberg
& Sinha, 2010). It is logical that students, who are academically well prepared, will tend
to have better academic achievement and higher level of commitment when they
pursue their post‐secondary education.
In the context of this study, rather than using a standard score to represent students’
pre‐college academic ability and performance, the term prior schooling was used to
denote the educational background of students prior to admitting into polytechnic
education. The educational background of students can largely be divided into three
categories: students with a Singapore‐Cambridge General Certificate of Education
Ordinary Level (‘O’ Level) qualification; students with a certificate awarded by the
Institute of Technical Education (ITE); and students with an equivalent ‘O’ Level
qualification from a foreign country.
In the Singapore educational system, students are streamed into different academic
pathways after they complete six years of primary school education. The top 10% of the
students proceed to integrated program where students sit for the Singapore‐
Cambridge General Certificate of Education Advanced Level examination after attending
four years of secondary and two years of junior college education in selected secondary
schools. The next 50% of the students proceed to the express stream where students
sit for the ‘O’ Level examination after attending four years of secondary school
education. About 30% of the students proceed to the normal academic stream where
students sit for the Singapore‐Cambridge General Certificate of Education Normal
Academic (‘NA’ Level) and about 10% of students proceed to normal technical stream
where students sit for Singapore‐Cambridge General Certificate of Education Normal
Technical Level examination (‘NT’ Level) after attending four years of secondary school
education. Students who performed well in the ‘NA’ Level examination take ‘O’ Level
examination in the fifth year of secondary school education. Students who do not do
well in either ‘O’, ‘NA’ or ‘NT’ Level examination will proceed to the Institute of Technical
Education (ITE) for a three‐year or two‐year vocational education and will receive a
certificate upon graduation.
Level of commitment to engineering education in a polytechnic in Singapore
34
The criteria for admission to a polytechnic vary and depend on students’ educational
background. Thus, because it is hard to compare students’ educational background
using a standard test score, prior schooling was used instead in this study to denote the
three different educational backgrounds of students. Prior schooling would provide a
good indicator in this study on how well prepared the students were prior to joining the
polytechnic engineering education.
2.10 Self‐efficacy
Self‐efficacy refers to one’s belief that he or she can successfully engage in and complete
a given task (Bandura, 1977). It is most strongly influenced by one’s previous
performance (Chen & Zimmerman, 2007) and experience, and it changes over time
(Raelin et al., 2014).
Self‐efficacy is believed to have powerful effects on level of commitment (Lent et al.,
2003; DeWitz, Woolsey, and Walsh 2009; Eccles & Wigfield, 2002), achievement
(Bandura, 1986; Gore, 2006; Lent et al., 2003; Robbins et al., 2004; Zajacova, Lynch, &
Espenshade, 2005), and interest (Lent, Singley, J. Schmidt, L. Schmidt, & Gloster, 2008).
Academic self‐efficacy, on the other hand, is specific to the academic context and refers
to one’s belief that he or she can successfully complete academic tasks (Chemers et al.,
2001; Hampel, Meier, & Kummel, 2008). Research studies showed that perceived
academic efficacy had great impact on students’ academic success and the academic
choices they made (Schunk & Pajares, 2005). Students’ beliefs about their academic
capabilities had also been shown to influence their performance in a variety of academic
subjects, their interest and effort, and their level of commitment to subsequent
academic and career choices (Pajares & Urdan, 2006). This belief, however, might stay
at the same level regardless of their GPAs (Gaylon, Blondin, Yaw, Nalls, & Williams, 2012).
A few studies in Singapore examined students’ academic self‐efficacy (Yeo & Tan, 2012).
Amil (2000) investigated academic self‐efficacy and self‐regulated abilities of Singapore
Junior College students taking Economics and found that there was a significant, positive
correlation between academic self‐efficacy and academic performance, and between
academic self‐efficacy and self‐regulated learning. Chong (2007) examined the
relationships between academic self‐efficacy and academic self‐regulation in a group of
Level of commitment to engineering education in a polytechnic in Singapore
35
Secondary 1 students in Singapore and found that academic self‐efficacy was positively
correlated with academic self‐regulation, but there were no differences due to students’
gender or prior achievement. Lau, Liem and Nie (2008) examined academic self‐efficacy
and task value and found that self‐efficacy was a predictor to English test scores. Yeo
and Tan (2012) examined the relationship between Secondary 1 students’ attributional
style and their perceived academic self‐efficacy and found that students’ attributional
style was positively associated with their academic self‐efficacy for self‐regulated
learning but there were no gender and ability differences for academic self‐efficacy.
In the domain of engineering, many researchers found that students were very much
affected by their academic self‐efficacy in their choices to pursue and persist in
engineering (Bandura, 1977; Pajares, 1996). Some studies involved investigating the
role of self‐efficacy in engineering education (Ponton, Edmister, Ukeiley, & Seiner, 2001);
others involved measuring engineering students’ self‐efficacy (Hutchison, Follman,
Sumpter, & Bodner, 2006; Marra et al., 2007; Towle et al., 2005), increasing students’
self‐efficacy (Hutchison et al., 2006; Ponton, 2002), and examining factors in the
engineering programs that impacted students’ academic self‐efficacy (Hutchison et al.,
2006).
In the area of self‐efficacy and situational interest, Niemivirta and Tapola (2007)
highlighted that studies that explicitly investigated the links between self‐efficacy and
situational interest were scant. Some studies showed that the relationship between
self‐efficacy and situational interest was reciprocal (Lent & Brown, 2006; Nauta, Kahn,
Angell, & Cantarelli, 2002; Niemivirta & Tapola, 2007; Tracey, 2002). Other studies
showed that self‐efficacy was linked to situational interest at the specific task level but
not necessarily at the general subject level (Ainley, Hidi, & Berndorff, 2002; Hidi & Ainley,
2008).
In the area of self‐efficacy and academic achievement, Vogt’s (2008) research on
undergraduate engineering students across several institutions in USA reported that
self‐efficacy was a strong predictor of academic achievement. Jones et al. (2010)
conducted an analysis of 363 first year engineering students and reported that the
largest predictor of student engineering GPA were expectancies for success in
engineering and engineering self‐efficacy. Purzer (2011) also did a study using
Level of commitment to engineering education in a polytechnic in Singapore
36
sequential mixed‐methods to examine the relationship between team discourse, self‐
efficacy and achievement. Results confirmed that self‐efficacy was positively and
significantly correlated with academic achievement. Similarly, Loo and Choy (2013)
examined 178 third‐year engineering students in a polytechnic in Singapore and found
that self‐efficacy sources were correlated with mathematics achievement scores as well
as cumulative GPA of electronics‐related engineering diplomas.
In the area of self‐efficacy and learning environment, many studies suggested that
students who perceived their teachers as more caring had significantly higher academic
self‐efficacy (Fast et al., 2010; Murdock & Miller, 2003; Patrick, Ryan, & Kaplan, 2007).
Students were more likely to seek help when they needed it and developed a wide range
of competencies when they felt emotionally supported by their teachers (Crosnoe,
Johnson, & Elder, 2004; Pianta, Hamre, & Stuhlman, 2003). Similarly, Vogt’s (2008)
research on undergraduate engineering students reported that the level of faculty
interaction had a strong correlation with self‐efficacy.
In the area of self‐efficacy and level of commitment, Eris and colleagues (2010)
conducted a study on 141 engineering students over a period of four years and found
that students’ confidence in mathematics and science skills were correlated with their
level of commitment in engineering programs. On the other hand, Lent and colleagues
(2010) suggested that efforts to promote engineering students’ self‐efficacy may offer a
viable means to solidify students’ intentions to continue to commit to engineering.
In terms of measuring self‐efficacy, Bandura (2006, p. 307) argued that there was no
‘one measure fits all’ for self‐efficacy. He emphasized that the items should be designed
in the context and domain of functioning and task demands. According to Mamaril
(2014), there were generally three ways to measure academic self‐efficacy in the
engineering domain: general self‐efficacy measures, modified self‐efficacy measures by
adapting general measures to the engineering domain, and task specific self‐efficacy
measures by creating self‐efficacy measures for specific engineering skills.
The general self‐efficacy measures are designed to measure students’ beliefs in their
capabilities to perform academic tasks. Students are asked to judge their general
confidence to function successfully in engineering without an explicit reference to
particular problems or tasks (Mamaril, 2014). For example, the Patterns of Adaptive
Level of commitment to engineering education in a polytechnic in Singapore
37
Learning Scale (Midgley et al., 2000), the Generalized Self‐Efficacy Scale (Schwarzer &
Jerusalem, 1995), and the Self‐Efficacy for Learning and Performance Scale of the
Motivated Strategies for Learning Questionnaire (Pintrich, Smith, Garcia, & McKeachie,
1991).
The modified self‐efficacy measures modified items in existing general self‐efficacy
instruments to make general self‐efficacy measures domain specific. For example, Fantz,
Siller, and DeMinranda (2011) adapted the MSLQ instrument by replacing the generic
label of ‘class’ with ‘engineering classes’. Similarly, Jones et al. (2010) adapted self‐
efficacy items from the Self‐Efficacy for Broad Academic Milestone Survey created by
Lent et al. (1986) by adding the word ‘engineering major’ to place the students in context
when they were asked to rate their confidence in their abilities.
The task specific self‐efficacy measures assess skills‐specific self‐efficacy in engineering.
For example, Kinsey, Towle, O’Brien, and Bauer (2008) designed a measure specifically
for engineering students’ self‐efficacy for spatial tasks. Similarly, Carberry, Lee and
Ohland (2010) designed a measure to include items based on a model of design process
while Schubert, Jacobitz, and Kim (2012) designed a measure to include items based on
a ten‐step engineering design process.
In this study, the intention was to measure the confidence of first year polytechnic
engineering students in terms of their ability to perform well academically and not so
much in specific engineering tasks. Thus, this study adopted the Self‐Efficacy for Broad
Academic Milestone Survey created by Lent and colleagues (1986) and modified the
items in this instrument to suit the polytechnic engineering education context in
Singapore. Table 5 provides a sample item as adapted from the description provided by
Lent and colleagues (1986).
Table 5
Sample item in the Self‐Efficacy for Broad Academic Milestone Survey
Scale Description Sample Item
The degree to which:
Self‐efficacy Students are confident to
perform well academically
Excel in your engineering
programme over the next
semester
Level of commitment to engineering education in a polytechnic in Singapore
38
2.11 Learning Environment
Research studies conducted over the past 20 years showed that the quality of the
learning environment in schools was an important factor for student learning (Fraser,
1994, 1998). Regardless of students’ background and characteristics, students learned
better when they perceived the learning environment positively (Dorman, Adams, &
Ferguson, 2003). Learning environment was also widely known as an important element
of successful schools and an influential predictor of students’ academic success
(Pascarella & Terenzini, 2005). Tynjälä, Salminen, Sutela, Nuutinen and Pitkänen (2005)
found that students’ perceptions of the learning environment were related to their
study orientations. In turn, students’ study orientations were related to academic
success. Vogt (2008) looked at the role faculty played in students’ academic success and
found that if faculty distanced themselves from the students, students had lower self‐
efficacy, academic confidence and GPA. Conversely, if faculty had good relationships
with the students and made themselves available to the students, students’ academic
confidence increased and thus had a positive effect on self‐efficacy. Many researchers
found that academic performance, self‐efficacy, self‐esteem, and outcome of the
motivational task and sense of management in learning situations could be modified by
students’ perception of the learning environment (Appleton, Christenson, Kim, &
Reschly, 2006, Gilman & Anderman 2006, Urdan & Schoenfelder 2006). Others also
found that students’ adjustment, commitment to learning and academic achievement
were linked to their perceptions of a supportive learning environment (Brand, Felner,
Shim, Seitsinger, & Dumas,2003; Eliot, Cornell, Gregory, & Fan, 2010).
Similarly, in STEM education, Seymour and Hewitt (1997) found that poor teaching by
STEM faculty was one of the top four factors contributing to the decisions of
undergraduates whether to commit or not commit to STEM education. In 2012,
Seymour and her team embarked on a five‐year mixed‐methods research project to
further explore what had and had not changed in the learning experiences of
undergraduates in STEM majors since the original study in 1997, and with what
consequences for student persistence. They subsequently confirmed many of the
original findings (Conolly & Hunter, 2012).
Level of commitment to engineering education in a polytechnic in Singapore
39
Xu (2016) also found that the most influential factor in students’ commitment to STEM
education was institutional control over the academic quality and learning environment.
Xu (2016) recommended that one potential intervention was to improve the teaching
quality in STEM courses in order to increase students’ level of commitment to STEM
education as poor teaching diminished students’ confidence and interest (Graham,
Byars‐Winston, Hunter, & Handelsman, 2013). Other empirical studies also showed that
students who did not commit to STEM education cited reasons related to the learning
environment. These reasons included inadequate teaching and advising (Marra et al.,
2012; Fleming et al., 2006; Johnson, 2007; Lagoudas, 2009; Marra et al., 2007) and lack
of faculty guidance and academic support (Fleming et al., 2006; Haag et al., 2007; Suresh,
2006). Other reasons included lack of personal encouragement and attention from
faculty members (Johson, 2007; Lagoudas, 2009; Haag et al., 2007) and mismatches
between the way engineering was taught and the way students learned (Bernol9d et al.,
2007).
In order to measure the quality of the learning environment, it is important to examine
relevant dimensions of the learning environment (Cosmovici, Idsoe, Bru, & Munthe,
2009). These dimensions include academic and emotional support that students receive
from teachers, support for student autonomy, students’ participation, task orientation,
innovation, and cooperation (Fraser, 2012). Many instruments measure students’
perception of the learning environment. Among those that were used in the research
studies conducted in Asia were My Class Inventory (Majeed, Fraser, & Aldridge, 2002),
the Questionnaire on Teacher Interaction (Goh & Fraser, 1998), and Science Laboratory
Environment Inventory (Quek, Wong & Fraser, 2005; Fraser & Lee, 2009). Other
instruments included Constructivist Learning Environment Survey (Fraser, 2012),
Technology‐Rich Outcomes‐Focused Learning Environment Inventory (Welch, Cakir,
Peterson, & Ray, 2012) , and What is Happening in this Class questionnaire (Fraser,
Aldridge & Adolphe, 2010; Peer & Fraser, 2015; Lim, 2013). The What is Happening in
this Class (WIHIC) questionnaire was of particular interest for this study.
Fraser, Fisher, & McRobbie (1996) developed the WIHIC questionnaire. The
questionnaire combined modified versions of the most salient scales from a wide range
of existing questionnaires with additional scales that accommodate contemporary
educational aspects such as equity and constructivism. Fraser (2002) reported that
Level of commitment to engineering education in a polytechnic in Singapore
40
Asian researchers who had translated the WIHIC questionnaire into different Asian
languages completed many studies and cross‐validated the questionnaire. Dorman
(2003, 2008) again validated that the WIHIC was a robust, valid and reliable tool to
assess learning environments, including cross‐cultural contexts. Since 2008, many other
studies also provided evidence to support the validity and reliability of the WIHIC
questionnaire. These studies included the research conducted by Khoo and Fraser (2008)
on 250 working adults in Singapore about their perceptions of their computer education
courses; and the research conducted by Chionh and Fraser (2009) on 2310 Secondary 3
students in Singapore comparing the differences in their perceptions of the geography
and mathematics learning environments. Other studies included the research
conducted by Fraser, Aldridge and Adolphe (2010) comparing the learning environment
in science classes between 567 students in Australia and 594 students in Indonesia; and
the research conducted by Helding and Fraser (2013) on 924 grade 8 to 10 students in
USA on their perception of the science learning environment.
The WIHIC questionnaire consists of seven eight‐item scales ‐ Student Cohesiveness,
Teacher Support, Involvement, Investigation, Task Orientation, Cooperation and Equity.
It has a five‐point scales ranging from Almost Never, to Very Often. All the seven eight‐
item scales were used in this study with slight modification of the words used in the
items to suit the context of this study. Table 6 provides a scale description and a sample
item for each of the seven scales as adapted from the description provided by Afari,
Aldridge, Fraser and Khine (2013).
Table 6
Scale description and sample item for each scale in the WIHIC questionnaire.
Scale Description Sample Item
The degree to which:
Student
Cohesiveness
Students know, help, and are
supportive of one another
I make friendships easily
among students in this class.
Teacher Support The teacher helps, befriends,
trusts, and is interested in
students
The teacher goes out his/her
way to help me.
Level of commitment to engineering education in a polytechnic in Singapore
41
Scale Description Sample Item
Involvement Students have attentive
interest, participate in
discussions, do additional
work, and enjoy the class
I discuss ideas in class.
Investigation The emphasis is on the skills
and process of inquiry and
their use in problem solving
and investigation
I carry out investigations to
test my ideas.
Task Orientation It is important to complete
activities planned and to stay
on the subject matter
Getting a certain amount of
work done is important to
me.
Cooperation Students cooperate rather
than compete with one
another on learning tasks
I cooperate with other
students when doing
assignment work.
Equity Students are treated equally
by the teacher
The teacher gives as much
attention to my questions as
to other students’ questions.
2.12 Situational Interest
Lack or loss of interest was the top reason why students remained in, or left, STEM
education according to the seminal study conducted by Seymour and Hewitt (1997).
Since then, many studies confirmed this finding (Leuwerke et al., 2004; Marra et al.,
2007; Conolly & Hunter, 2012; Lynch, Seery and Gordon, 2011). In fact, level of interest
among other variables was a significant predictor for choice of further studies or career
path (Bøe, 2012; Bøe & Henriksen, 2013; Mujtaba & Reiss, 2013). Interest development
was supported by the content, tasks and the organization of the learning environment
(Renninger & Hidi, 2011) but interest development depended on the learner’s prior
knowledge (Hidi & Renninger, 2006; Wigfield, Eccles, Schiefele, Roeser, & Davis‐Kean,
2006). Level of interest was also found to be an important factor that influenced
students’ academic performance (Ainley et al., 2002; Bybee & McCrae, 2011; Lynch et
al., 2011). Interestingly, there are limited studies that explicitly investigate the
Level of commitment to engineering education in a polytechnic in Singapore
42
relationship between level of interest and self‐efficacy and the findings showed that
they had a bidirectional relationship (Lent et al., 2006; Nauta et al., 2002; Tracey, 2002).
There are generally two types of interest: individual interest and situational interest.
Individual interest can be defined as a deep personal connection to the domain and a
willingness to re‐engage in the domain over time (Ainley et al., 2002; Hidi & Renninger,
2006; Krapp, 2005; Renninger, 2009; Schiefele, 2009). Situational interest, on the other
hand, depends on aspects of the environment including the ways the learning situation
is organised (Ainley et al., 2002; Hidi & Renninger, 2006; Krapp, 2002; Schiefele, 2009).
However, situational interest can be the starting point for long‐term interest
development (Krapp, 2002) in a specific domain. According to Hidi and Renninger (2006),
there were four phases in interest development: triggered situational interest,
maintained situational interest, emerging individual interest and well‐developed
individual interest. The four phases were sequential and distinct, and represented a
form of cumulative and progressive development.
