Chapter I

The Problem and Its Setting

Introduction

Have you ever tried to learn something fairly, yet failed to grasp the key ideas? Or tried to teach

people and found that some were confused by something quite basic? If so, you may have experienced a

clash of learning styles.

Learning is defined as acquiring new, or modifying and reinforcing, existing knowledge,

behaviors, skills, values or preferences and may involve synthesizing different types of information. But,

in order for you to learn best, you must first understand your learning style.

Learning Styles are a popular concept in psychology and education that are intended to identify

how people learn best and simply, the way individual processes information (Lucas,2011). It is

characterized as cognitive, effective, and physiological behaviors that serve as relatively stable

indicators of how learners perceive, interact with and respond to the learning environment. These

learning styles are considered to be one of the factors of success in higher education. Students

experiencing classroom lectures with their teachers’ teaching style match their learning style have a high

possibility of achieving an overall educational satisfaction. Therefore, it is so important for both students

and educators to have knowledge on learning style.

The study of learning styles began with the cognitive research of the mid 20th century (Rundle, 2006).

These researchers were influenced by Confucius’ famous line, “I hear and forget, I see and I remember, I do and

I understand”. ‘Till then, several studies conducted. One of those was the research Kolb‘s model, sometimes

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referred to as the Kolb Learning Style. The model gave rise to the Kolb Learning Styles Inventory, an

assessment method used to determine individual’s learning style which served as the main basis of this study.

The researchers preferred this Kolb Learning Styles Test as the determinant of the learning preference

and researchers correlate those learning style to mathematical performance of students. These learning styles

are:

Diverging - this style’s dominant learning abilities are Concrete Experience (CE) and

Reflective Observation (RO). People with this learning style are best at viewing concrete situations from many

different points of view.

Assimilating - this style’s dominant learning abilities are Abstract Conceptualization (AC) and

Reflective Observation (RO). People with this learning style are best at understanding a wide range of

information and putting into concise, logical form.

Converging - this style’s dominant learning abilities are Abstract Conceptualization (AC) and

Active Experimentation (AE). People with this learning style are best at finding practical uses for ideas and

theories.

Accommodating - this style’s dominant learning abilities are Concrete Experience (CE) and

Active Experimentation (AE). People with this learning style have the ability to learn from primarily “hands-

on” experience.

These Learning Styles would help classify the style of the students in learning Mathematics and other

subject areas.

Moreover, Mathematics contributes directly or indirectly to the most phases of man’s life. It is because

Mathematics literacy has become an absolute necessity to all possible level of education.

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The researchers conduct this research with the purpose of helping students improve their mathematics

performance through investigating the relationship of learning style to mathematical ability.

Statement of the Problem

The general aim of the study was to determine the preferred Learning Styles and Mathematics

Performance of Civil Engineering Students of Polytechnic University of the Philippines-Santa Maria

Bulacan Campus.

Specifically, the study sought answers to the following questions:

1. What is the profile of the respondents in terms of?

1.1 Gender

1.2 Year Level

2. What is the Mathematics Performance of the respondents?

3. What are the perceived learning styles of the respondents?

4. Is there a significant difference between learning styles of the respondents when they are grouped

according to year level?

5. Is there a significant difference between the Mathematics Performance when the respondents are

grouped according to their Learning Styles?

Scope and Delimitation

The study was conducted to determine the perceived learning styles and the mathematics

performance civil engineering students of Polytechnic University of the Philippines-Santa Maria

Bulacan Campus.

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The total population of third-year, fourth year, and fifth-year civil engineering students of

Polytechnic University of the Philippines-Santa Maria Bulacan Campus for academic year 2014-2015

was chosen as the respondents of the study. The topics looked into were the profile of the respondents in

terms of gender and year level, the significance difference between learning styles of the respondents

when are grouped according to year level and, the significance difference between the Mathematics

Performance when the respondents are grouped according to their Learning Styles.

Limitation of the Study

The inconsistency and dishonest responses of the respondents are beyond the control of the

researchers that might lead to the inaccuracy of the study.

Significance of the Study

This study may provide needed information to students, parents, teachers, school administrators,

government officials and researchers regarding the Learning Styles and the Mathematics Performance of

Civil Engineering Students of Polytechnic University of the Philippines-Santa Maria Bulacan Campus.

The result of the study benefits the following:

For the Students

This study will help to determine their learning styles, so that students will be able to understand

better. If students are aware of their learning styles in such subject area, they would suit themselves to

conditions and strategies from which they would learn best and thus, increase their likelihood to benefit

from and succeed in the subject area they are studying.

For the Parents

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This study will guide and enlighten the parents regarding the provisions of appropriate

educational management for their children when studying at home. They may likewise provide them

with necessary materials and guidance to maximize their learning.

For the Teacher

This study will help teachers as to what teaching styles to use, to understand the learning styles

of their students, and eventually make efforts to restructure classroom activities and structural materials

by matching their teaching styles with the learning styles of the students.