In this study, the focus was on triggered situational interest and maintained situational
interest. Triggered situational interest refers to a psychological state of interest that
result from short‐term changes in affective and cognitive processing (Hidi & Baird, 1986,
1988; Mitchell, 1993). At the moment the situational interest is triggered, students will
have immediate and affective reactions such as focused attention, increased cognitive
functioning, persistence and affective involvement (Hidi & Renninger, 2006; Jack &
Lin, 2014; Krapp & Prenzel, 2011). However, situational interest may decrease, fluctuate,
or be maintained after it is triggered and it depends on the task and learner
characteristics (Durik & Matarazzo, 2009; Tulis & Fulmer, 2013). Maintained situational
interest, on the other hand, refers to a psychological state of interest that is subsequent
to a triggered state. It involves focused attention and persistence over an extended
period in time, and may or may not reoccur. Thus, Linnenbrink‐Garcia and colleagues
(2010) further classified maintained situational interest into maintained‐situational‐
interest‐feeling and maintained‐situational‐interest‐value. The former refers to
students experiencing positive affect toward the domain through instructional support
and the latter refers to students cognitively finding meaning and personal usefulness in
the domain through instructional support.
Level of commitment to engineering education in a polytechnic in Singapore
43
There are a few measurement tools available to assess situational interest with respect
to the learning environment. Mitchell (1993) developed a scale to measure situational
interest in secondary school mathematic classrooms. Chen, Darst, and Pangrzi (2001)
developed a scale to examine the influence of the five dimensional source: novelty,
challenge, attention demand, exploration intention, and instant enjoyment on
situational interest. Hoffman (2002) developed a scale to assess students’ interest in
physics. Linnenbrink‐Garcia and colleagues (2010) developed a new measurement tool,
the Situational Interest Survey, to measure situational interest for use in varying
domains and developmental levels. This tool measured students’ situational interest as
an accumulation of experiences in the classroom rather than at a single slice in time. It
was designed to test a three‐factor model of situational interest, yielding separate
measures for triggered situational interest, maintained‐situational‐interest‐feeling, and
maintained‐situational‐interest‐value.
Linnenbrink‐Garcia and colleagues (2010) administered the Situational Interest Survey
to undergraduate students and secondary mathematics students in three separate
studies and reported good internal consistency. Their results supported the distinction
between situational interest and individual interest as well as the three‐factor
situational interest model. In addition, situational interest was also shown to be a
significant predictor of individual interest. Subsequently, the survey was used in many
studies in different domains and developmental levels and good internal consistency
were reported in these studies (González & Paoloni, 2015; Linnenbrink, Patall &
Messersmith, 2013; Matis 2013; Plass et al., 2013).
The Situational Interest Survey developed by Linnenbrink‐Garcia and colleagues (2010)
was used in this study as it could be used to understand the types of learning
environments and instructional practices that could be designed to promote situational
interest in the development of individual interest. There were twelve items which were
rated on a five‐point Likert scale ranging from 1 (not at all true) to 5 (very true) to test a
three‐factor model of situational interest, yielding separate measures for triggered
situational interest, maintained situational interest (feeling), and maintained situational
interest (value). Table 7 provides a scale description and a sample item for each of the
three scales as adapted from the description provided by Linnenbrink‐Garcia and
colleagues (2010).
Level of commitment to engineering education in a polytechnic in Singapore
44
Table 7
Scale description and sample item for each scale in the Situational Interest Survey.
Scale Description Sample Item
The degree to which
students’ interest is
generated through:
Triggered Situational
Interest
the presentation of course
material that grabbed
students’ attention
My class is so exciting it’s easy
to pay attention
Maintained
Situational Interest
(Feeling)
the extent to which the
material itself was
enjoyable and engaging
I like what we are learning in
this module this semester
Maintained
Situational Interest
(Value)
whether the material was
viewed as important and
valuable
What we are learning in this
module this semester can be
applied to real life
2.13 Academic Performance
Many research studies identified first year grade point average (GPA) as a strong
predictor for students’ level of commitment (Allen et al., 2008; Allen & Robbins, 2010;
Mendez et al., 2008; Pascarella & Terenzini 2005; Westrick et al., 2015). There are also
many research studies that point to students’ lack of proficiency in mathematical and
analytical skills as the main reason why students remain in, or leave, engineering
education (Leuwerke et al., 2004; Louis, & Mistele, 2011; Mattern, Radunzel, & Westrick,
2015; Tolley, Blat, McDaniel, Blackmon, and Royster, 2012).
There is little research; however, that investigates the relationships between academic
performance in other key introductory modules and the level of commitment to
engineering education. Suresh (2006) found that poor performance in key introductory
engineering modules caused students to question their decision to pursue engineering.
Araque et al. (2009) found that level of commitment was dependent on the module
studied. Bao, Edwards, Koenig, & Schen (2012) found that students’ perceptions of
introductory engineering modules, and the time commitment required to pass these
modules, were often wrong and resulted in students leaving engineering. Thus, learning
Level of commitment to engineering education in a polytechnic in Singapore
45
about the relationship between academic performance of students in key introductory
engineering modules and level of commitment to engineering education offers another
perspective on students’ level of commitment to engineering education.
In this study, the relationships between academic performance in two key introductory
modules in the first year ‐ mathematics and introduction to engineering ‐ and level of
commitment to engineering education were investigated.
2.14 Chapter Summary
This chapter reviews literature pertaining to the level of commitment of first year
polytechnic engineering students that are relevant to this study. Section 2.1 provided
an overview of the four stages of development of polytechnic education in Singapore
since independence in 1965: ‘survival‐driven’, ‘efficiency‐driven’, ‘ability‐driven’, and
‘student‐centric, values‐driven’. A steady decrease in the percentage of students
graduated from engineering disciplines in Singapore were reported during these four
stages. Section 2.2 reviewed initiatives that institutions worldwide, including Singapore,
embarked on to reform engineering education but the impact of these initiatives had
yet to be studied at the polytechnic level in Singapore.
Section 2.3 reviewed and discussed the definition of retention, persistence and level of
commitment that was used in past studies. This section also provided the reasons for
using the definition of level of commitment in this study. Section 2.4 reviewed the
instruments that were used to measure level of commitment and the results showed
that there were not many that were used in past research studies. Two instruments
were reviewed and discussed. These two instruments are the three‐factor model to
measure organizational commitment and the College Persistence Questionnaire. The
decision to use the two sub‐scales of the College Persistence Questionnaire in this study
was based on its validity and reliability, as reported in various past studies.
Section 2.5 reviewed theories and models that were related to level of commitment:
Tinto’s theory of student departure, Bean’s model of student attrition, Astin’s theory of
student involvement. Contradictory views were found in past research studies on
whether there were differences in the predictor variables between the engineering and
non‐engineering students. Section 2.6 reviewed literature that examined level of
Level of commitment to engineering education in a polytechnic in Singapore
46
commitment from the cultural perspectives; in particular, the East Asian perspective as
this study was conducted in Singapore. Section 2.7 reviewed literature related to first
year experience and students’ level of commitment while section 2.8 reviewed literature
on factors that contribute to students’ level of commitment. After reviewing the
literature in these sections, the conclusion was that none of these studies fully explained
why some students committed to stay while others decided to leave engineering
education after their first year of studies. Thus, it is important for this study to
determine factors that influence first year students’ level of commitment to engineering
education.
Sections 2.9 to 2.13 reviewed past research studies that were done on the instruments
that were used to examine factors related to prior schooling, self‐efficacy, perceived
learning environment, situational interest, academic performance, and their
relationships with students’ level of commitment in engineering education. Instruments
that were well supported by past studies became the thread connecting all these factors.
In this study, students’ educational background (‘O’ Level, ITE, Foreign) instead of a
standard test score was used to denote students’ prior schooling. The Self‐Efficacy for
Broad Academic Milestone Survey was used as the instrument to measure self‐efficacy.
The What is Happening in this Class questionnaires (WIHIC) was used as the instrument
to measure perceived learning environment. The Situational Interest Survey was used
as the instrument to measure situational interest. Finally, students’ results in two key
introductory modules in the first year (mathematics and introduction to engineering)
were used to measure academic performance.
The next chapter focuses on the methods used in this study, detailing its design, sample,
instruments and methods of data collection and analysis.
Level of commitment to engineering education in a polytechnic in Singapore
47
Chapter 3: Methods
3.1 Introduction
This chapter describes the research design and the methods used for investigating the
research questions formulated in this study. First, it describes the background on how
the specific research questions were formulated and how the sample was selected.
Second, the selection, adoption, modification, and assembling of the final instrument,
and the details in administering the survey over two periods are described. Third, the
details on how the data were collected and analysed are reported.
3.2 Specific Research Questions
As discussed in Chapter 2, the Situational Interest Survey, the What is Happening in this
Class questionnaires (WIHIC), the Self‐Efficacy for Broad Academic Milestone Survey and
the College Persistence Questionnaires were used successfully in many countries. These
instruments were used with first year engineering students in a polytechnic in Singapore
for the first time. In addition, some slight modifications to the instruments were carried
out to suit local context. These modifications can influence the reliability and validity of
the instruments. Therefore the first research question is:
Research Question 1
Are the following instruments valid and reliable when used with first year engineering
students in a polytechnic in Singapore?
a. Situational interest scale
b. Learning environment scale
c. Self‐efficacy scale
d. Level of commitment scale
In addition, chapter 2 reviewed previous research regarding relationships between first‐
year students’ perception of their learning environment and self‐efficacy, the situational
interest stimulated in these environments, as well as their academic performance and
level of commitment to engineering education. To determine whether there are such
relationships in this study over a one‐semester period, the second set of research
questions is:
Level of commitment to engineering education in a polytechnic in Singapore
48
Research Question 2a
What are the relationships between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, and level of commitment at
the end of first semester (time 1)?
Research Question 2b
What are the relationships between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, and level of commitment at
the end of the second semester (time 2)?
Chapter 2 also reviewed previous research studies regarding the identification of key
factors of students’ commitment to engineering education. To determine such factors
from the present sample which were collected at two time periods, the third set of
research questions is:
Research Question 3a
What is the joint relationship between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, prior schooling as
independent variables and level of commitment as dependent variable at time 1?
Research Question 3b
What is the joint relationship between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, prior schooling as
independent variables and level of commitment as dependent variable at time 2?
As reviewed in Chapter 2, past research studies showed that first‐year students’
commitment to engineering education differs over their first year of studies. Similarly,
this study investigates the difference between students’ commitment to engineering
between time 1 and time 2. The fourth specific research question is:
Research Question 4
What change is there in students’ level of commitment to engineering education
between time 1 and time 2?
Level of commitment to engineering education in a polytechnic in Singapore
49
3.3 Background and Selection of the Sample
This research was based on a quantitative survey targeting about 1000 first year
students who were enrolled in two modules in 2014, Engineering Mathematics and
Introduction to Engineering, in the School of Engineering at the polytechnic in this study.
As described in chapter 1, these first year students have different prior learning
experiences and background. Generally, students’ prior schooling can be categorised as
students with ‘O’ Level qualification (‘O’ Level), students with ITE qualification (ITE), and
students with equivalent qualification from foreign countries (foreign). When new
students join the polytechnic under this study, they are enrolled on two study paths:
study path A and study path B. Students who are enrolled on study path A take
Introduction to Engineering in semester 1 and Engineering Mathematics in semester 2,
whereas students who are enrolled on study path B take Engineering Mathematics in
semester 1 and Introduction to Engineering in semester 2.
In an engineering curriculum, a student typically takes six to seven modules in a
semester. Some of these modules like Engineering Mathematics focus on theoretical
training while others like Introduction to Engineering focus on skills training. It was
decided to use Engineering Mathematics and Introduction to Engineering as the data
source in this study for five reasons.
First, most of the first year students had little prior knowledge in engineering while they
had considerable prior knowledge in mathematics during secondary schools. This
difference was important when students’ situational interest was included as one of the
variables. As pointed out by Hidi and Renninger (2006), interest was primarily
situationally supported when domain knowledge was low but was less situationally
supported when domain knowledge was high. Students’ reactions to the Introduction
to Engineering module would likely to have been largely situational, whereas students’
reaction to Engineering Mathematics could be situational, individual or both. Thus, both
Engineering Mathematics and Introduction to Engineering modules were selected for
this sample so that a more balanced view would be collected from the students.
Second, these two modules were compulsory for all first year students enrolled in all the
engineering courses offered in the School of Engineering at the polytechnic under this
Level of commitment to engineering education in a polytechnic in Singapore
50
study. In fact, these two modules were included in most of the curriculum of
engineering courses offered in other polytechnics in Singapore. The results generated
from this study, therefore, potentially could be extended to most polytechnics in
Singapore considering the profile used in this study.
Third, as discussed in previous chapters, students who were enrolled in engineering
courses at the polytechnic under this study came from a diverse background and they
generally perceived Engineering Mathematics as a challenging module and Introduction
to Engineering as a relatively easy module. If only difficult or challenging modules were
selected for this study, they would tend to generate negative feelings among students
and thus led to bias in the data collected.
Fourth, the two modules were chosen for convenience as it was easier to trace and
monitor students’ participation as the study required the same group of students to
participate in two surveys in order to examine the factors that predict their level of
commitment to engineering over a period of one year.
Fifth, it was more manageable to handle data because the surveys were conducted using
paper and pen in an examination‐like environment instead of an online system. The
purpose was to ensure that the survey was conducted at the same period to ensure
consistency and a high response rate. The intention was to produce the best possible
quality of data using these procedures.
To address the research questions stated in section 3.2, a final study sample of 402 first
year students was used for the analyses. Out of the 402 first year students, 266 students
were enrolled on study path A and 136 students were enrolled on study path B, 254
students with ‘O’ level qualification prior to joining the polytechnic, 78 with ITE
qualification and 70 with equivalent qualification from foreign countries. More details
on data collection are reported in section 3.7.
3.4 Selection of Instruments
In chapter 2, a literature review on first‐year students’ level of commitment to
engineering education showed that it was related to students’ prior experiences and
self‐efficacy, their perceived learning environment experiences and situational interest
stimulated in the learning environment, as well as their academic performance. This
Level of commitment to engineering education in a polytechnic in Singapore
51
section describes the selection of instruments to measure students’ self‐efficacy, their
perceptions of the learning environment, their situational interest stimulated in the
learning environment and their level of commitment to engineering education. The
measures of students’ prior schooling and academic performances are described in
section 3.5.
As discussed in previous chapters, there is a wide range of instruments that have been
developed to measure self‐efficacy, perceived learning environment, situational interest
and level of commitment. To fulfil the aims of this study, the following instruments were
modified and adapted with permissions obtained from the authors of the respective
instruments.
3.4.1 The Self‐Efficacy for Broad Academic Milestone Survey
The Self‐Efficacy for Broad Academic Milestone Survey was developed and used by Lent
and his colleagues to measure the confidence in undergraduate engineering students’
ability to perform well academically in engineering (Lent et al., 1986; Lent, Singley, Sheu,
J. Schmidt, & L. Schmidt, 2007; Lent et al., 2008). The validity and reliability of Self‐
Efficacy for Broad Academic Milestone Survey were demonstrated in past studies (Jones
et al., 2010; Lee, Flores, Navarro, & Kanagui‐Muñoz, 2015). The survey consists of
twelve generic behaviours and asks participants to rate their confidence in performing
them. All items are rated on a scale ranging from 0 (no confidence at all) to 9 (complete
confidence).
Similar to the research work done by Jones and colleagues where they examined factors
that affected female and male first‐year engineering students’ motivation at Virginia
Tech, USA (Jones et al., 2010), the engineering self‐efficacy in this study was measured
using four items modified and extracted from the Self‐Efficacy for Broad Academic
Milestone Survey. The reason was that the original instrument contained items that
were outside the scope of the research questions in this study. Appendix A shows the
modification of items as compared to the original items used in the Self‐Efficacy for
Broad Academic Milestone Survey.
Level of commitment to engineering education in a polytechnic in Singapore
52
3.4.2 What Is Happening In this Class Survey
The What is Happening in this Class Survey was developed by Fraser and colleagues in
1996 and targeted students at the secondary level. Based on the literature review in
Chapter 2, the validity and reliability of the What is Happening in this Class Survey were
demonstrated in many studies and administered to students from lower secondary to
colleges and working adults in various countries including Singapore (Dorman, 2008;
Fraser et al., 2010; MacLeod & Fraser, 2010). There are eight items in each of the seven
scales: student cohesiveness, lecturer support, involvement, investigation, task
orientation, cooperation and equity. It has a five‐point response scale ranging from
‘almost never’ to ‘almost always’. There is a total of 56 items.
The What is Happening in this Class Survey was appropriate for this study. There was a
minor modification in which the word ‘teacher’ was replaced with the word ‘lecturer’.
The What is Happening in this Class Survey was chosen for two reasons. First, the survey
was able to assess students’ perceptions in areas relevant the polytechnic learning
environement. Second, previous research as highlighted in chapter 2 suggested that
students were likely to respond honestly as the items in the survey did not directly assess
their performance, personality or character. A copy of the modified ‘what is happening
in this class survey’ is provided in Appendix B.
3.4.3 Situational Interest Survey
Chapter 2 included a review of literature relevant to this relatively new instrument,
including the conception and development of the Situational Interest Survey, as well as
its use and validation in previous research. According to Linnenrbink‐Garcia and
colleagues (2010), types of learning environments and instructional practices could be
designed to promote situational interest, and hence the development of individual
interest. They developed the Situational Interest Survey for use in various domains and
appropriate for assessing adolescents across different levels of schooling, from middle
school to college. Unlike other instruments, this tool conceptualized situational interest
as general perception and reaction to the classroom context which reflected an
accumulation of experiences in the classroom.
Level of commitment to engineering education in a polytechnic in Singapore
53
The instrument consists of three scales: triggered situational interest, maintained
situational interest‐feeling and maintained situational interest‐value. Whereas
triggered situational interest refers to the positive affective reaction learners can have
to the way classroom materials are presented, maintained situational interest refers to
the reactions of learners to the material itself, either affectively (feeling) or at a deeper
level (value). These three scales are believed to be precursors to individual interest.
Linnenrbink‐Garcia and colleagues (2010) pointed out that maintained situational
interest‐feeling and maintained situational interest‐value could be combined if the
correlation between the two was substantial. There are twelve items with four items in
each of the three scales. Each item is responded on a scale ranging from 1 (not at all
true) to 5 (very true) (Linnenbrink‐Garcia et al., 2010). All of the items in the survey are
presented as positive statements, there are no negative statements.
In this study, the domains were Engineering Mathematics and Introduction to
Engineering. Consideration was given whether to change some of the statements to
have a negative meaning. However, in order to minimise misinterpretation by students
and to compare results of similar studies using the same survey, it was decided to keep
all items as positive statements. In addition, some words in the survey were modified
to suit the context of this study. Appendix C shows the modification of items as
compared to the original items used in ‘situational interest survey’.
3.4.4 College Persistence Questionnaire
The College Persistence Questionnaire was designed by Davidson and colleagues in 2009
to predict whether college freshman return for their sophomore years. There are six
scales in the questionnaire: academic integration; social integration; support services
satisfaction; degree commitment; institutional commitment; and academic
conscientiousness. A total of 53 close‐ended items are included in the questionnaire,
answered on a five‐point Likert scale. The response choices for the questions differ
depending on the item wording and all are converted to a favourability continuum that
ranges from ‐2 (least favourable answer) to +2 (most favourable answer) (Davidson et
al., 2009; Gore, 2010).