For the University Director

This study will guide the university director in preparing curricular, co-curricular, and

extracurricular activities in school that will likely provide for the learning needs of the students. The

idea of different learning styles among students will urge to provide the instructional materials and

facilities that will cater the students’ diverse learning needs. This condition will also make them support

different programs that will promote holistic development among students.

For the Government Official

This study will contribute to the awareness of the Officials to initiate comprehensive changes in

school curriculum which may be closely related to changes in learning styles that can be applied to

variety of subjects and situations. To encourage School Administrators and Teachers to let their students

choose from the options of different learning styles this is most appropriate and suitable, not only in

Mathematics but also in other subject areas.

For the Future Researchers

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This study will provide findings and information to researcher as basis in determining the

directors to take especially in the areas under study. This study would also serve as basis for related

studies in learning styles in relation to mathematics achievement to be conducted in the future

DEFINITION OF TERMS

ACCOMODATING - This term is used to identify a learning preference of an individual

who can learn best from primarily “hands-on” experience

ASSIMILATING - This term is used to identify an individual’s learning style that is best

at understanding a wide range of information and putting into concise, logical form.

CONVERGING - This term is used to identify an individual’s learning style that is

best on finding practical uses for ideas and theories.

DIVERGING - This term is used to identify an individual’s learning style that is

best at viewing concrete situations from many different points of view.

KOLB LEARNING STYLE INVENTORY - A comprehensive instrument designed and created by David

Kolb to asses individual’s preferred learning style.

LEARNING STYLES - Approaches, behaviors, specific ways to responding by which an

individual learns and interacts with the environment. It is imparted by the environment, genetics, and culture

embedded in one’s personality.

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MATHEMATICS PERFORMANCE - This refers to the grade of the previous quarter. It is a measure of the

extent of students understanding as well as retention of some mathematics concepts.

Chapter II

Review of Related Literature and Studies

Cognitive researches, theories, concepts, ideas and, studies have conducted regarding correlating

learning styles to learning outcomes and academic performances of students, in which, this chapter

presents, considering their relevance to the present study. The related literatures and studies focus on the

learning styles and mathematics performance of the students.

Foreign Literature

According to Robotham, in considering learning and how to improve student learning, one needs

to understand the way(s) in which an individual learns. It is widely accepted that while it is possible to

identify common constituent elements, the learning process varies at an individual level. Students will

develop a way or style of learning, and refine that style in response to three groups of factors: (1)

unconscious personal interventions by individual, (2) conscious interventions by learner themselves, and

(3) interventions by some other external agent.

Workrman (2012) conceptualized learning styles as a way to explain the differences between

student performance levels. Many of these learning style theories were developed by educators and

psychologists and have been widely accepted. One would be hard-pressed to find someone who has not

heard phrases such as, “He’s left brain dominant,” or “She’s right brain dominant,” as an explanation

understanding. More common Learning Style Explanations include: (1) auditory learners, (2) visual

learners, and (3) hands-on or kinesthetic learners. There exist several other styles which will be

discussed, but these are three prevalent learning style theories which are easily transferable for

understanding other theories as well.7

Benjamin Bloom (1956) developed the Bloom’s Taxonomy, which many consider to be the

foundation of the education. Bloom’s Taxonomy is a developmental model by which students evolve

through knowledge, comprehension, application, analysis, synthesis, and evaluation.

Broadly, Gardner (1983) also developed the Gardner’s Seven Knowledge Types. This theory

breaks down human learning into rather distinct areas including: Logical-Mathematical Intelligence,

linguistic Intelligence, Spatial Intelligence, Musical Intelligence, Kinesthetic Intelligence, Interpersonal

Intelligence, and Intrapersonal Intelligence.

Meanwhile, Johnson (1995) said that there are four styles related to learning mathematics. The

first has something to do with the role of discovering in meaningful learning. Accordingly, it is

important that the learner discovers things for himself. The second relates to intuition-the class of non-

rigorous ways by which mathematics speed toward solutions. The third is concerned with mathematics

as an analytic language concentrating on the translation of intuitive ideas into mathematics, and the last

refers to matter of readiness.

On the other hand, Merill (2000), has the best philosophy for using learning styles-instructional

strategies should first determined on the basis of the types of the content to be taught or the goals of the

instruction (the content-by-strategy interactions) and secondarily, learner styles and preferences are then

used to adjust or fine-tune these fundamental learning strategies. Finally, content-by-strategy

interactions take procedure over learning-style-by-strategy interactions regardless of the instructional

style or philosophy of the instructional situation.

He continued with the argument that most students are unaware of their learning styles and, if

left to their own means, they are unlikely to start learning in new ways. Thus, knowledge of one’s

learning styles can be used to increase self-awareness about their strengths and weaknesses as learners.