The College Persistence Questionnaire was proven to be valid and reliable in several
studies in the past. Of the six scales, the best predictors of student persistence from
Level of commitment to engineering education in a polytechnic in Singapore
54
past research studies were institutional commitment and degree commitment
(Davidson et al., 2009; Garrison, 2014; Gore, 2010). In this study, these two scales were
chosen and combined to measure students’ level of commitment to engineering
education. There was a total of nine items.
In addition, instead of using a scale ranging from ‐2 to +2, each item was responded on
a scale ranging from 1 (least favourable answer) to 5 (most favourable answer) in order
to be consistent with the other scales that were used in this study. The College
Persistence Questionnaire includes a neutral option as the mid‐point in a five‐point scale.
From the methodological viewpoint, research showed that this midpoint might affect
the reliability and validity of an instrument (Tsang, 2012). In addition, from the
epistemological viewpoint, it might lead to respondents’ misinterpretation of the
midpoint option and their socially desirable responses through using midpoints (Tsang,
2012). In order to solve these problems, adverbs such as ‘slightly’, ‘fairly’, etc. were used
to label the response options of the nine items to reduce the number of midpoint
selections. This approach might be able to solve the problem of ‘untrue’ middle
response category endorsement according to past research studies (Kulas & Stachowski,
2009; Tsang, 2012).
Appendix D shows the modification of items and the response options as compared to
the original items and the response options used in College Persistence Questionnaire.
3.5 Other Measures
3.5.1 Prior Schooling
This variable refers to the prior learning experiences that students possessed prior to
joining the polytechnic. As discussed in earlier chapters, there are generally three
streams of students who are admitted to polytechnic education if they meet the criteria
of admission. The first stream of students are secondary school students with General
Certification of Education Ordinary level (‘O’ Level) certificate (generally 4‐5 years of
education in the secondary school). The second stream of students are students from
the Institute of Technical Education (ITE) with ITE certificate (generally 1‐2 years of
education in ITE after secondary school education). The third stream of students are
foreign students (Foreign) who hold certification or other qualification that is equivalent
Level of commitment to engineering education in a polytechnic in Singapore
55
to GCE ‘O’ level certificate and is recognised by Singapore Ministry of Education. The
item ‘Prior to joining the polytechnic, where did you study?’ seeks response from
students as either they are from a secondary school in Singapore, from ITE in Singapore,
or from a school/an institution in foreign country.
3.5.2 Academic Performance
The academic performance of students who participated in this study was obtained from
the student information management system of the polytechnic for two academic
modules, Engineering Mathematics and Introduction Engineering. The final scores
obtained in these two modules (based on 100 marks) measured students’ academic
performance.
3.6 Assembling the Instruments
It was decided to assemble various scales into one questionnaire to facilitate the
administration of the surveys to students during the face‐to‐face data collection
sessions. There was 81 items in the final questionnaire with four scales. The first scale
consists of twelve items for situational interest with four item for each of the three
factors (triggered situational Interest, maintained situational interest‐feeling, and
maintained situational interest‐value). The second scale consists of 56 items for learning
environment with eight item for each of the seven factors (student cohesiveness,
lecturer support, involvement, investigation, task orientation, cooperation and equity).
The third scale consists of four items for self‐efficacy and the fourth scale consists of
nine items for the level of commitment. The questionnaire was organised into four parts
with the items of the situational interest scale appearing first (items 1 – 12), followed by
the items of the learning environment scale (items 13 – 68), the self‐efficacy scale (items
69 – 72) and lastly, the items of the level of commitment scale (items 73 – 81).
Before the 81 items were presented to students, students were given instructions that
the first part contained statements about situational interest in the classroom and the
second part contained statements about practices, which could take place in the
classroom. The students were also told that the third part contained statements about
their confidence in completing the academic requirement in engineering diplomas, and
the last part contained statements about their intention to obtain an engineering
Level of commitment to engineering education in a polytechnic in Singapore
56
qualification. Instructions were also written to remind students that part 1 and part 2
required them to think about how well each statement described what the class was like
for them, while part 3 and part 4 required them to think about how well each statement
described how they felt about engineering.
The next section required students fill in their demographic information such as
identification number, gender, birth year and prior schooling, etc. The identification
number was required in this study for traceability purpose as the data was collected
from two academic modules, Engineering Mathematics and Introduction to Engineering,
that were offered to first year engineering students in two different semesters. The
identification number was assigned to students who participated in the survey and it
was kept against the student’s name in the registration list in case students forgot it in
the interval between the two surveys.
3.7 Data Collection
A pilot study was conducted with 10 students with the same background as the sample
identified for the study prior to the actual administration of the surveys. The intention
was to ensure the readability and comprehensibility of the questionnaire, as well as to
determine a suitable duration for the actual administration of the questionnaire and the
adequacy of the flow of the items in the questionnaire. These students were from the
School of Engineering but did not participate in the actual administration of the survey.
In this pilot study, students were asked to highlight the items that they were not sure of,
in addition to completing the questionnaire. At the end of the pilot, students were asked
to provide recommendations on how to revise items that were not clear to them. Table
8 lists the changes made to the items after the pilot study:
Table 8 Comparison of the wording of the items before and after the pilot study
Item Before Pilot Study After Pilot Study
21 The lecturer takes a personal
interest in me.
The lecturer takes a personal
interest in my learning.
23 The lecturer considers my feelings. The lecturer is considerate and takes
account of my feelings.
Level of commitment to engineering education in a polytechnic in Singapore
57
Item Before Pilot Study After Pilot Study
72 Complete the upper level required
modules in your engineering
programme with an overall grade
point average (i.e. GPA) of B or
better.
Complete the upper level required
modules in your engineering
programme with an overall grade
point average (i.e. GPA) of 3.0 or
better.
In general, the students took about ten to twenty minutes to complete the
questionnaire. They did not experience fatigue while answering all the 81 items and
they found the format of the questionnaire to be clear and the flow of the items to be
good. Two separate questionnaires were then compiled, one for Engineering
Mathematics and one for Introduction to Engineering. As an example, the final
questionnaire for Engineering Mathematics is shown in Appendix E.
The students who volunteered in this study needed to complete two surveys during their
first year of studies at the polytechnic. The first survey (time 1) was conducted at the
end of semester 1 (July 2014). The second survey (time 2) was conducted towards the
end of semester 2 (January 2015), about six months after the first survey was conducted.
Students who registered for Engineering Mathematics at time 1 received the
questionnaire that required them to think about situational interest and practices which
could take place in the Engineering Mathematics classroom. On the other hand,
students who registered for Introduction to Engineering at time 1 received the
questionnaire that required them to think about situational interest and practices which
could take place in the Introduction to Engineering classroom. The distribution of
questionnaires to students was done in similar manner at time 2.
One week before the commencement of the survey at time 1, consent forms were
distributed to students and for those who were under 18 years of age, consent forms
for parents were also distributed at the same time. A student was only considered to
be a participant after consent forms from the student himself/herself and his/her
parents were all obtained. Lecturers’ consent were also sought to allow the surveys to
be conducted using a portion of the lesson time from lecture, tutorial or practical lessons
of a compulsory module in each semester. A staff member, who was not involved in
Level of commitment to engineering education in a polytechnic in Singapore
58
teaching the students, was tasked to administer the survey, and was also briefed and
trained in the administration of the survey.
During the administration of the survey at time 1, the staff member briefed the
participants on the purpose of the study prior to the commencement of the survey in
the class, and an information sheet was then distributed to the students. Participants
were told that there were two rounds of surveys. They were also informed that the
participation in the study was voluntary, and that their personal information would be
kept strictly confidential. However, an identification number was used so that data can
be linked across the two different points of data collection. It was stressed that no
names would be used at any time. In addition, they were reminded to respond to the
items carefully and took the survey seriously. A similar procedure was applied at time 2
when the second survey was administered.
Students completed the two surveys under as close to examination conditions as
possible, and they took no longer than 30 minutes to complete the survey. All in all, 594
students participated in the survey at time 1, 586 students participated in the survey at
time 2 and 494 students participated in both surveys. However, there were some faulty
responses which include multiple responses or unanswered items. As the data are
missing only on the dependent variable and the data are missing at random, listwise
deletion was used to handle these faulty responses. As the sample was large enough,
all data from any participant with missing values were deleted without substantial loss
of statistical power. After discarding the missing data, the remaining sample remained
large with 402 students, about 35% of all first year students in the 2014 batch at the end
of their first year of studies. Table 9 shows the overall response rate of the two surveys
that were administered at time 1 and time 2.
Table 9 Overall response rate
First Year Engineering Students Count Response Rate Number of students who completed the questionnaire at time 1
594 44.4%
Total number of students at time 1 1338 Number of students who completed the questionnaire at time 2
586 50.9%
Level of commitment to engineering education in a polytechnic in Singapore
59
First Year Engineering Students Count Response Rate Total number of students at time 2 1152 Number of students who completed the questionnaire at both time 1 and time 2
402 34.9%
Total number of students at time 2 1152
Table 10 shows that the percentage of the number of students in different age groups
in my sample is similar to those in the population. The percentage of students with
different prior schooling in my sample is also similar to those in the population for the
O level and ITE students. However, the percentage of students with qualification from
foreign countries is higher in my sample (17.4%) as compared to those in the population
(9.7%). The percentage of female students in my sample (33.8%) is also slightly higher
than the percentage of female students in the total population in this study (28%).
Table 10
Demographics of respondents
Variables Count (sample)
Percentage (sample)
Count (population)
Percentage (population)
Age More than 22 years old (before 1993)
27 6.7% 86 7.5%
20 to 22 years old (1993‐1995) 109 27.1% 364 31.6% 16 to 19 years old (1996‐1998) 266 66.2% 702 60.9% Total 402 100% 1152 100%
Gender Male 266 66.2% 829 72% Female 136 33.8% 323 28% Total 402 100% 1152 100%
Prior Schooling ‘O’ level students
254 63.2% 776 67.4%
ITE students
78 19.4% 264 22.9%
Foreign students 70 17.4% 112 9.7% Total 402 100% 1152 100%
Level of commitment to engineering education in a polytechnic in Singapore
60
The data were entered into Microsoft Excel spreadsheets. The spreadsheets were then
imported into SPSS version 20.0 for statistical analysis.
3.8 Data Analysis
The data collected from the survey were used to answer the research questions shown
in section 3.2 of this chapter. This section provides a brief description on the statistical
analysis procedures that were used to answer the research questions.
The first research question involved psychometric investigations of the modified
situational interest, learning environment, self‐efficacy, and level of commitment scales
used in the context of a polytechnic in Singapore. Factor analysis was used to check the
questionnaire structure and Cronbach’s alpha coefficeint was used to measure the
internal consistency. Detailed procedures to determine the psychometric
characteristics of the four scales are discussed later in chapter 4, section 4.2 to 4.4.
The second set of research questions analysed the relationships between the learning
environment, students’ situational interest, self‐efficacy, academic performance and
level of commitment at two time periods (time 1 and time 2). Pearson product‐moment
correlations were computed to address these two questions. More details can be found
in chapter 4, section 4.5.
Multiple linear regression analysis was used to answer the third set of research
questions. These research questions investigated the factors that contribute to level of
commitment to engineering education at two time periods (time 1 and time 2). The
detailed analyses are presented in chapter 4, section 4.6.
Finally, a paired sample t‐test was used to address the fourth research question. The
fourth research question examined the change in students’ level of commitment to
engineering education over the first year of studies. The detailed discussions are
reported in chapter 4, Section 4.7.
Level of commitment to engineering education in a polytechnic in Singapore
61
3.9 Chapter Summary
This chapter described and discussed methods used in this study. It began by discussing
the sample and the instruments used in this study. The sample consisted of 402 students
from the School of Engineering in a polytechnic in Singapore. The modified 81‐item
questionnaire contained four scales: the learning environment scale with seven factors,
the situational interest scale with three factors, the self‐efficacy scale and the level of
commitment scale. This questionnaire was administered to obtain data from these 402
students. This chapter also briefly described the analysis methods used to answer the
four specific research questions in this study. The next chapter provides a detailed
report of the procedures and findings. Results are presented in tables and detailed
explanations are provided to show how the findings address the four research questions.
Level of commitment to engineering education in a polytechnic in Singapore
62
Chapter 4: Results
4.1 Introduction
This chapter reports results for the research questions stated in chapter 3. First, findings
are reported for the psychometric testing of the modified questionnaire which was
based on the twelve items of the situational interest scale (three factors), the 56 items
of the learning environment scale (seven factors), the four items of the self‐efficacy scale
and the nine items of the level of commitment scale. Second, findings are reported for
the relationships between students’ perception of the learning environment, their
situational interest, self‐efficacy and level of commitment at time 1 and time 2. Third,
findings are reported for the joint relationship between the learning environment scales,
the situational interest scales, self‐efficacy, academic performances, and prior schooling
as independent variables, and level of commitment as the dependent variable at time 1
and time 2. Fourth, findings are reported for the changes in students’ level of
commitment between time 1 and time 2.
As described in chapter 3, new students who joined the polytechnic under this study
were enrolled in two different study paths (study path A and study path B) and they took
six or seven modules per semester over their first year of studies. Two of these modules
were selected for this study: Introduction to Engineering and Engineering Mathematics.
Students who were enrolled in study path A took Introduction to Engineering in
semester 1 and Engineering Mathematics in semester 2; while students who were
enrolled in study path B took Engineering Mathematics in semester 1 and Introduction
to Engineering in semester 2. In addition, as described in chapter 1, first year students
had different prior learning experiences and background when they joined the
polytechnic under this study. Generally, students’ prior schooling could be categorised
as students with ‘O’ Level qualification (‘O’ Level), students with ITE qualification (ITE),
and students with equivalent qualification from foreign countries (Foreign).
This study was carried out with a sample of 402 first year students who completed the
surveys at time 1 (July 2014) and time 2 (January 2015). Of these 402 students, 266
students who were enrolled on study path A completed the survey for Introduction to
Engineering at time 1 and the survey for Engineering Mathematics at time 2; 136
Level of commitment to engineering education in a polytechnic in Singapore
63
students who were enrolled on study path B completed the survey for Engineering
Mathematics at time 1 and the survey for Introduction to Engineering at time 2. There
were 254 ‘O’ Level students, 78 ITE students and 70 Foreign students. In view of this
complex set of data, the strategies for analysing the data in order to answer the four
research questions were as follows:
First, the sample of 402 students was partitioned into four subsamples of students
based on time 1 and time 2, study path A and study path B, Introduction to
Engineering and Engineering Mathematics, and prior schooling (‘O’ Level, ITE and
Foreign students). Next, the data were analysed using the subsamples to address
the four research questions and the results were compared (see Table 11).
Table 11
Subsamples for Data Analysis
1. Time 1 (402 students) and Time 2 (402 students)
2. Path A (366 students) and Path B (136 students)
3. Introduction to Engineering (402 students) and Engineering Mathematics (402 students)
4. ‘O’ Level (254 students), ITE (78 students) and Foreign (70 students)
For research question 1, psychometric characteristics of the instruments were
examined first by using the combined subsamples of students who were enrolled on
study path A and study path B at time 1 and separately at time 2. The same
examination was then carried out by using the combined subsamples of students
who took Introduction to Engineering at time 1 and time 2, and separately for
students who took Engineering Mathematics at time 1 and time 2.
For research question 2, the relationships between students’ perception of the
learning environment, self‐efficacy, situational interest, academic performance, and
level of commitment were examined first by using the combined subsamples of
students who were enrolled on study path A and study path B at time 1 and
separately at time 2. Second, these relationships were examined by using the
combined subsamples of students who took Introduction to Engineering at time 1
and time 2, and separately for students who took Engineering Mathematics at time
Level of commitment to engineering education in a polytechnic in Singapore
64
1 and time 2. Third, these relationships were examined by using the subsamples of
students who were enrolled on study path A, and separately for students who were
enrolled on study path B. Lastly, these relationships were examined using the
subsamples of students with different prior schooling (‘O’ Level students, ITE
students, and Foreign students).
For research question 3, the joint relationship between students’ perception of the
learning environment, self‐efficacy, situational interest, academic performance,
prior schooling as independent variables and level of commitment as dependent
variable was examined first by using the combined subsamples of students who were
enrolled on study path A and study path B at time 1 and separately at time 2. Second,
the joint relationship was examined by using the subsamples of students who were
enrolled on study path A, and separately for students who were enrolled on study
path B.
For research question 4, the change in students’ level of commitment to engineering
education was examined first by using the combined subsamples of students who
were enrolled on study path A and study path B. Second, the change was examined
by using the subsamples of students with different prior schooling (O level students,
ITE students, and foreign students).
When the above partitions were performed and data analyses were done, examination
of these findings showed extremely similar results. This was the case for all partitions
of students based on time 1 and time 2, study path A and study path B, Introduction to
Engineering and Engineering Mathematics, and prior schooling (‘O’ Level, ITE and
Foreign students). These findings indicated that it was appropriate to consider the
whole set of data as one pool of data rather than concentrating on the subsamples of
data. Thus, the reporting of findings in this chapter is based on the pooled sample of
402 students at time 1 and separately at time 2. Some of the findings based on the
various partitions are included in Appendix F.
As stated in chapter 3, the specific research questions are as follows:
Research Question 1
Are the following instruments valid and reliable when used with first year engineering
students in a polytechnic in Singapore?
Level of commitment to engineering education in a polytechnic in Singapore
65
a. Situational interest scale
b. Learning environment scale
c. Self‐efficacy scale
d. Level of commitment scale
Research Question 2a
What are the relationships between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, and level of commitment at
time 1?
Research Question 2b
What are the relationships between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, and level of commitment at
time 2?
Research Question 3a
What is the joint relationship between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, prior schooling as
independent variables and level of commitment as dependent variable at time 1?
Research Question 3b
What is the joint relationship between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, prior schooling as
independent variables and level of commitment as dependent variable at time 2?
Research Question 4
What change is there in students’ level of commitment to engineering education
between time 1 and time 2?
To address research question 1, factor analysis was used to check the questionnaire
structure and Cronbach’s alpha coefficient was used as a measure of internal
consistency. To address research questions 2a and 2b, Pearson product‐moment
correlations were computed for time 1 and separately for time 2. To address research
questions 3a and 3b, multiple linear regression analysis was used, with perceived
learning environment, self‐efficacy, situational interest, academic performance and
prior schooling as independent variables, and level of commitment as dependent
Level of commitment to engineering education in a polytechnic in Singapore
66
variable at time 1 and separately at time 2. To address research question 4, a paired
sample t‐test was used.
4.2 Psychometric Investigation of the Situational Interest Scale
What are the psychometric characteristics of the situational interest scale when used
with first year engineering students in a polytechnic in Singapore?
The situational interest scale used in this study was adapted from the Situational Interest
Survey developed by Linnenbrink‐Garcia and colleagues (2010). The situational interest
scale aims to investigate students’ general perception and reaction to the classroom
context which reflects an accumulation of experiences in the classroom. As described
in chapter 3, the instrument consists of three factors: triggered situational interest,
maintained situational interest‐feeling and maintained situational interest‐value.
Whereas triggered situational interest refers to the positive affective reaction learners
can have to the way classroom materials is presented, maintained situational interest
refers to the reactions of learners have to the materials affectively (feeling) or at a
deeper level (value) and is believed to be precursor to individual interest. The
maintained situational interest‐feeling and maintained situational interest‐value can be
combined if the correlation among these two is substantial (Linnenrbink‐Garcia, et. al.,
2010). Some of the words in the instrument were modified to suit the context of this
study (see section 3.4.3 for details).
Linnenbrink‐Garcia and colleagues (2010) empirically evaluated the psychometric
properties of the factors of the situational interest scale and reported three factors with
strong factorial validity. To confirm the dimensional structure of the situational interest
scale at time 1 and time 2, principal axis factoring followed by varimax rotation and
Kaiser normalization was conducted to verify the number of factors and to identify
which items made up each factor.
The Kaiser‐Meyer‐Olkin measures (KMO) were well above 0.50 (KMO = 0.94 at time 1;
KMO = 0.92 at time 2) indicating the data were suitable for factor analysis. The Bartlett’s
test of sphericity showed that there were patterned relationships between the items
( 66 4722.90, 0.01 at time 1; 66 3665.12, 0.01 at time 2). The
criteria for the retention of any item were that it had to have factor loadings of at least
Level of commitment to engineering education in a polytechnic in Singapore
67
0.40 on its own scale and less than 0.40 on all of the other scales. Table 12 shows the
factor loading obtained for each situational interest item at time 1. At the bottom of
the tables, the eigenvalue and percentage of variance accounted for are shown.
At time 1, the percentage of variance accounted for was 25.8% for triggered situational
interest, 28.8% for maintained situational interest‐feeling, and 28.3% for maintained
situational interest‐value. The cumulative percentage of variance accounted for by
these three factors was 82.9%. The eigenvalues ranged from 3.14 (triggered situational
interest) to 3.46 (maintained situational interest‐feeling). Using an eigenvalue cut‐off of
1.0, all of the original three factors of the situational interest scale were retained at time
1.
Table 12 Factor Loadings for Situational Interest Scales – Time 1
Item
Factor Loadings
Triggered Situational
Interest
Maintained
Situational Interest ‐
Feeling
Maintained
Situational Interest ‐
Value
1 0.86
2 0.85
3 0.71
4 0.65
5 0.78
6 0.78
7 0.78
8 0.74
9 0.76
10 0.85
11 0.82
12 0.81
Eigenvalue 3.14 3.46 3.39
% of
Variance 25.8 28.8 28.3
N = 402 students; factor loadings less than 0.40 had been omitted from the table
Similar results were found at time 2 (see Table 13). The percentage of variance
accounted for was 25.6% for triggered situational interest, 25.5% for maintained
situational interest‐feeling, and 25.7% for the maintained situational interest‐value. The
Level of commitment to engineering education in a polytechnic in Singapore
68
cumulative percentage of variance accounted for by these three factors was 76.7%. The
eigenvalues ranged from 3.06 (maintained situational interest‐feeling) to 3.48
(maintained situational interest‐value). Again, these factor analysis results constituted
evidence towards the factorial validity of the three factors of the situational interest
scale when used with first year engineering students in a polytechnic in Singapore.
Table 13 Factor Loadings for Situational Interest Scale – Time 2
Item
Factor Loadings
Triggered Situational
Interest
Maintained
Situational Interest ‐
Feeling
Maintained
Situational Interest ‐
Value
1 0.77
2 0.79
3 0.75
4 0.74
5 0.72
6 0.78
7 0.78
8 0.76
9 0.73
10 0.79
11 0.84
12 0.78
Eigenvalue 3.07 3.06 3.08
% of
Variance 25.6 25.5 25.7
N = 402 students; factor loadings less than 0.40 had been omitted from the table
Cronbach’s alpha was computed to check for internal consistency of the situational
interest scale. The following criteria were used to judge the values (George and Mallery,
2011): greater than 0.9 was excellent, between 0.8 and 0.9 was good, between 0.7 and
0.8 was acceptable, between 0.6 and 0.7 was questionable, between 0.5 and 0.6 was
poor, and below 0.5 was unacceptable.
For this study, the Cronbach’s alphas were excellent for triggered situational interest (α
= 0.91 at time 1; α = 0.92 at time 2), excellent for maintained situational interest‐feeling
(α = 0.95 at time 1; α = 0.92 at time 2), and good for maintained situational interest‐
Level of commitment to engineering education in a polytechnic in Singapore
69
value (α = 0.92 at time 1; α = 0.89 at time 2). These figures were similar to those found
in the research undertaken by Linnenbrink‐Garcia and colleagues (2010) which ranged
from 0.81 to 0.89 for a sample of 278 adolescents in Grades 7 through 12 from a large,
urban area in the western United States.
As mentioned earlier, the maintained situational interest‐feeling and maintained
situational interest‐value could be combined if the correlation between the two is
substantial (Linnenrbink‐Garcia, et. al., 2010). For this study, maintained situational
interest‐feeling and maintained situational interest‐value were highly correlated (r =
0.74, p < 0.001 at time 1; r = 0.68, p < 0.001 at time 2). Thus, maintained situational
interest‐feeling and maintained situational interest‐value were combined for
subsequent analyses of this study.
4.3 Psychometric Investigation of the Learning Environment Scale
What are the psychometric characteristics of the learning environment scale when used
for first year engineering students in a polytechnic in Singapore?
The learning environment scale used in this study was adapted from the What is
Happening in This Class Survey developed by Fraser and colleagues (1996). The sample
of 402 students responded to the seven factors with eight items per factor from the
learning environment scale: student cohesiveness, lecturer support, involvement,
investigation, task orientation, cooperation and equity. Minor modifications were made
to some of the words used in the scale and these were discussed in chapter 3 (see
section 3.4.2 for details). To confirm the dimensional structure of the learning
environment scale at time 1 and time 2, principal axis factoring followed by varimax
rotation and Kaiser normalization was conducted to verify the number of factors and to
identify which items made up each factor.
Using the same criteria as described in section 4.2, one of the items under involvement
– ‘The lecturer asks me questions’ (item 31) was excluded at time 1 as it did not meet
the criteria set (KMO = 0.96, χ2(1485) = 19746.68, p < 0.01). However, all items of the
seven factors of the learning environment scale were retained at time 2 (KMO = 0.95,
(1540) = 16410.50, p < 0.01). Appendix G shows the factor loading obtained for each
item of the seven learning environment factors at time 1 and time 2 respectively, with
Level of commitment to engineering education in a polytechnic in Singapore
70
the eigenvalue and percentage of variance accounted for presented at the bottom of
the tables.
At time 1 (with item 31 excluded in the analysis), the percentage of variance accounted
for was 9.84% for student cohesiveness, 11.7% for lecturer support, 6.74% for
involvement, 11.4% for investigation, 10.1% for task orientation, 10.2% for cooperation
and 10.6% for equity. The cumulative percentage of variance accounted for by these
seven factors was 70.5%. The eigenvalues ranged from 3.71 (involvement) to 6.44
(lecturer support). At time 2, the percentage of variance accounted for was 7.4% for
student cohesiveness, 10.3% for lecturer support, 7.61% for involvement, 10.2% for
investigation, 8.70% for task orientation, 9.50% for cooperation and 10.6% for equity.
The cumulative percentage of variance accounted for by these seven factors was 64.2%.
The eigenvalues ranged from 4.12 (student cohesiveness) to 5.92 (equity). Again, these
factor analysis results constituted evidence towards the factorial validity of the seven
factors of the learning environment scale when used with first year engineering students
in a polytechnic in Singapore.
Cronbach’s alpha was computed for each of the seven factors of the learning
environment scale. Using the same criteria as described in section 4.2, the Cronbach’s
alphas were good for student cohesiveness (α = 0.91 at time 1; α = 0.85 at time 2),
excellent for lecturer support (α = 0.95 at time 1; α = 0.94 at time 2), and excellent for
involvement (α = 0.92 at time 1; α = 0.90 at time 2). The Cronbach’s alphas were
excellent for investigation (α = 0.94 at time 1; α = 0.93 at time 2), good for task
orientation (α = 0.92 at time 1; α = 0.89 at time 2), excellent for cooperation (α = 0.94 at
time 1; α = 0.92 at time 2), and excellent for equity (α = 0.96 at time 1; α = 0.94 at time
2). These figures were similar to those found in the research undertaken by Fraser et al.
(2010) which ranged from 0.78 to 0.89 for a sample of 1161 students in grades 9 and 10
in Australia.
4.4 Psychometric Investigation of Self‐efficacy Scale and Level of Commitment Scale
What are the psychometric characteristics of the self‐efficacy scale and the level of
commitment scale when used for first year engineering students in a polytechnic in
Singapore?
Level of commitment to engineering education in a polytechnic in Singapore
71
The self‐efficacy scale used in this study was four items adapted from the Self‐Efficacy
for Broad Academic Milestones Survey to measure engineering self‐efficacy. The level
of commitment scale used in this study was nine items adapted from the College
Persistence Questionnaire to measure the level of commitment to engineering
education. As described in chapter 3, the validity and reliability of the Self‐Efficacy for
Broad Academic Milestone Survey and the College Persistence Questionnaire were
demonstrated in past studies. In this study, four out of the twelve items in the Self‐
Efficacy for Broad Academic Milestone Survey and nine out of the 81 items (or two out
of the six scales) in the College Persistence Questionnaire were modified and adapted
to suit the context of engineering diploma in a polytechnic in Singapore. Details of the
changes to the items were described in chapter 3 (see section 3.4.1 and section 3.4.4 for
details).
To confirm the dimensional structure of the adapted self‐efficacy and level of
commitment scales, principal axis factoring followed by varimax rotation and Kaiser
normalization was conducted to verify the factors and to identify which items made up
each factor. Using the criteria set in section 4.2, all the four items of the self‐efficacy
scale (KMO = 0.85, (6) = 1923.46, p < 0.01 at time 1; KMO = 0.83, (6) = 1663.611, p
< 0.01 at time 2). All the nine items of the level of commitment scale were retained as
well (KMO = 0.88, (6) = 1508.78, p < 0.01 at time 1; KMO = 0.87, (6) = 1225.86, p <
0.01 at time 2). Table 14 shows the factor loading obtained for self‐efficacy at time 1
and time 2. Table 15 shows the factor loading obtained for level of commitment at time
1 and time 2. At the bottom of each of the two tables, the eigenvalue and percentage
of variance accounted for are shown.
Using eigenvalue cut‐off of 1.0, there was one factor for the self‐efficacy scale that
explained a total variance of 89.0% at time 1 and 86.1% at time 2, and one factor for the
level of commitment scale that explained a total variance of 48.6% at time 1 and 43.8%
at time 2. The eigenvalues for the self‐efficacy scale were 3.56 at time 1 and 3.44 at
time 2 and the eigenvalues for the level of commitment scale were 4.37 at time 1 and
3.95 at time 2. Overall, these factor analysis results constituted evidence towards the
factorial validity of the self‐efficacy scale and the level of commitment scale when used
with first year engineering students in a polytechnic in Singapore.
Level of commitment to engineering education in a polytechnic in Singapore
72
Cronbach’s alpha was computed and using the same criteria as described in section 4.2,
the Cronbach’s alphas were excellent for self‐efficacy (α = 0.96 at time 1; α = 0.95 at
time 2) and good for level of commitment (α = 0.84 at time 1; α = 0.79 at time 2). These
figures were similar to those found in the past research ranging from 0.89 to 0.94 for
self‐efficacy (Lent et. al., 2008; Jones et. al. 2010) and ranging from 0.7 to 0.78 for level
of commitment (Davidson et al. 2009).
Table 14 Factor Loadings for Self‐Efficacy
Item Factor Loadings
Time 1 Time 2
69 0.93 0.90
70 0.97 0.95
71 0.95 0.95
72 0.93 0.92
Eigenvalue 3.56 3.44
% of
Variance 89.0 86.1
N = 402 students; factor loadings less than 0.40 had been omitted from the table
Table 15 Factor Loadings for Level of Commitment
Item Factor Loadings
Time 1 Time 2
73 0.81 0.80
74 0.69 0.68
75 0.75 0.76
76 0.43 0.40
77 0.44 0.41
78 0.82 0.83
79 0.84 0.81
80 0.79 0.71
81 0.57 0.53
Eigenvalue 4.37 3.95
% of
Variance 48.6 43.8
N = 402 students; factor loadings less than 0.40 had been omitted from the table
Level of commitment to engineering education in a polytechnic in Singapore
73
4.5 Relationships between Perception of the Learning Environment, students’ Self‐Efficacy, Situational Interest, Academic Performance and Level of Commitment
What are the relationships between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, and level of commitment at
time 1 and at time 2?
Correlation coefficients were computed for all these scales at time 1 and separately at
time 2. Table 16 shows the relationships between the seven factors of the learning
environment scale, self‐efficacy, the two factors of the situational interest scale,
academic performance and level of commitment. Most of the correlation coefficients
are statistically significant (p < 0.01) at time 1. However, academic performance did not
show substantial correlations with any of the seven factors of the learning environment
scale, self‐efficacy, any of the two factors of the situational interest scale, and level of
commitment at time 1. Most of them did not reach statistical significance and for those
that did reach statistical significance, they showed only weak correlations.
The correlation coefficients between the seven factors of the learning environment
scale ranged from 0.41 to 0.72. Strong relationships (r 0.60) were found between
student cohesiveness and cooperation (r = 0.61, p < 0.01), between lecturer support and
involvement (r = 0.71, p < 0.01), between lecturer support and equity (r = 0.72, p < 0.01),
between involvement and investigation (r = 0.67, p < 0.01), and between task
orientation and equity (r = 0.61, p < 0.01). The correlation coefficients between each of
the seven factors of the learning environment scale and self‐efficacy ranged from 0.19
to 0.45, triggered situational interest ranged from 0.32 to 0.65, maintained situational
interest ranged from 0.37 to 0.61, and level of commitment ranged from 0.14 to 0.38.
Strong correlations (r 0.60) were also found between lecturer support and triggered
situational interest (r = 0.65, p < 0.01), between lecturer support and maintained
situational interest (r = 0.61, p < 0.01), and between triggered situational interest and
maintained situational interest (r = 0.74, p < 0.01).
Similar results were found at time 2 when correlation coefficients were computed
between the seven factors of the learning environment scale, self‐efficacy, the two
factors of the situational interest scale, academic performance and level of commitment.
Level of commitment to engineering education in a polytechnic in Singapore
74
Most of the correlation coefficients are statistically significant (p < 0.01) at time 2. Again,
academic performance did not show substantial correlations with any of the seven
factors of the learning environment scale, self‐efficacy, any of the two factors of the
situational interest scale, and level of commitment at time 2. Most of them did not
reach statistical significance and for those that did reach statistical significance, they
showed only weak correlations.
In Table 17, the correlation coefficients between the seven factors of the learning
environment scale ranged from 0.37 to 0.69. Strong relationships were found between
lecturer support and involvement (r = 0.60, p < 0.01), between lecturer support and
equity (r = 0.64, p < 0.01), and between involvement and investigation (r = 0.69, p < 0.01).
The correlation coefficients between each of the seven factors of the learning
environment scale and self‐efficacy ranged from 0.18 to 0.33, triggered situational
interest ranged from 0.27 to 0.61, maintained situational interest ranged from 0.30 to
0.60, and level of commitment ranged from 0.28 to 0.39. Again, strong correlations (r
0.60) were found between lecturer support and triggered situational interest (r = 0.61,
p < 0.01), between lecturer support and maintained situational interest (r = 0.60, p <
0.01), and between triggered situational interest and maintained situational interest (r
= 0.72, p < 0.01).
Comparing the correlation coefficients between the seven factors of the learning
environment scale, self‐efficacy, the two factors of the situational interest scale,
academic performance and level of commitment at time 1 with those at time 2, there
were no substantial differences.
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Table 16 Correlation between Learning Environment, Self‐efficacy, Situational Interest, Academic Performance and Level of Commitment at Time 1
Scale Learning Environment Self‐
efficacy
Situational Interest Acade‐
mic
Perfor‐
mance
Level of
Commit‐
ment
Student
Cohesiven
ess
Lecturer
Support
Invo
lvem
ent
Investigation
Task
Orien
tation
Cooperation
Equity
Triggered
Situational
Interest
Maintained
Situational
Interest
Learning Environment
Student Cohesiveness ‐‐
Lecturer Support 0.43* ‐‐
Involvement 0.56* 0.71* ‐‐
Investigation 0.46* 0.57* 0.67* ‐‐
Task Orientation 0.41* 0.52* 0.53* 0.47* ‐‐
Cooperation 0.61* 0.46* 0.52* 0.45* 0.55* ‐‐
Equity 0.42* 0.72* 0.58* 0.44* 0.61* 0.57* ‐‐
Self‐efficacy 0.27* 0.22* 0.29* 0.28* 0.45* 0.19* 0.27* ‐‐
Situational Interest
Triggered Situational Interest 0.32* 0.65* 0.48* 0.39* 0.40* 0.33* 0.54* 0.21* ‐‐
Maintained Situational
Interest
0.39* 0.61* 0.54* 0.48* 0.54* 0.37* 0.53* 0.37* 0.74* ‐‐
Academic Performance ‐0.10 ‐0.11 ‐0.11 ‐0.10 0.01 ‐0.12 ‐0.03 0.27* ‐0.09 ‐0.12 ‐‐
Level of Commitment 0.23* 0.14* 0.16* 0.19* 0.38* 0.22* 0.27* 0.57* 0.07 0.23* 0.23* ‐‐
N = 402 students, *p < 0.01
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Table 17 Correlation between Learning Environment, Self‐efficacy, Situational Interest, Academic Performance and Level of Commitment at Time 2
Scale Learning Environment Self‐
efficacy
Situational Interest Acade‐
mic
Perfor‐
mance
Level of
Commit‐
ment
Student
Cohesiven
ess
Lecturer
Support
Invo
lvem
ent
Investigation
Task
Orien
tation
Cooperation
Equity
Triggered
Situational
Interest
Maintained
Situational
Interest
Learning Environment
Student Cohesiveness ‐‐
Lecturer Support 0.42* ‐‐
Involvement 0.54* 0.60* ‐‐
Investigation 0.43* 0.53* 0.69* ‐‐
Task Orientation 0.39* 0.46* 0.51* 0.51* ‐‐
Cooperation 0.58* 0.39* 0.50* 0.39* 0.51* ‐‐
Equity 0.37* 0.64* 0.53* 0.44* 0.45* 0.41* ‐‐
Self‐efficacy 0.21* 0.26* 0.31* 0.33* 0.31* 0.18* 0.28* ‐‐
Situational Interest
Triggered Situational Interest 0.27* 0.61* 0.43* 0.43* 0.42* 0.28* 0.47* 0.24* ‐‐
Maintained Situational
Interest
0.30* 0.60* 0.53* 0.53* 0.48* 0.33* 0.47* 0.34* 0.72* ‐‐
Academic Performance 0.08 0.08 0.08 0.03 0.07 0.00 0.12 0.30* 0.04 0.10 ‐‐
Level of Commitment 0.28* 0.30* 0.36* 0.30* 0.39* 0.31* 0.36* 0.55* 0.21* 0.34* 0.16* ‐‐
N = 402 students, *p < 0.01
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Comparing the correlation coefficients between the seven factors of the learning
environment scale, self‐efficacy, the two factors of the situational interest scale,
academic performance and level of commitment at time 1 with those at time 2, there
were no substantial differences.
4.6 Joint Relationships between Learning Environment, Self‐efficacy, Situational Interest, Academic Performance, and Prior Schooling as Independent Variables and Level of Commitment as Dependent Variable
What is the joint relationship between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, prior schooling as
independent variables and level of commitment as dependent variable at time 1 and at
time 2?
Multiple linear regression analysis was used to examine the joint relationships between
the seven factors of the learning environment scale, the two factors of the situational
interest scale, students’ academic performance, and prior schooling as independent
variables and student’ level of commitment as dependent variable at time 1 and
separately at time 2. Table 18 and 19 show the results at time 1 and time 2 respectively.
At time 1, the regression analysis results were statistically significant at the beginning of
the first year, R2 = 0.39, F(13, 388) = 19.10, p < 0.001, indicating that self‐efficacy ( =
0.46, p < 0.01) made the largest contribution to the regression equation, when holding
all other variables constant. The results also indicated that triggered situational interest
( = ‐0.18, p < 0.01) played a more minor but still statitically significant role in the
regression effect while holding all other variables constant. Overall, at the beginning of
the first year, i.e. time 1, these two variables accounted for 39.0% of the variance in
students’ level of commitment to engineering education. Self‐efficacy was a good
predictor for level of commitment. Triggered situational interest was a weak predictor
of level of commitment.
Similarly, the regression analysis results were statistically significant at the end of the
first year, R2 = 0.40, F(13, 388) = 19.76, p < 0.001, indicating that self‐efficacy ( = 0.44,
p < 0.01) made the largest contribution to the regression equation, when holding all
other variables constant. The results also indicated that task orientation ( = 0.14, p <
0.01) played a more minor but still statitically significant role in the regression effect
Level of commitment to engineering education in a polytechnic in Singapore
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while holding all other variables constant. Overall, at the end of the first year (i.e. time
2), these two variables accounted for 40.0% of the variance in students’ level of
commitment to engineering education. Self‐efficacy was a good predictor for level of
commitment. Task orientation was a weak predictor of level of commitment.
The earlier results showed that academic performance did not show substantial
correlations with any of the factors. As expected, academic performance did not
significantly affect level of commitment in the regression model. Suprisingly, prior
schooling also did not contribute to the regression model, suggesting that it was not a
significant predictor for students’ level of commiment to engineering education.
Table 18 Regression Analysis to Predict Level of Commitment at Time 1
Independent Variables Level of Commitment
Student Cohesiveness 0.08
Lecturer Support ‐0.04
Involvement ‐0.14
Investigation 0.03
Task Orientation 0.12
Cooperation 0.04
Equity 0.16
Self‐efficacy 0.46*
Triggered Situational Interst ‐0.18*
Maintained Situational Interest 0.09
Academic Performance 0.09
ITE student ‐0.02
Foreign student 0.02
= 0.39
F(13, 388) = 19.10*
*p < 0.01
Table 19 Regression Analysis to Predict Level of Commitment at Time 2
Independent Variables Level of Commitment
β
Student Cohesiveness 0.03
Lecturer Support 0.01
Involvement 0.07
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Independent Variables Level of Commitment
β
Investigation ‐0.07
Task Orientation 0.14*
Cooperation 0.08
Equity 0.13
Self‐efficacy 0.44*
Triggered Situational Interst ‐0.12
Maintained Situational Interest 0.10
Academic Performance ‐0.02
ITE student ‐0.06
Foreign student 0.04
= 0.40
F(13, 388) = 19.76*
*p < 0.01
4.7 Changes in Students’ Level of Commitment between Time 1 and Time 2
What changes are there in students’ level of commitment to engineering education
between time 1 and time 2?
To assess whether the mean value of the level of commitment scale at time 1 is
statistically higher or lower than time 2, a paired‐sample t‐test was conducted. As
described in chapter 3, the level of commitment scale consists of nine items that seek
responses from students on a scale of 1 (least favourable answer) to 5 (most favourable
answer). Thus, the score range is from 9 to 45. The mean score at time 1 was 32.41 (SD
= 4.79) and the mean score at time 2 was 31.75 (SD = 4.65). The t‐test result revealed a
statistically significant difference between the mean scores for level of commitment
(t(401) = 2.91, p = 0.004) between time 1 and time 2. The effect size (d = 0.14) was found
to be smaller than Cohen’s (1988) convention for a small effect (d = 0.2). The finding
indicated that students’ level of commitment to engineering education decreased over
the first year at the polytechnic under this study but the decrease was clearly very small.
4.8 Summary
This study involved a sample of 402 first year students from a polytechnic in Singapore.
This chapter has reported the findings for the four research questions.
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80
The first research question concerned the psychometric investigation of the situational
interest scale, learning environment scale, self‐efficacy scale and level of commitment
scale that were extracted and modified slightly from the Situational Interest Survey, the
What is Happening in This Class Survey, the Self‐Efficacy for Broad Academic Milestones
Survey, and the College Persistence Questionnaires. Factor analysis confirmed the
conceptual structure of the situational interest scale with the three factors of triggered
situational interest, maintained situational interest‐feeling, and maintained situational
interest‐value. As there were high correlations between maintained situational interest‐
feeling and maintained situational interest‐value, and in line with previous research,
these two scales were combined for subsequent analyses. Factor analyses also
confirmed the conceptual structure of the learning environment scale with the seven
factors of student cohesiveness, lecturer support, involvement, investigation, task
orientation, cooperation and equity; the one factor structure for the self‐efficacy scale;
as well as a one‐factor structure for the level of commitment scale, when used with first
year students in a polytechnic in Singapore. The Cronbach alpha coefficients confirmed
the internal consistency reliability of the situational interest scale, the learning
environment scale, the self‐efficacy scale and the level commitment scale.
The second research question examined the relationships between students’ perception
of the learning environment, self‐efficacy, their situational interest, academic
performance, and level of commitment at time 1 and separately at time 2. Similar results
were found when comparing these relationships at time 1 and at time 2. The
correlations at time 1 and time 2 showed that the more support lecturers provided for
students in the class (lecturer support), the more they perceived themselves to be
treated equally by the lecturers (equity). At the same time, students were more
attentive and interested in participating in class activities (involvement) when lecturers
encouraged them to ask questions, gave opinions and explained ideas (lecturer support).
The correlations at time 1 and time 2 also showed that when students were provided
opportunities to take part in inquiry problem‐solving activities and investigations
(investigation), they were more attentive and interested in participating in these
activities (involvement). In addition, the correlations at time 1 and time 2 showed that
when lecturers created a caring and supportive learning environment in the class
(lecturer support), students were more emotionally involved in both the presentation
Level of commitment to engineering education in a polytechnic in Singapore
81
mode (triggered situational interest) and the domain content (maintained situational
interest) of the materials. Finally, there were strong positive correlations between
students’ emotional involvement in the presentation mode of the materials (triggered
situational interest) and their emotional involvement in the domain content (maintained
situational interest) of the materials. Surprisingly, students’ perception of their learning
environment, their situational interest, and level of commitment to engineering
education had no substantial relationships with their academic performances.
The third research question examined the joint relationships between students’
perception of the learning environment, their self‐efficacy, situational interest,
academic performance, and prior schooling as independent variables, and level of
commitment to engineering education as dependent variable. Approximately 39.0% of
the variance in students’ level of commitment to engineering education was accounted
for by using these independent variables at time 1 and time 2. Regression analysis
results showed that students’ self‐efficacy was a good predictor, and the extent to which
they valued the importance of completing planned activities and stayed on the subject
matter (task‐orientation) and their emotional involvement in the presentation mode
(triggered situational interest) were weak predictors of students’ level of commitment
to engineering education. As expected, students’ academic performance in key
engineering module was not a significant predictor of students’ level of commitment to
engineering education. Surprisingly, there was no significant evidence that students’
prior schooling (‘O’ Level, ITE or Foreign) was a good predictor of students’ level of
commitment to engineering education.
The fourth research question examined the changes in students’ level of commitment
to engineering between time 1 and time 2. A paired‐sample t‐test indicated that
students’ level of commitment to engineering education decreased as they progressed
from semester 1 to semester 2 during their first year of studies. However, although this
decrease was statistically significant, the decrease was small.
The next chapter provides a summary of the findings and discusses the findings and their
implications, as well as making some recommendations for future research.
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Chapter 5: Summary, Discussion, Implications and Recommendations
5.1 Summary of the Study
The purpose of this study was to examine the relationships between five independent
variables and students’ level of commitment to engineering education as the dependent
variable during the first year of studies at a polytechnic in Singapore. These five
independent variables include students’ perception of their learning environment, the
situational interest generated in the classrooms of Engineering Mathematics and
Introduction to Engineering, students’ self‐efficacy, academic performance, and prior
learning experiences. This study involved a sample of 402 first year students from a
polytechnic in Singapore. Two administrations of survey questionnaires were carried
out at time 1 (July 2014) and time 2 (January 2015).
More specifically, this study sought to: (1) investigate the psychometric characteristics
of several existing instruments ‐ the situational interest scale, the learning environment
scale, the self‐efficacy scale and the level of commitment scale ‐ when they are used
with first year engineering students in a polytechnic in Singapore; (2) examine the
relationships between students’ perception of the learning environment, self‐efficacy,
situational interest, academic performance, and level of commitment; (3) investigate
the joint relationship between students’ perception of the learning environment, self‐
efficacy, situational interest, academic performance, and prior schooling as independent
variables and level of commitment as dependent variable; and (4) examine changes in
students’ level of commitment to engineering education over the first year of studies.
The research questions are:
Research Question 1
Are the following instruments valid and reliable when used with first year engineering
students in a polytechnic in Singapore?
a. Situational interest scale
b. Learning environment scale
c. Self‐efficacy scale
d. Level of commitment scale
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83
Research Question 2
What are the relationships between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, and level of commitment at
time 1 and time 2?
Research Question 3
What is the joint relationship between students’ perception of the learning environment,
self‐efficacy, situational interest, academic performance, prior schooling as
independent variables and level of commitment as dependent variable at time 1 and
time 2?
Research Question 4
What change is there in students’ level of commitment to engineering education
between time 1 and time 2 of this study?
To address research question 1, factor analysis was used to check the questionnaire
structure and Cronbach’s alpha coefficeint was used to measure internal consistency.
To address research question 2, Pearson product‐moment correlations were computed
to analyse the relationships between learning environment, students’ situational
interest, self‐efficacy, academic performance and level of commitment. To address
research question 3, multiple linear regression analysis was used to investigate the joint
relationship between the five independent variables (learning environment, situational
interest, self‐efficacy, academic performance, and prior schooling) and level of
commitment as dependent variable. Finally, a paired sample t‐test was used to address
research question 4. Research question 4 focused on changes in students’ level of
commitment to engineering education between time 1 and time 2.
Before the main data analysis was carried out, the sample of 402 students was
partitioned into subsamples of students based on time 1 and time 2, study path A and
study path B, Introduction to Engineering and Engineering Mathematics, and prior
learning experiences (‘O’ Level, ITE and Foreign students). As the findings showed
extremely similar results using the different subsamples, the reporting of findings was
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84
based on the pooled sample of 402 students. The results are summarized in the next
section.
5.2 Summary of Results
5.2.1 Research Question 1: Are the instruments (situational interest scale, learning environment scale, self‐efficacy scale, and level of commitment scale) valid and reliable when they are used with first year engineering students in a polytechnic in Singapore?
The existing survey instruments for measuring situational interest, learning environment,
self‐efficacy, and level of commitment were slightly modified to be more suitable for the
Singapore context. Appropriate items from the different instruments were then
compiled into an overall questionnaire of 81 items, to measure situational interest,
learning environment, self‐efficacy, and level of commitment. This overall questionnaire
was then administered to the 402 engineering students in the Engineering Mathematics
and Introduction to Engineering classrooms during their first year studies.
Principal axis factor analyses followed by varimax rotation confirmed the existence of
three factors for the 12 items from the situational interest scale (accounted for
82.9% of the variance),
seven factors for the 56 items from the learning environment scale (accounted
for 70.5% of the variance),
one factor for the four items from the self‐efficacy scale (accounted for 89.0% of
the variance), and
one factor for the nine items from the level of commitment scale (accounted for
48.6% of the variance).
Using Cronbach’s alpha, strong internal consistency was found for all extracted factors
for the different variables. Alpha coefficients ranged from 0.84 to 0.96. The factor
analyses and internal consistency analyses were carried for on both sets of data – time
1 and time 2 – and very similar results were obtained for the two sets.
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85
5.2.2 Research Question 2: What are the relationships between students’ perception of the learning environment, self‐efficacy, situational interest, academic performance, and level of commitment?
The aim of research question 2 was to examine the relationship between students’
perception of the learning environment in the Engineering Mathematics and
Introduction to Engineering classrooms, their situational interest, self‐efficacy,
academic performance, level of commitment. It also aimed to examine whether these
relationships were similar at time 1 and at time 2.
Generally, the several different factors here were positively related to each other. More
specifically:
The seven factors of learning environment (student cohesiveness, lecturer
support, involvement, investigation, task orientation, cooperation and equity)
were positively related to the one factor of self‐efficacy, the two factors of
situational interest (triggered situational interest and maintained situational
interest), and the one factor of level of commitment in both the Engineering
Mathematics and Introduction to Engineering classrooms.
Self‐efficacy was positively related to the two factors of situational interest
(triggered situational interest and maintained situational interest), the academic
performance and the level of commitment in both the Engineering Mathematics
and Introduction to Engineering classrooms.
Triggered situational interest was positively related to maintained situational
interest and maintained situational interest was positively related to level of
commitment.
Academic performance was positively related to level of commitment. However,
academic performance had no substantial relationships with the seven factors
of learning environment (student cohesiveness, lecturer support, involvement,
investigation, task orientation, cooperation and equity) and the two factors of
situational interest (triggered situational interest and maintained situational
interest).
In general, the pattern of correlations at time 2 was again similar to the pattern at
time 1.
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86
5.2.3 Research Question 3: What is the joint relationship between students’ perception of the learning environment, self‐efficacy, situational interest, academic performance, prior schooling as independent variables and level of commitment as dependent variable?
The aim of research question 3 was to examine the joint relationship between students’
perception of the seven factors of the learning environment scale, their self‐efficacy, the
two factors of the situational interest scale, their academic performance, and prior
schooling (O Level, ITE or Foreign) as independent variables, and the level of
commitment to engineering education as dependent variable. The analyses were
performed at time 1 and separately at time 2.
Multiple linear regression analysis was carried out, using learning environment,
situational interest, self‐efficacy, academic performance, and prior schooling as
independent variables, and level of commitment as dependent variable at time 1. The
squared multiple correlation for this was 0.39, (p < 0.01), suggesting that about 39.0%
of the variance in level of commitment can be accounted for by these independent
variables. The beta weights indicated that the most important independent variable
here was self‐efficacy. Task orientation and triggered situational interest were weak
predictors to students’ level of commitment to engineering education. Academic
performance and prior schooling did not contribute to the regression model. Similar
results were obtained when this analysis was repeated using measures taken at time 2.
5.2.4 Research Question 4: What change is there in students’ level of commitment to engineering education between time 1 and time 2?
The aim of research question 4 was to examine the changes in students’ level of
commitment to engineering education between time 1 and time 2. The finding showed
that the level of commitment decreased slightly as they progressed from the beginning
to the end of their first year of studies. While decrease was statistically significant, this
decrease was small.
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87
5.3 Discussion
5.3.1 Research Question 1: Are the instruments (situational interest scale, learning
environment scale, self‐efficacy scale, and level of commitment scale) valid and
reliable when they were used with first year engineering students in a
polytechnic in Singapore?
The factor analysis results constituted strong evidence of the two‐factor structure of the
situational interest scale, the seven‐factor structure of the learning environment scale,
and the unidimensionality of both the self‐efficacy scale and the level of commitment
scale when they were used for data collected from the Engineering Mathematics and
Introduction to Engineering classrooms.
Overall, these results help to confirm that these instruments have satisfactory
psychometric properties. These results are also consistent with the psychometric
characteristics reported in the literature: the situational interest scales (Linnenbrink‐
Garcia et al. in 2010; Linnenbrink‐Garcia, Patall, & Messersmith, 2013); the learning
environment scales (Fraser et al., 2010; MacLeod and Fraser, 2010); the self‐efficacy
scale (Jones et al., 2010; Lee et al., 2015); and the level of commitment scale (Davidson
et al., 2009; Garrison, 2014).
5.3.2 Research Question 2: What are the relationships between students’ perception
of the learning environment, self‐efficacy, situational interest, academic
performance, and level of commitment?
The findings from research question 2 showed all seven factors in the learning
environment scale (student cohesiveness, lecturer support, involvement, investigation,
task orientation, cooperation and equity) were positively and significantly related to
self‐efficacy, to both factors of the situational interest scale (triggered situational
interest and maintained situational interest), and to the level of commitment in the
Engineering Mathematics and Introduction to Engineering classrooms. These findings
are consistent with research studies that show that learning environment contributes
significantly to student learning (Fraser, 2007, 2012; Dorman and Fraser 2009).
Students’ situational interest, self‐efficacy and commitment are enhanced in
Engineering Mathematics and Introduction to Engineering classrooms where lecturers
provide a positive learning environment that allows students to work in groups, do
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88
hands‐on activities, engage in group discussion, and work with friends (Del Favero,
Boscolo, Vidotto, & Vicentini, 2007; Dohn, Madsen, & Malte, 2009; Palmer, 2009).
Students’ situational interest, self‐efficacy and commitment are further enhanced when
they are engaged with lecturers who are seen as fair, approachable and friendly (Dohn
et al., 2009; Frenzel, Goetz, Pekrun, & Watt, 2010; Rotgans & Schmidt, 2011; Schiefele,
2009). Students’ situational interest, self‐efficacy and commitment are also enhanced
when the module materials are made meaningful and tied to everyday life (Dohn et al.,
2009; Durik & Harackiewicz, 2007; Hulleman & Harackiewicz, 2009; Hulleman, Durik,
Schweigert, & Harackiewicz, 2008). While individual interest is not measured in this
study, research also shows that if students’ situational interest is maintained over a
longer period of time, it may offer an alternative to the development of individual
interest (Shen, Chen, & Guan 2007; Subramaniam, 2010). When students’ situational
interest is maintained, it has the potential to enhance the development of growth of
individual interest in engineering education in the future.
What is surprising from this study is the finding that there are either no relationships, or
only weak relationships, between the seven factors in the learning environment scale
and students’ academic performance in Engineering Mathematic and Introduction to
Engineering. These results are not consistent with research studies which show that
learning environment plays a key role in students’ academic performance (Lizzio, Wilson,
& Simons, 2002; Tella, 2008). However, these results are aligned with research studies
which find little support for a direct relationship between student perceptions of the
learning environment and academic performance (Dethlefs, 2002; Wong, 2003). In this
connection, Wong (2003) argued that a mismatch between instruction and assessment
task might be one reason why there were no direct relationships between students’
perception of the learning environment and their academic performance. This is
because when instruction and assessment are not aligned, students tend to withdraw
or skip classes, and they tend to become frustrated with their learning environment,
which in turn, leads to poor academic performance.
Another possible explanation is that it is unlikely that students’ self‐reported perception
of their learning environment directly translates into academic performance. Rotgans
et al. (2008) showed that it was unlikely that self‐reported beliefs, which students had
about their interest and motivation to study, directly translated into academic
Level of commitment to engineering education in a polytechnic in Singapore
89
performance. Their assumption was that motivational beliefs as measured by self‐report
measures must be converted into observable achievement‐related classroom
behaviours first, before they could influence academic performance. Some of these
observable achievement‐related classroom behaviours highlighted by Rotgans and
colleagues included the extent to which students participated in group discussions, the
extent to which they engaged and persisted in self‐directed learning, and the quality of
their presentations in the classroom.
As for the correlations among the self‐efficacy scale, the two factors of the situational
interest scale, academic performance and the level of commit scale, the findings show
a moderate positive relationship (0.40 < r < 0.60) between self‐efficacy and level of
commitment, a weak positive relationship (0.20 < r < 0.40) between self‐efficacy and
triggered situational interest, maintained situational interest, and academic
performance. Once again, these findings are consistent with similar research studies.
As highlighted by Niemivirta and Tapola (2007), when one successfully completes a task
in a progressive manner, it encourages situational interest and, subsequently, results in
stronger sense of self‐efficacy. Consequently, such positive changes in self‐efficacy and
situational interest contribute to students’ academic performance (Jones et al., 2010;
Loo & Choy, 2013; Purzer, 2011; Vogt, 2008). In turn, students’ academic performance
is likely to influence their level of commitment to engineering education (Eris et al., 2010;
Lent, Sheu, Gloster, & Wilkins, 2010).
The present findings also show a strong positive relationship (r ≥ 0.60) between
triggered situational interest and maintained situational interest, and a weak positive
relationship (0.20 < r < 0.40) between maintained situational interest and level of
commitment. This suggests that once students are engaged and focused on the
materials of the subject matter while the materials are presented (triggered situational
interest), they are more likely to value the materials beyond the context of that
particular content and to seek new opportunities to explore the domain and expand
their knowledge (maintained situational interest). Although individual interest and
situational interest are conceptually different, studies show that maintained situational
interest can grow into individual interest (Hidi & Renninger, 2006; Krapp, 2002). Once
individual interest is developed, it is a driver that leads one to commit oneself to do
things (Hidi & Regnninger, 2006) and has an influence on students’ choice of college
Level of commitment to engineering education in a polytechnic in Singapore
90
major (Harackiewicz, Barron, Tauer, & Elliot, 2002) and career (Tai et al., 2006). As
discussed earlier, while individual interest is not measured in this study, the positive
relationship between maintained situational interest and level of commitment shows
that the more students seek opportunities to expand their knowledge in engineering,
the higher their level of commitment to engineering education. Further research could
examine whether such maintained situational interest does indeed grow into individual
interest.
5.3.3 Research Question 3: What is the joint relationship between students’
perception of the learning environment, self‐efficacy, situational interest,
academic performance, prior schooling as independent variables and level of
commitment as dependent variable?
The overall R2 was 0.39, which indicates that approximately 39.0% of the variance in
level of commitment is accounted for by these independent variables. The beta weights
indicated that the most important independent variable here was self‐efficacy. Similar
results were obtained when this analysis was repeated using measures taken at time 2.
These findings are consistent with similar research (Lent et al., 2010; Vuong, Brown‐
Welty, & Tracz, 2010).
Students with high self‐efficacy are more ready to persist when facing difficulties and
recover more quickly after a failure. Conversely, students with low self‐efficacy tend to
lose interest in their learning and undermines their ability to commit to achieving their
goals (Bandura, 1997; Luszczynska, Gutiérrez‐Doña, & Schwarzer; 2005). For example,
many students experience Engineering Mathematics as a difficult module and do not
perceive the relevance of Engineering Mathematics to their future studies or career
choice. Therefore, students who generally experience lower levels of self‐efficacy will
probably invest less of their time and effort in their studies. By investing less time and
effort in their studies, students’ overall level of commitment to engineering education
will be affected. The beliefs that students develop about their academic abilities are
likely to influence the type of decisions they take ‐ whether to remain in, or leave,
engineering education (Pajares, 2002).
While task orientation is a weak predictor of students’ level of commitment in this study,
this finding is consistent with research studies. These studies showed that students,
who perceived their classroom environment to be task oriented, might have higher
Level of commitment to engineering education in a polytechnic in Singapore
91
academic self‐efficacy (Lim, 2013; Dorman & Adams, 2004). Students who have high
academic self‐efficacy have greater level of commitment (Pajares, 2002).
In addition, triggered situational interest is a weak negative predictor of students’ level
of commitment in this study. This result is counterintuitive as one may assume that
increased students’ engagement and focus on the materials of the subject matter while
the materials are presented (triggered situational interest) would improve students’
level of commitment. A plausible explanation for the decreased level of commitment of
students who are engaged and focused on the materials presented in the class is that
they may consider the subject matter, Engineering Mathematics and Introduction to
Engineering, as not important learning subjects in relation to their future educational or
career plan. As described by Krapp (2007), a student would continuously engage with a
learning domain and its tasks only if the engagement is assessed as important in
individually relevant cognitive, affective, and value dimensions.
On the other hand, while it is expected that students’ academic performance in key
introductory modules (Engineering Mathematics and Introduction to Engineering) is not
a significant predictor of students’ level of commitment to engineering education, it is
surprising to find that students’ prior schooling (‘O’ Level, ITE or Foreign) is not a
significant predictor. This finding is not consistent with research studies that identified
academic performance in key introductory modules (Araque et al., 2009; Suresh 2006)
and students’ pre‐college performance (Kokkelenberg & Sinha, 2010; Prochea et al.,
2010; Richardson et al., 2012) as strong predictors for students’ level of commitment to
engineering education.
As discussed in chapter 1, polytechnic students who were involved in this study are from
different backgrounds. It is generally observed that the ‘O’ level and foreign students
are generally more academically inclined while ITE students favour a more practice‐
based approach to learning (Koh et al., 2010). The two key introductory modules used
in this study were Engineering Mathematics and Introduction to Engineering.
Engineering Mathematics module is more theoretical oriented while Introduction to
Engineering is more practical oriented. Students of differential backgrounds may
perceive these two key introductory modules as easy or challenging differently. For
example, ‘O’ level and foreign students may perceive Engineering Mathematics module
Level of commitment to engineering education in a polytechnic in Singapore
92
as easy and thus perform well in this module. However, they may perceive Introduction
to Engineering module as challenging and thus may not perform as well in this module.
The converse is true for ITE students. Thus, students’ academic performance in these
two key introductory modules and their prior schooling may not contribute much to
students’ level of commitment to engineering education. In summary, the reasons for
students eventually leaving engineering education may be linked to other factors that
can be more difficult to identify.
5.3.4 Research Question 4: What change is there in students’ level of commitment to
engineering education between time 1 and time 2?
The findings from research question 4 showed that students’ level of commitment to
engineering education decreased over the first year, but the decrease was small.
The earlier findings in this study indicate that changes in students’ perception of the
learning environment, as well as changes in how much self‐efficacy the students develop
during their first year of studies at the polytechnics, are related to changes in students’
level of commitment (Human‐Vogel & Mahlangu, 2009; Luszczynska et al., 2005). When
students transit from secondary school to polytechnic, they need time and effort to
adjust and adapt themselves to the new environment. At the beginning of the first year,
students generally do well in engineering‐related modules that provide a positive
learning environment. As they progress through their first year of studies, there is a
steady increase in the difficulty of the coursework, and some may find it a challenge to
complete the engineering tasks that require more effort and time. Thus, towards the
end of their first year, it is not surprising that students’ level of commitment decreases
(Jones et al., 2010).
Although the decrease in level of commitment over the first year is found to be small in
this study, research studies have shown that students frequently re‐evaluate their level
of commitment over the course of their studies (Lichtenstein et al., 2007; McCain,
Fleming, Williams, & Engerman, 2007). Thus, students’ commitment to engineering
education may increase, decrease or stay the same as they progress to their second or
third year of study at the polytechnic. One other possible explanation for the small
decrease in the level of commitment may be from the cultural perspective as described
in Chapter 2, where students tend to conform to their parent’s wishes to stay in the
Level of commitment to engineering education in a polytechnic in Singapore
93
polytechnic until graduation. Further research could examine how students’ level of
commitment changes over the course of their three‐year diploma programme rather
than over only their first year of studies.
5.4 Implications
In this section, four main implications of the findings from this study are discussed.
5.4.1 Measure of Situational Interest, Learning Environment, Self‐efficacy and Level
of Commitment for Polytechnic Students
Results from this study show that the 81‐item instrument, designed to measure
situational interest, learning environment, self‐efficacy and level of commitment, has
satisfactory psychometric properties when it is used with first year engineering students
from a polytechnic in Singapore. Chapter 2 reviews the past research undertaken on
level of commitment and the variety of instruments developed for measuring level of
commitment. The focus of past research, mostly at the university level in western
countries, has been mainly on individual student and institutional factors. The
uniqueness of the present study lies in its focus on individual and learning environmental
factors in the Engineering Mathematics and Introduction to Engineering classrooms at a
polytechnic in an East Asia country. As discussed in chapter 1, this study adds an
additional cross‐cultural dimension on how values, from the East‐Asian perspective,
where culture has strong influences over values and expectancies, impact students’
decision on whether to commit to engineering education or a career in engineering in
Singapore. This study provides further evidence of satisfactory psychometric properties
of the situational interest scale, the learning environment scale, the self‐efficacy scale
and the level of commitment scale when used with engineering classes at a polytechnic
in Singapore.
An implication is that this instrument can be used to measure polytechnic students’ level
of commitment not just for the engineering disciplines, but also for other disciplinary
diploma courses that are offered in the polytechnics in Singapore.
Level of commitment to engineering education in a polytechnic in Singapore
94
5.4.2 Strategies to Increase Students’ Self‐Efficacy in Engineering
One important finding from this study is that self‐efficacy is a strong predictor of level
of commitment, and there is a strong positive relationship between self‐efficacy and
level of commitment. Self‐efficacy is also found to have positive relationship with the
learning environment, situational interest and academic performance. These findings
demonstrate that students who believe they can do well in engineering generally enjoy
doing the engineering tasks in a positive learning environment. They are more engaged
in the activities and focused on the materials presented to them in the classrooms, and
they also seek opportunities to expand their knowledge in engineering. As they
progress in the tasks and accomplish the tasks, positive affective states are induced. In
turn, the positive affective states result in stronger sense of self‐efficacy. As both their
self‐efficacy and interest improve, it is likely that they perform well academically and
commit themselves to complete the engineering diploma (Niemivirta & Tapola, 2007).
The implication for the lecturers of the polytechnic in this study is to be aware that they
do have control of the learning environment. The creation of a positive learning
environment by the lecturers has the potential to enhance the development and growth
of students’ self‐efficacy in engineering and increase students’ level of commitment to
engineering education in the future (Hidi & Renninger, 2006; Shen et al., 2007).
Lecturers should consider using classroom strategies that foster mastering of task
according to students’ standards in the classroom in order to strengthen students’ self‐
efficacy beliefs and their commitment to engineering education. These teaching
strategies include emphasizing learning from mistakes, giving positive, diagnostic
feedback that focuses on personal improvement, minimizing comparisons with other
students and emphasizing comparisons with previous performance, and giving students
choice (Friedel, Cortina, Turner, & Midgley, 2007; Vansteenkiste, Lens, & Deci, 2006).
Lecturers should also provide a learning environment that actively involves and engages
students in discussion and provides more opportunities for students to work together.
The greater the involvement in peer group interaction, the more likely students are to
commit to their studies (Kuh, Cruce, Shoup, Kinzie, & Gonyea, 2008; Tinto, 2006; Titus,
2004).
Level of commitment to engineering education in a polytechnic in Singapore
95
Another implication is for the lecturers of polytechnics to instil positive engineering self‐
efficacy in their students using learning strategies such as goal setting and think‐aloud
procedures (Schunk and Pajares, 2002). Students’ engineering self‐efficacy can be
strengthened if they are encouraged to develop specific, short‐term goals that are
personally meaningful to them, so that they are able to attain these goals and find their
studies more meaningful. Students’ engineering self‐efficacy can be further
strengthened if lecturers help students to lay out specific learning strategies and
verbalise their plan, so that students are able to note their progress, be more systematic
in their work and more in control of their learning.
5.4.3 Strategies to Increase Students’ Situational Interest in Engineering
Another important finding from this study is that there is a significant positive
relationship between triggered situational interest and maintained situational interest,
and between maintained situational interest and level of commitment. While
correlation between two variables does not necessarily imply causation, these findings
suggest that when students’ situational interest is triggered by environmental cues (such
as surprising information or personally relevant context), they are more likely to sustain
their situational interest by participating in meaningful tasks. When students
participate in meaningful tasks to expand their knowledge in engineering, they are more
likely to commit to engineering education (Harackiewicz et al., 2002; Hidi & Regnninger,
2006; Tai et al., 2006).
The implication is for lecturers to implement strategies in the classroom to increase
students’ situational interest. These strategies include offering meaningful choices to
students in a task, providing well‐organised, vivid and coherent instructional materials,
encouraging students to be active learners, being friendly, approachable, and supportive
to students, and providing relevance to the learning tasks. Some strategies to promote
the development of situational interest are briefly described below.
First, when students are given meaningful choices, they have greater control and
autonomy in a task, thus increasing their engagement and willingness to work on the
task and leading to greater conceptual learning (Harackiewicz et al., 2002; Harackiewicz,
Durik, Barron, Linnenbrink‐Garcia, & Tauer, 2008; Linnenbrink‐Garcia et al., 2013).
Second, when students are provided with well‐organised, vivid and coherent
Level of commitment to engineering education in a polytechnic in Singapore
96
instructional materials, their situational interest in a text increases and thus enhances
their recall and learning because students can follow and understand how the main idea
is elaborated (Schraw, Flowerday & Lehman, 2001). Third, when students are
encouraged to be actively involved in doing hands‐on activities, working in groups,
engages in group discussion, and working with friends, their situational interest
increases (Del Favero et al., 2007; Dohn et al., 2009; Palmer, 2009; Renninger & Hidi,
2002). Fourth, when students find the lecturers to be friendly, approachable, and
supportive, they develop feelings of relatedness, belongingness and a sense of
connection to the lecturers, thus facilitating the development of situational interest
(Frenzel et al., 2010; Linnenbrink‐Garcia et al., 2013; Rotgans & Schmidt, 2011). Fifth,
when the relevance and value of the information and skills are highlighted to students,
their situational interest increases and they are more motivated to learn (Dohn et al.,
2009; Hulleman & Harackiewicz, 2009; Hulleman et al., 2008; Linnenbrink‐Garcia et al.,
2013).
5.4.4 Policy to support students during first year of engineering education
One interesting finding from this study is that students’ level of commitment to
engineering education decreased over the first year but the decrease was small. The
fact that this decrease is only small is likely the result of the initiatives that the
polytechnic has put in place to help the first year students. These initiatives help to
create a positive first year experience to help students to transit from secondary school
to polytechnic life, making students aware of the resources that are available to them,
and increasing student interaction with both their peers and lecturers. These initiatives
include academic support and advising by lecturers, career counselling, peer tutoring,
and co‐curricular activities. In addition, an introduction to engineering module has been
introduced in the first year first semester in the school of engineering to motivate and
excite students about engineering.
As highlighted earlier, students frequently re‐evaluate their level of commitment over
the course of their studies (Lichtenstein et al., 2007; McCain et al., 2007). Therefore it
is important for the polytechnic to develop a policy that focuses on developing an
engaging and effective curriculum for the three year of studies and monitor students so
that relevant interventions can take place. One possible intervention is to have peer‐
Level of commitment to engineering education in a polytechnic in Singapore
97
mentoring programs to ensure that students, especially first year students, have
someone to turn to when they require assistance or when they need someone to talk to
other than the lecturers (Glaser, Hall & Halperin, 2006; Kift, 2009; Kift, Nelson & Clarke,
2010; Wheeler, 2012). The peer mentoring programs allow students to develop positive
relationships with their mentors who are of similar age, to better integrate into the
polytechnic environment, and to develop a wider connection with other students
(Glaser et al., 2006; Rodger & Tremblay, 2003).
5.5 Recommendations for Future Research
As indicated in the discussions in earlier sections, there are four main recommendations
from this study. First, while this quantitative study contributes to the growing literature
on academic commitment (Human‐Vogel, 2013; Human‐Vogel & Rabe, 2015), future
studies could use a mixed‐methods approach that is likely to enrich and extend the
insights gathered from the study. In addition, future studies could examine how
students’ level of commitment to engineering education changes over the three years
of the diploma courses rather than just focus on first year.
Second, one important finding is that self‐efficacy is a strong predictor for level of
commitment ‐ that is, it is likely that students’ low self‐efficacy perceptions, and not lack
of capability or skill, lead to the avoidance of engineering education and career. Further
studies could examine sources of engineering self‐efficacy and go deeper into how self‐
efficacy influences students’ level of commitment in engineering education. These
findings will help institutions to identify practices or interventions that foster the
development of both engineering competency and confidence among the students.
Third, multiple linear regression analysis was carried out for this study, using learning
environment, situational interest, self‐efficacy, academic performance, and prior
schooling as independent variables, and level of commitment as dependent variable.
About 39.0% of the variance in level of commitment is accounted for by these
independent variables. Future studies could include other independent variables such
as motivation to account for the remaining 61.0% of the variance in level of commitment.
Fourth, one surprising finding from this study is that while there are positive, significant
relationships between learning environment and self‐efficacy, between learning
Level of commitment to engineering education in a polytechnic in Singapore
98
environment and situational interest, and between learning environment and level of
commitment, there is no significant relationship between learning environment and
academic performance. As discussed in the earlier section, further investigations are
required to find out more about the relationships between the learning environment
and students’ academic performance. Research questions to be answered could include:
Which specific aspects of the learning environment influence students’ academic
performance? Is there a misalignment of instruction and assessment tasks? What
additional measures are to be included to measure students’ observable achievement‐
related classroom behaviours? How do these behaviours influence students’ academic
performance?
Level of commitment to engineering education in a polytechnic in Singapore
99
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Appendices
Appendix A: Comparison of the Wording of the Original versus the Modified and
Extracted Version of Self‐Efficacy for Broad Academic Milestone Survey
S/N Original Modified and Extracted
1 Complete the written communication
general education requirements (e.g.,
courses in writing skills) with grades
of at least 3.0.
Item not used
2 Complete the arts and humanities
general education requirements (e.g.,
courses in literature, history) with
grades of at least 3.0.
Item not used
3 Complete the biological, physical, and
mathematical sciences general
education requirements (e.g. course
in biology, geology) with grades of at
least 3.0.
Complete all of the ‘basic science’
(i.e. mathematics, engineering
science, mechanics, electronics etc.)
requirements for your engineering
programme with grades of B or
better.
4 Complete the social and behavioural
sciences general education
requirements (e.g., courses in
political science, sociology) with
grades of at least 3.0
Item not used
5 Earn a cumulative grade point
average of at least 2.0 after two
years of study.
Item not used
6 Earn a cumulative grade point
average of at least 2.0 after three
years of study.
Item not used
7 Gain admission to your first choice
major.
Item not used
8 Complete the requirements for your
academic major with a grade point
average of at least 3.0
Complete the upper level required
modules in your engineering
programme with an overall grade
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S/N Original Modified and Extracted point average (i.e. GPA) of 3.0 or
better
9 Excel at UMD over the next quarter Excel in your engineering programme
over the next semester.
10 Excel at UMD over the next two
quarters
Excel in your engineering programme
over the next two semesters.
11 Excel at UMD over the next three
quarters
Item not used
12 Graduate from UMD Item not used
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Appendix B: Comparison of the wording of the original and modified Version of ‘What
Is Happening In this Class?’ (WIHIC) Instrument
S/N Original Modified
Student Cohesiveness
1 I make friendships easily among
students in this class.
2 I know other students in this class.
3 I am friendly to members of this
class.
4 Members of the class are my friends.
5 I work well with other class
members.
6 I help other class members who are
having trouble with their work.
7 Students in this class like me.
8 In this class, I get help from other
students.
Teacher Support Lecturer Support
9 The teacher takes a personal interest
in me.
The lecturer takes a personal interest
in me.
10 The teacher goes out of his/her way
to help me.
The lecturer goes out of his/her way
to help me.
11 The teacher considers my feelings. The lecturer considers my feelings.
12 The teacher helps me when I have
trouble with the work.
The lecturer helps me when I have
trouble with the work.
13 The teacher talks with me. The lecturer talks with me.
14 The teacher is interested in my
problems.
The lecturer is interested in my
problems.
15 The teacher moves about the class to
talk with me.
The lecturer moves about the class to
talk with me.
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S/N Original Modified 16 The teacher’s questions help me to
understand.
The lecturer’s questions help me to
understand.
Involvement
17 I discuss ideas in class.
18 I give my opinions during class
discussions.
19 The teacher asks me questions. The lecturer asks me questions.
20 My ideas and suggestions are used
during classroom discussions.
21 I ask the teacher questions. I ask the lecturer questions.
22 I explain my ideas to other students.
23 Students discuss with me how to go
about solving problems.
24 I am asked to explain how I solve
problems.
Investigation
25 I carry out investigations to test my
ideas.
26 I am asked to think about the
evidence for statements.
27 I carry out investigations to answer
questions coming from discussions.
28 I explain the meaning of statements,
diagrams, and graphs.
29 I carry out investigations to answer
questions that puzzle me.
30 I carry out investigations to answer
the teacher’s questions.
I carry out investigations to answer
the lecturer’s questions.
31 I find out answers to questions by
doing investigations.
Level of commitment to engineering education in a polytechnic in Singapore
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S/N Original Modified 32 I solve problems by using information
obtained from my own
investigations.
Task Orientation
33 Getting a certain amount of work
done is important to me.
34 I do as much as I set out to do.
35 I know the goals for this class.
36 I am ready to start this class on time.
37 I know what I am trying to
accomplish in this class.
38 I pay attention during this class.
39 I try to understand the work in this
class.
40 I know how much work I have to do.
Cooperation
41 I cooperate with other students
when doing assignment work.
42 I share my books and resources with
other students when doing
assignments.
43 When I work in groups in this class,
there is teamwork.
44 I work with other students on
projects in this class.
45 I learn from other students in this
class.
46 I work with other students on class
activities.
Level of commitment to engineering education in a polytechnic in Singapore
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S/N Original Modified 47 I cooperate with other students on
class activities.
48 Students work with me to achieve
class goals.
Equity
49 The teacher gives as much attention
to my questions as to other students’
questions.
The lecturer gives as much attention
to my questions as to other students’
questions.
50 I get the same amount of help from
the teacher as do other students.
I get the same amount of help from
the lecturer as do other students.
51 I have the same amount of say in this
class as other students.
52 I am treated the same as other
students in this class.
53 I receive the same encouragement
from the teacher as other students
do.
I receive the same encouragement
from the lecturer as other students
do.
54 I get the same opportunity to
contribute to class discussions as
other students.
55 My work receives as much praise as
other students’ work.
56 I get the same opportunity to answer
questions as other students.
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Appendix C: Comparison of the Wording of the Original and Modified Version of
Situational Interest Survey
S/N Original Modified
1 My math teacher is exciting My Engineering Mathematics1
lecturer is exciting
2 When we do math, my teacher does
things that grab my attention.
When we do Engineering
Mathematics, my lecturer does things
that grab my attention.
3 This year, my math class is often
entertaining.
This semester, my Engineering
Mathematics class is often
entertaining.
4 My math class is so exciting it’s easy
to pay attention.
My Engineering Mathematics class is
so exciting it’s easy to pay attention.
5 What we are learning in math class
this year is fascinating to me.
What we are learning in Engineering
Mathematics class this semester is
fascinating to me.
6 I am excited about what we are
learning in math class this year.
I am excited about what we are
learning in Engineering Mathematics
class this semester.
7 I like what we are learning in math
this year.
I like what we are learning in
Engineering math this semester.
8 I find the math we do in class this year
interesting.
I find the Engineering Mathematics
we do in class this semester
interesting.
9 What we are studying in math class is
useful for me to know.
What we are studying in Engineering
Mathematics class is useful for me to
know.
10 The things we are studying in math
this year are important to me.
The things we are studying in
Engineering Mathematics this
semester are important to me.
11 What we are learning in math this
year can be applied to real life.
What we are learning in Engineering
Mathematics this semester can be
applied to real life.
1 Engineering Math will be replaced with Introduction to Engineering for students in another study path.
Level of commitment to engineering education in a polytechnic in Singapore
129
S/N Original Modified 12 We are learning valuable things in
math class this year.
We are learning valuable things in
Engineering Mathematics class this
semester.
Level of commitment to engineering education in a polytechnic in Singapore
130
Appendix D: Comparison of the Wording and Likert Scale Type of the Original and
Modified Version of College Persistence Questionnaire
S/N Original Modified
Institutional Commitment
1 How likely is it that you will earn a
degree from here?
[Very unlikely, Somewhat unlikely,
Neutral, Somewhat likely, Very likely]
How likely is it that you will earn a
diploma from here?
[Not at all likely, Slightly likely,
Somewhat likely, Very likely,
Extremely likely]
2 How confident are you that this is the
right university for you?
[Very unconfident, Somewhat
unconfident, Neutral, Somewhat
confident, Very confident]
How confident are you that this is the
right polytechnic for you?
[No confidence at all, Very little
confidence, Some confidence, Much
confidence, Complete confidence]
3 How likely is it that you will re‐enrol
here next semester?
[Very unlikely, Somewhat unlikely,
Neutral, Somewhat likely, Very likely]
No change
[Not at all likely, Slightly likely,
Somewhat likely, Very likely,
Extremely likely]
4 How much thought have you given to
stopping your education here
perhaps transferring to another
college, going to work, or leaving for
other reasons?
[Very little thought, Little thought,
Neutral, Some thought, A lot of
thought]
How much thought have you given to
stopping your education here perhaps
transferring to another polytechnic,
going to work, or leaving for other
reasons?
[No thought at all, Very little thought,
Some thought, Much thought, A lot of
thought]
Degree Commitment Diploma Commitment
5 When you think of the people who
mean the most to you (friends and
family), how disappointed do you
think they would be if you quit
school?
No change
[Not at all disappointed, Slightly
disappointed, Somewhat
disappointed, Very disappointed,
Extremely disappointed]
Level of commitment to engineering education in a polytechnic in Singapore
131
S/N Original Modified [Not at all disappointed, Not very
disappointed, Neutral, Somewhat
disappointed, Very disappointed]
6 At this moment in time, how certain
are you that you will earn a college
degree?
[Very uncertain, Somewhat
uncertain, Neutral, Somewhat
certain, Very certain]
At this moment in time, how certain
are you that you will earn a
polytechnic diploma?
[Not certain at all, Slightly certain,
Somewhat certain, Very certain,
Extremely certain]
7 At this moment in time, how strong
would you say your commitment is
to earning a college degree, here or
elsewhere?
[Very weak, Somewhat weak,
Neutral, Somewhat strong, Very
strong]
At this moment in time, how strong
would you say your commitment is to
earning a polytechnic diploma, here or
elsewhere?
[Not strong at all, Slightly strong,
Somewhat strong, Very strong,
Extremely strong]
8 How strong is your intention to
persist in your pursuit of the degree,
here or elsewhere?
[Very weak, Somewhat weak,
Neutral, Somewhat strong, Very
strong]
How strong is your intention to persist
in your pursuit of the diploma, here or
elsewhere?
[Not strong at all, Slightly strong,
Somewhat strong, Very strong,
Extremely strong]
9 How supportive is your family of your
pursuit of a college degree, in terms
of their encouragement and
expectations?
[Very unsupportive, Somewhat
unsupportive, Neutral, Somewhat
supportive, Very supportive]
How supportive is your family of your
pursuit of a polytechnic diploma, in
terms of their encouragement and
expectations?
[Not at all supportive, Slightly
supportive, Somewhat supportive,
Very supportive, Extremely
supportive]
Level of commitment to engineering education in a polytechnic in Singapore
132
Appendix E: Situational Interest, Learning environment, Self‐efficacy, and Persistence
Questionnaires
Directions to Students 1. This questionnaires has four parts:
a. Part 1 contains statements about situational interest in Engineering Mathematics class.
b. Part 2 contains statements about practices which could take place in Engineering Mathematics class.
c. Part 3 contains statements about your confidence in completing the academic requirements in engineering diploma.
d. Part 4 contains statements about your intention to obtain an engineering qualification.
2. There are no ‘right’ or ‘wrong’ answer. Your opinion is what is wanted. 3. For part 1 and part 2, think about how well each statement describes what the
class is like for you. 4. For part 3 and part 4, think about how well each statement describes about how
you feel about engineering.
5. Draw a circle around the number. If you change your mind about an answer, just cross it out and circle another.
6. Be sure to give an answer to ALL questions. 7. Some statements in this questionnaire are fairly similar to other statements.
Don’t worry about this. Simply give your opinion about all statements.
Level of commitment to engineering education in a polytechnic in Singapore
134
Part 1 Situational Interest
The following contains statements about situational interest in Engineering
Mathematics class. Please indicate how true each situation is in this class.
S/N Item Not
at all
true
Slightly
true
Some‐
what
true
Mostly
true
Very
true
1 My lecturer in this module is
exciting. 1 2 3 4 5
2
When we do this module, my
lecturer does things that grab my
attention.
1 2 3 4 5
3 This semester, my class is often
entertaining. 1 2 3 4 5
4 My class is so exciting it’s easy to
pay attention. 1 2 3 4 5
5 What we are learning in class this
semester is fascinating to me. 1 2 3 4 5
6 I am excited about what we are
learning in class this semester. 1 2 3 4 5
7 I like what we are learning in this
module this semester. 1 2 3 4 5
8 I find what we do in class this
semester interesting. 1 2 3 4 5
9 What we are studying in class is
useful for me to know. 1 2 3 4 5
10
The things we are studying in this
module this semester are
important to me.
1 2 3 4 5
11
What we are learning in this
module this semester can be
applied to real life.
1 2 3 4 5
12 We are learning valuable things in
this module this semester. 1 2 3 4 5
Level of commitment to engineering education in a polytechnic in Singapore
135
Part 2 Practices in the classroom
The following contains statements about practices which could take place in
Engineering Mathematics class. Please indicate how often each practice takes place in
this class.
S/N Item Never Almost
never
Some‐
times
Almost
every
time
Every
time
Student Cohesiveness
13 I make friendships easily
among students in this class. 1 2 3 4 5
14 I know other students in this
class. 1 2 3 4 5
15 I am friendly to members of
this class. 1 2 3 4 5
16 Members of the class are my
friends. 1 2 3 4 5
17 I work well with other class
members. 1 2 3 4 5
18 I help other class members
who are having trouble with
their work.
1 2 3 4 5
19 Students in this class like me. 1 2 3 4 5
20 In this class, I get help from
other students. 1 2 3 4 5
Lecturer Support
21 The lecturer takes a personal
interest in my learning. 1 2 3 4 5
22 The lecturer goes out of
his/her way to help me. 1 2 3 4 5
Level of commitment to engineering education in a polytechnic in Singapore
136
S/N Item Never Almost
never
Some‐
times
Almost
every
time
Every
time
23 The lecturer is considerate
and takes account of my
feelings.
1 2 3 4 5
24 The lecturer helps me when I
have trouble with the work. 1 2 3 4 5
25 The lecturer talks with me. 1 2 3 4 5
26 The lecturer is interested in
my problems. 1 2 3 4 5
27 The lecturer moves about the
class to talk with me. 1 2 3 4 5
28 The lecturer’s questions help
me to understand. 1 2 3 4 5
Involvement
29 I discuss ideas in class. 1 2 3 4 5
30 I give my opinions during
class discussions. 1 2 3 4 5
31 The lecturer asks me
questions. 1 2 3 4 5
32 My ideas and suggestions are
used during classroom
discussions.
1 2 3 4 5
33 I ask the lecturer questions. 1 2 3 4 5
34 I explain my ideas to other
students. 1 2 3 4 5
35 Students discuss with me
how to go about solving
problems.
1 2 3 4 5
36 I am asked to explain how I
solve problems. 1 2 3 4 5
Level of commitment to engineering education in a polytechnic in Singapore
137
S/N Item Never Almost
never
Some‐
times
Almost
every
time
Every
time
Investigation
37 I carry out investigations to
test my ideas. 1 2 3 4 5
38 I am asked to think about the
evidence for statements. 1 2 3 4 5
39 I carry out investigations to
answer questions coming
from discussions.
1 2 3 4 5
40 I explain the meaning of
statements, diagrams, and
graphs.
1 2 3 4 5
41 I carry out investigations to
answer questions that puzzle
me.
1 2 3 4 5
42 I carry out investigations to
answer the lecturer’s
questions.
1 2 3 4 5
43 I find out answers to
questions by doing
investigations.
1 2 3 4 5
44 I solve problems by using
information obtained from
my own investigations.
1 2 3 4 5
Task Orientation
45 Getting a certain amount of
work done is important to
me.
1 2 3 4 5
46 I do as much as I set out to
do. 1 2 3 4 5
47 I know the goals for this class. 1 2 3 4 5
Level of commitment to engineering education in a polytechnic in Singapore
138
S/N Item Never Almost
never
Some‐
times
Almost
every
time
Every
time
48 I am ready to start this class
on time. 1 2 3 4 5
49 I know what I am trying to
accomplish in this class. 1 2 3 4 5
50 I pay attention during this
class. 1 2 3 4 5
51 I try to understand the work
in this class. 1 2 3 4 5
52 I know how much work I have
to do. 1 2 3 4 5
Cooperation
53 I cooperate with other
students when doing
assignment work.
1 2 3 4 5
54 I share my books and
resources with other students
when doing assignments.
1 2 3 4 5
55 When I work in groups in this
class, there is teamwork. 1 2 3 4 5
56 I work with other students on
projects in this class. 1 2 3 4 5
57 I learn from other students in
this class. 1 2 3 4 5
58 I work with other students on
class activities. 1 2 3 4 5
59 I cooperate with other
students on class activities. 1 2 3 4 5
60 Students work with me to
achieve class goals. 1 2 3 4 5
Level of commitment to engineering education in a polytechnic in Singapore
139
S/N Item Never Almost
never
Some‐
times
Almost
every
time
Every
time
Equity
61 The lecturer gives as much
attention to my questions as
to other students’ questions.
1 2 3 4 5
62 I get the same amount of help
from the lecturer as do other
students.
1 2 3 4 5
63 I have the same amount of
say in this class as other
students.
1 2 3 4 5
64 I am treated the same as
other students in this class. 1 2 3 4 5
65 I receive the same
encouragement from the
lecturer as other students do.
1 2 3 4 5
66 I get the same opportunity to
contribute to class
discussions as other students.
1 2 3 4 5
67 My work receives as much
praise as other students’
work.
1 2 3 4 5
68 I get the same opportunity to
answer questions as other
students.
1 2 3 4 5
Level of commitment to engineering education in a polytechnic in Singapore
140
Part 3 Self‐Efficacy
The following is a list of major steps along the way to completing an engineering
diploma. Please indicate how much confidence you have in your ability to complete
each of these steps in relation to the engineering programme that you are most
likely to pursue.
S/
N
Item No confi‐
dence at
all
Very
Little
Confi‐
dence
Some
Confi‐
dence
Much
Confi‐
dence
Com‐
plete
Confi‐
dence
69 Complete all of the
‘basic science’ (i.e.
mathematics,
engineering science,
mechanics,
electronics etc.)
requirements for
your engineering
programme with
grades of B or
better.
0 1 2 3 4 5 6 7 8 9
70 Excel in your
engineering
programme over the
next semester.
0 1 2 3 4 5 6 7 8 9
71 Excel in your
engineering
programme over the
next two semesters.
0 1 2 3 4 5 6 7 8 9
72 Complete the upper
level required
modules in your
engineering
programme with an
overall grade point
average (i.e. GPA) of
3.0 or better.
0 1 2 3 4 5 6 7 8 9
Level of commitment to engineering education in a polytechnic in Singapore
141
Part 4 Persistence
The following contains statements about your intention to obtain an engineering
diploma. There are no 'right' or 'wrong' answers. Your opinion is what is wanted.
S/N Item
73 How likely is it that you will earn a diploma from here?
1
Not at all
likely
2
Slightly likely
3
Somewhat
likely
4
Very likely
5
Extremely
likely
74 How confident are you that this is the right polytechnic for you?
1
No
confidence at
all
2
Very little
confidence
3
Some
confidence
4
Much
confidence
5
Complete
confidence
75 How likely is it that you will continue your study here next semester?
1
Not at all
likely
2
Slightly likely
3
Somewhat
likely
4
Very likely
5
Extremely
likely
76 How much thought have you given to stopping your education here perhaps
transferring to another polytechnic, going to work, or leaving for other
reasons?
1
No thought at
all
2
Very little
thought
3
Some
thought
4
Much
thought
5
A lot of
thought
77 When you think of the people who mean the most to you (friends and family),
how disappointed do you think they would be if you quit school?
1
Not at all
disappointed
2
Slightly
disappointed
3
Somewhat
disappointed
4
Very
disappointed
5
Extremely
disappointed
Level of commitment to engineering education in a polytechnic in Singapore
142
78 At this moment in time, how certain are you that you will earn a polytechnic
diploma?
1
Not certain at
all
2
Slightly
certain
3
Somewhat
certain
4
Very certain
5
Extremely
certain
79 At this moment in time, how strong would you say your commitment is to
earning a polytechnic diploma, here or elsewhere?
1
Not strong at
all
2
Slightly
strong
3
Somewhat
strong
4
Very strong
5
Extremely
strong
80 How strong is your intention to persist in your pursuit of the diploma, here or
elsewhere?
1
Not strong at
all
2
Slightly
strong
3
Somewhat
strong
4
Very strong
5
Extremely
strong
81 How supportive is your family of your pursuit of a polytechnic diploma, in
terms of their encouragement and expectations?
1
Not at all
supportive
2
Slightly
supportive
3
Somewhat
supportive
4
Very
supportive
5
Extremely
supportive
Thank you for your participation.
Level of commitment to engineering education in a polytechnic in Singapore
143
Appendix F: Results of Analyses using Subsamples
Factor analyses using subsamples of students in Engineering Mathematics and
Introduction to Engineering to address research question 1a: Is the situational
interest scale valid and reliable when used with first year engineering students in a
polytechnic in Singapore?
Results of the analyses are shown in Table F1 and F2.
Table F1 Factor Loadings for Situational Interest – Engineering Mathematics
Item
Factor Loadings
Triggered Situational
Interest
Maintained
Situational Interest ‐
Feeling
Maintained
Situational Interest –
Value
1 0.82
2 0.82
3 0.69
4 0.65
5 0.75
6 0.77
7 0.70
8 0.75
9 0.76
10 0.79
11 0.85
12 0.83
% of
Variance 24.2 26.3 26.2
Eigenvalue 2.9 3.16 3.14
N = 402 students Factor loadings less than 0.40 had been omitted from the table
Level of commitment to engineering education in a polytechnic in Singapore
144
Table F2 Factor Loadings for Situational Interest ‐ Introduction to Engineering
Item
Factor Loadings
Triggered Situational
Interest
Maintained
Situational Interest ‐
Feeling
Maintained
Situational Interest –
Value
1 0.83
2 0.84
3 0.75
4 0.71
5 0.77
6 0.79
7 0.77
8 0.73
9 0.75
10 0.86
11 0.80
12 0.75
% of
Variance 26.6 27.8 27.4
Eigenvalue 3.19 3.34 3.25
N = 402 students Factor loadings less than 0.40 had been omitted from the table
Level of commitment to engineering education in a polytechnic in Singapore
145
Factor analyses using subsamples of students in Engineering Mathematics and
Introduction to Engineering to address research question 1b: Is the learning
environment scale valid and reliable when used with first year engineering students
in a polytechnic in Singapore?
Results of the analyses are shown in Table F3 and F4.
Table F3 Factor Loadings for Learning Environment – Engineering Mathematics
Item
Factors
Student
Cohesive‐
ness
Lectu‐rer
Sup‐port
Involve
‐ment
Investiga
‐tion
Task
Orienta‐
tion
Coopera‐
tion Equity
13 0.70
14 0.60
15 0.71
16 0.75
17 0.72
18 0.55
19 0.60
20 0.47
21 0.71
22 0.73
23 0.74
24 0.77
25 0.72
26 0.76
27 0.76
28 0.72
29 0.68
30 0.73
31 0.52
32 0.73
33 0.53
34 0.67
35 0.59
36 0.57
37 0.66
38 0.74
39 0.73
40 0.66
Level of commitment to engineering education in a polytechnic in Singapore
146
Item
Factors
Student
Cohesive‐
ness
Lectu‐rer
Sup‐port
Involve
‐ment
Investiga
‐tion
Task
Orienta‐
tion
Coopera‐
tion Equity
41 0.79
42 0.72
43 0.76
44 0.73
45 0.71
46 0.65
47 0.65
48 0.71
49 0.76
50 0.59
51 0.66
52 0.64
53 0.62
54 0.65
55 0.69
56 0.75
57 0.72
58 0.77
59 0.74
60 0.63
61 0.62
62 0.74
63 0.78
64 0.79
65 0.79
66 0.78
67 0.72
68 0.75
% of
Variance 7.93 10.6 7.69 9.98 8.53 8.78 10.2
Eigenvalue 4.44 5.96 4.3 5.59 4.78 4.92 5.67
N = 402 students Factor loadings less than 0.40 had been omitted from the table
Level of commitment to engineering education in a polytechnic in Singapore
147
Table F4 Factor Loadings for Learning Environment – Introduction to Engineering
Item
Factors
Student
Cohesive
‐ness
Lectu‐
rer
Sup‐
port
Involve
‐ment
Investiga
‐tion
Task
Orienta‐
tion
Coopera‐
tion Equity
13 0.72
14 0.73
15 0.77
16 0.77
17 0.73
18 0.54
19 0.60
20 0.63
21 0.73
22 0.75
23 0.72
24 0.68
25 0.74
26 0.76
27 0.74
28 0.70
29 0.65
30 0.69
32 0.54
33 0.52
34 0.62
35 0.50
36 0.57
37 0.76
38 0.66
39 0.79
40 0.70
41 0.81
42 0.81
43 0.77
44 0.75
45 0.68
46 0.78
47 0.73
Level of commitment to engineering education in a polytechnic in Singapore
148
Item
Factors
Student
Cohesive
‐ness
Lectu‐
rer
Sup‐
port
Involve
‐ment
Investiga
‐tion
Task
Orienta‐
tion
Coopera‐
tion Equity
48 0.75
49 0.69
50 0.62
51 0.78
52 0.70
53 0.64
54 0.72
55 0.74
56 0.77
57 0.72
58 0.81
59 0.79
60 0.74
61 0.66
62 0.73
63 0.78
64 0.75
65 0.75
66 0.80
67 0.74
68 0.75
% of
Variance 9.19 11.3 6.25 11.5 10.2 10.6 11.0
Eigenvalue 5.05 6.23 3.44 6.3 5.59 5.84 6.04
N = 402 students Factor loadings less than 0.40 had been omitted from the table
Level of commitment to engineering education in a polytechnic in Singapore
149
Factor analyses using subsamples of students in Engineering Mathematics and
Introduction to Engineering to address research question 1c and 1d: Are the self‐
efficacy scale and the level of commitment scale valid and reliable when they are
used with first year engineering students in a polytechnic in Singapore?
Results of the analyses are shown in Table F5 and F6.
Table F5
Factor Loadings for Self‐efficacy– Engineering Mathematics and Introduction to Engineering
Item Factors
Engineering Mathematics Introduction to Engineering
69 0.88 0.93
70 0.96 0.96
71 0.95 0.96
72 0.91 0.93
% of Variance 85.3 89.5
Eigenvalue 3.41 3.58
N = 402 students Factor loadings less than 0.40 had been omitted from the table Table F6
Factor Loadings for Level of Commitment – Engineering Mathematics and Introduction to Engineering
Item Factors
Engineering Mathematics Introduction to Engineering
73 0.80 0.80 74 0.67 0.70 75 0.76 0.75 78 0.82 0.83 79 0.83 0.82 80 0.76 0.74 81 0.54 0.56
% of Variance 45.5 46.7 Eigenvalue 4.10 4.20
N = 402 students Factor loadings less than 0.40 had been omitted from the table
Correlations computed using subsamples of students in Engineering Mathematics
and Introduction to Engineering to address research question 2: What are the
Level of commitment to engineering education in a polytechnic in Singapore
150
relationships between students’ perception of the learning environment, self‐
efficacy, situational interest, academic performance, and level of commitment?
Results of the analyses are shown in Table F7 and F8.
Table F7 Pearson’s Correlation – Engineering Mathematics
Scale Self‐efficacy
Triggered Situational Interest
Maintained Situational Interest
Academic Performance
Level of Commitment
Student Cohesiveness
0.23* 0.33* 0.34* 0.01 0.25*
Lecturer Support
0.23* 0.59* 0.58* 0.03 0.25*
Involvement 0.26* 0.43* 0.51* 0.05 0.26* Investigation 0.26* 0.42* 0.46* ‐0.01 0.23* Task Orientation
0.35* 0.44* 0.52* 0.09 0.41*
Cooperation 0.13* 0.30* 0.31* ‐0.03 0.26* Equity 0.28* 0.47* 0.47* 0.14* 0.38*
*p < 0.01
Table F8 Pearson’s Correlation – Introduction to Engineering
Scale Self‐
efficacy
Triggered Situational Interest
Maintained Situational Interest
Academic Performance
Level of Commitment
Student Cohesiveness 0.24* 0.25* 0.33* 0.08 0.23* Lecturer Support 0.25* 0.66* 0.63* 0.00 0.16* Involvement 0.35* 0.45* 0.55* 0.01 0.21* Investigation 0.34* 0.39* 0.52* 0.00 0.21* Task Orientation 0.41* 0.38* 0.50* 0.04 0.35* Cooperation 0.22* 0.27* 0.35* 0.04 0.24* Equity 0.27* 0.53* 0.52* ‐0.01 0.24*
*p < 0.01
Level of commitment to engineering education in a polytechnic in Singapore
151
Correlations computed using subsamples of prior schooling (O Level, ITE and foreign
students) in the Engineering Mathematics and Introduction to Engineering
classrooms to address research question 2: What are the relationships between
students’ perception of the learning environment, self‐efficacy, situational interest,
academic performance, and level of commitment?
Results of the analyses are shown in Table F9 and F10.
Table F9 Pearson’s Correlation – Engineering Mathematics
Scales Self‐
efficacy
Triggered Situational Interest
Maintained Situational Interest
Academic Performance
Level of Commitment
Students with O' Level Qualification (N = 254)
Student Cohesiveness
0.23* 0.34* 0.34* 0.06 0.24*
Lecturer Support 0.27* 0.53* 0.56* 0.07 0.36* Involvement 0.30* 0.37* 0.51* 0.18* 0.34* Investigation 0.32* 0.37* 0.50* 0.14 0.33* Task Orientation 0.37* 0.45* 0.53* 0.16* 0.46* Cooperation 0.15 0.27* 0.27* 0.06 0.26* Equity 0.31* 0.43* 0.45* 0.11 0.40*
Students with ITE Qualification (N = 78)
Student Cohesiveness
0.24 0.27 0.37* ‐0.16 0.29
Lecturer Support 0.23 0.69* 0.69* ‐0.11 0.16 Involvement 0.27 0.54* 0.56* ‐0.13 0.26 Investigation 0.33* 0.43* 0.48* ‐0.11 0.25 Task Orientation 0.18 0.44* 0.46* ‐0.12 0.34* Cooperation 0.07 0.34* 0.38* ‐0.27 0.20 Equity 0.20 0.60* 0.57* ‐0.01 0.30*
Students with Qualification in Foreign Countries (N = 70)
Student Cohesiveness
0.33* 0.37* 0.32* 0.09 0.26
Lecturer Support 0.13 0.62* 0.44* 0.15 0.06 Involvement 0.30 0.49* 0.46* 0.13 0.06 Investigation 0.17 0.55* 0.40* ‐0.16 ‐0.02 Task Orientation 0.53* 0.42* 0.59* 0.11 0.40* Cooperation 0.23 0.36* 0.37* ‐0.04 0.37* Equity 0.15 0.44* 0.32* 0.22 0.32*
*p < 0.01
Level of commitment to engineering education in a polytechnic in Singapore
152
Table F10
Pearson’s Correlation – Introduction to Engineering
Scales Self‐
efficacy
Triggered
Situational
Interest
Maintained
Situational
Interest
Academic
Performance
Level of
Commitment
O' Level (N = 254)
Student
Cohesiveness 0.23* 0.22* 0.32* 0.09 0.21*
Lecturer Support 0.21* 0.66* 0.60* 0.03 0.14
Involvement 0.32* 0.43* 0.51* 0.05 0.18*
Investigation 0.31* 0.33* 0.45* 0.07 0.26*
Task Orientation 0.43* 0.32* 0.49* 0.06 0.40*
Cooperation 0.20* 0.28* 0.34* 0.07 0.24*
Equity 0.29* 0.53* 0.49* 0.04 0.27*
ITE (N = 78)
Student
Cohesiveness 0.29 0.18 0.37* 0.10 0.36*
Lecturer Support 0.28 0.72* 0.70* 0.00 0.15
Involvement 0.47* 0.42* 0.65* 0.02 0.36*
Investigation 0.40* 0.47* 0.71* ‐0.11 0.16
Task Orientation 0.23 0.40* 0.45* ‐0.14 0.17
Cooperation 0.20 0.22 0.35* ‐0.06 0.24
Equity 0.21 0.56* 0.55* 0.01 0.17
Foreign (N = 70)
Student
Cohesiveness 0.35* 0.38* 0.31* 0.06 0.15
Lecturer Support 0.41* 0.56* 0.59* ‐0.19 0.21
Involvement 0.37* 0.48* 0.51* ‐0.19 0.07
Investigation 0.45* 0.46* 0.49* ‐0.07 0.03
Task Orientation 0.55* 0.58* 0.58* 0.02 0.34*
Cooperation 0.31 0.24 0.31* ‐0.03 0.24
Equity 0.36* 0.45* 0.58* ‐0.24 0.21
*p < 0.01
Level of commitment to engineering education in a polytechnic in Singapore
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Regression analyses using subsamples of students who are enrolled in study path A
and study path B to address research question 3: What is the joint relationship
between students’ perception of the learning environment, self‐efficacy, situational
interest, academic performance, and prior schooling as independent variables and
level of commitment as dependent variable?
Results of the analyses are shown in Table F11 to F14.
Table F11 Regression Analysis at Time 1 for Students on Study Path A
Independent Variables Level of Commitment
β
Student Cohesiveness 0.14 Lecturer Support ‐0.11 Involvement ‐0.13 Investigation 0.06 Task Orientation 0.13 Cooperation ‐0.23 Equity 0.16 Self‐efficacy 0.45* Triggered Situational Interst ‐0.16 Maintained Situational Interest 0.08 Academic Performance 0.03 O Level student ‐0.01 ITE student ‐0.03
= 0.35 F(13, 252) = 10.37*
*p < 0.01
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154
Table F12 Regression Analysis at Time 2 for Students on Study Path A
Independent Variables Level of Commitment
β
Student Cohesiveness 0.02 Lecturer Support ‐0.02 Involvement 0.05 Investigation ‐0.03 Task Orientation 0.18* Cooperation 0.04 Equity 0.22* Self‐efficacy 0.40* Triggered Situational Interst ‐0.24* Maintained Situational Interest 0.14 Academic Performance 0.04 O Level student ‐0.05 ITE student 0.02
= 0.42 F(13, 252) = 13.86*
*p < 0.01
Table F13 Regression Analysis at Time 1 for Students on Study Path B
Independent Variables Level of Commitment
β
Student Cohesiveness ‐0.05 Lecturer Support 0.04 Involvement ‐0.17 Investigation ‐0.03 Task Orientation 0.14 Cooperation 0.19 Equity 0.12 Self‐efficacy 0.45* Triggered Situational Interst ‐0.16 Maintained Situational Interest 0.13 Academic Performance 0.22 O Level student ‐0.01 ITE student ‐0.03
= 0.51 F(13, 122) = 9.71*
*p < 0.01
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Table F14 Regression Analysis at Time 2 for Students on Study Path B
Independent Variables Level of Commitment
β
Student Cohesiveness 0.00
Lecturer Support ‐0.05
Involvement 0.02
Investigation ‐0.17
Task Orientation 0.19 Cooperation 0.15 Equity 0.00 Self‐efficacy 0.41* Triggered Situational Interst 0.07 Maintained Situational Interest 0.04 Academic Performance 0.15 O Level student ‐0.08 Foreign student 0.10
= 0.43 F(13, 122) = 7.19*
*p < 0.01
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Paired sample t‐tests using subsamples of students who are enrolled in study path A
and study path B; and subsamples of prior schooling (O Level, ITE and foreign
students) in the Engineering Mathematics and Introduction to Engineering
classrooms to address research question 4: What change is there in students’ level
of commitment to engineering education between time 1 and time 2?
Results of the analyses are shown in Table F15 and F16.
Table F15 Time Differences for Students’ Level of Commitment in Study Path A and Study Path B
Subsample N Mean (SD) Mean
Difference t df p Time 1 Time 2
Study Path A 266 36.02
(5.47)
35.64
(4.97) 0.38 1.26 265 0.21
Study Path B 136 36.56
(5.18)
34.92
(5.21) 1.64 4.01 135 0.00*
* p < 0.01; SD = Standard Deviation Table F16 Prior Schooling Differences (ANOVA) for Students’ Level of Commitment in Engineering Mathematics and Introduction to Engineering
Subsample
Mean (SD)
F(2,399) p ‘O’ Level
Students
ITE
Students
Foreign
Students
Engineering
Mathematics
35.74
(4.82) 34.63 (5.50) 38.16 (4.78) 9.97 0.00*
Introduction to
engineering
35.49
(5.27) 33.72 (5.88) 38.37 (4.16) 14.97 0.00*
* p < 0.01; SD = Standard Deviation ‘O’ Level (N = 254); ITE (N = 78); Foreign (N = 70)
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Appendix G: Factor loading for Learning Environment Scale
Table G1 Factor Loadings for Learning Environment Scale – Time 1
Item
Factors
Student
Cohesive‐
ness
Lectu‐
rer
Sup‐
port
Involve‐
ment
Investiga‐
tion
Task
Orienta‐
tion
Coopera‐
tion Equity
13 0.74 14 0.71 15 0.73 16 0.78 17 0.79 18 0.64 19 0.65 20 0.62
21 0.72 22 0.79 23 0.67 24 0.73 25 0.71 26 0.76 27 0.77 28 0.70
29 0.68 30 0.71 32 0.59 33 0.53 34 0.66 35 0.55 36 0.62
37 0.73 38 0.68 39 0.78 40 0.70 41 0.82 42 0.80 43 0.81 44 0.75
45 0.73 46 0.76 47 0.68 48 0.73 49 0.68 50 0.59
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158
Item
Factors
Student
Cohesive‐
ness
Lectu‐
rer
Sup‐
port
Involve‐
ment
Investiga‐
tion
Task
Orienta‐
tion
Coopera‐
tion Equity
51 0.76 52 0.74
53 0.59 54 0.66 55 0.72 56 0.78 57 0.73 58 0.81 59 0.78 60 0.71
61 0.61 62 0.71 63 0.77 64 0.75 65 0.74 66 0.80 67 0.71 68 0.73
Eigenvalue 5.41 6.44 3.71 6.24 5.57 5.61 5.81 % of
Variance 9.84 11.7 6.74 11.4 10.1 10.2 10.6
N = 402 students Factor loadings less than 0.40 had been omitted from the table
Level of commitment to engineering education in a polytechnic in Singapore
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Table G2 Factor Loadings for Learning Environment Scale – Time 2
Item
Factors
Student Cohesive‐
ness
Lectu‐rer Sup‐port
Involve‐ment
Investiga‐tion
Task Orienta‐tion
Coopera‐tion
Equity
13 0.67 14 0.66 15 0.76 16 0.75 17 0.64 18 0.43 19 0.53 20 0.50
21 0.72 22 0.70 23 0.77 24 0.73 25 0.75 26 0.76 27 0.73 28 0.71
29 0.65 30 0.70 31 0.51 32 0.68 33 0.52 34 0.66 35 0.58 36 0.53
37 0.69 38 0.71 39 0.74 40 0.67 41 0.77 42 0.73 43 0.73 44 0.74
45 0.65 46 0.67 47 0.71 48 0.73 49 0.76 50 0.61 51 0.70 52 0.62
Level of commitment to engineering education in a polytechnic in Singapore
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Item
Factors
Student Cohesive‐
ness
Lectu‐rer Sup‐port
Involve‐ment
Investiga‐tion
Task Orienta‐tion
Coopera‐tion
Equity
53 0.68 54 0.71 55 0.70 56 0.74 57 0.72 58 0.78 59 0.77 60 0.68
61 0.67 62 0.76 63 0.78 64 0.79 65 0.79 66 0.79 67 0.73 68 0.75
Eigenvalue 4.12 5.74 4.26 5.71 4.87 5.32 5.92 % of
Variance 7.36 10.3 7.61 10.2 8.70 9.50 10.6
N = 402 students Factor loadings less than 0.40 had been omitted from the table