In other words, all the advantages claimed for metacognition (being aware of one’s own thought or

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learning processes) can be gained by encouraging learners to become knowledgeable about their own

learning and that of others.

Local Literature

Diaz and Cartnel (1999) stated that if optimal student learning is dependent on learning style,

then teacher should be aware of their differences and alter their preparation and instructional method.

Tenedero (2002) averred mentioned the learning style is one aspect of a child’s innate

uniqueness which must be learned to recognize, acknowledge and respect.

While Abrescato (1996), discussed that people who believed that individuals learned different

ways used the term learning style. The term means exactly what it says: - each person has his or her own

way of learning. If this is true then anyone who seeks to teach in the discovery based classroom needs to

be aware that there may be fundamental differences in the way in which children in the classroom

learned.

Maningding, (1992), stated that there are several reasons for the unsatisfactory achievement in

Mathematics. One of these is the wrong notion of both teachers and students that mathematics is an

activity intended only to those with high Intelligence Quotient (IQ). For this reason learning

mathematics in the classroom has become a monopoly of none, but a few.

Enriquez stated that Filipinos have their own learning styles, “The Filipino seems to be the most

effective when he is exposed to a material as a meaningful whole. He does this not according to an

inflexible and pre-conceived plan according to the most efficient combination or interaction between the

exigencies of the situation and the changing demands of the active itself. The Filipino would rather

control his schedule than allow him to become compulsive victim of an imposed structure.”

Foreign Studies

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Leong (1990), in his study on learning styles and math achievement concluded that the teachers

need to know the learning style preference of the student in class and how to work effectively with them.

The kind of activity that is appropriate to particular students may be suggested by ones particular

learning style profile. He further suggested that the teacher should boost the pupil morale and capitalize

on one’s motivation to enhance learning.

With the learning style preference of the student in mind, educational managers could make

instructional designs that could aid learning of the individuals. He suggested that instruction should be

designed in such a way that all students could have equal opportunities to develop their talents to the

fullest.

Brown B. (1984) conducted a descriptive analysis perceptual modality of learning style in older

adults to Oklahoma State University. His findings in his study indicated that the adults do utilize

perceptual elements in individual learning, that variations in perceptual modality can be measured, and

that dominant patterns of learning styles in older adults can be identified. The results also indicated that

older adult learners self-assessment of learning styles do not show positive correlation with empirical

measurements of the same styles. There were no significant differences on perceptual modality of

learning styles among older adult subgroup of age, sex, educational level, learning location and

particular administration order of measurement instrument.

Lynch conducted a study on the relationship of academic achievement, learning style, and time

preference of the eleventh and twelfth grade students identified as initial and chronic truants. Significant

findings were a.) class schedule considering students’ time preference matched with learning style or

method, b.) class schedule matched teacher assignment significantly lessened truancy and c.) time

preference was crucial factor in reversing truancy patterns. These findings prompted the

recommendations that school make necessary adjustment and arrangement in the class program or

schedule of classes to match our suit learning style considering time preference.

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Turner (1992), studied the effects of learning style prescriptions on the spelling achievement of

5th grade students. A total of 65 students in three intact groups participated in the study. The control

group received instruction and studied in a traditional manner while the instructional group received

modality based instruction determined by preferences on the learning styles inventor. The individualized

group received similar instruction and independently applied prescription information.

The aim of this study is to identify and compare the preferred learning styles of students in the

eight (8) faculties of UiTM Sarawak. This study also analyzes the similarities and differences by gender

and course achievements within and among these groups of students. Canfield Learning Styles Inventory

(CLSI) is adapted as measuring instrument in this study. The data reveal that there are significant

differences of preferences for the learning styles of Mathematics students among the 8 faculties with

respect to scales of Detail, Authority, Numeric, Qualitative, Inanimate, People, Reading, and Direct

Experience. Male students show strong preferences for scales of Instructor, Inanimate, and Direct

Experience whereas female students show strong preferences for scale of Detail, Independent,

Qualitative, and People. The scales of Organization, Numeric, A and B Expectations of the course grade

are learning styles which contribute to a positive relationship to academic achievements while scales of

Goal, Qualitative, People and D Expectation of the course grade learning styles that have a negative

relationship to academic achievements. This study also suggests that awareness of the learning styles of

students would help lecturers adopt teaching methods to enhance the learning of Mathematics students

and thus to improve students’ academic achievements.

The preceding reviews disclosed that the students’ accumulation of knowledge depends heavily

on their styles of learning.

One study which probed into the learning styles was conducted by Ferrer and Pak (1989). Their

study aimed to identify the learning styles of primary four children and their implications on

instructional procedures. The research involved 200 primary four pupils randomly selected from six

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primary schools in Penang, Malaysia. The analysis of the learning styles revealed four predominant

combinations representing personality, perception, process, and motivation dimensions.

Ferrer and Pak (1991), Totica (1990) and Tan (1995), studied learning style as a factor which

could affect mathematics learning. They asserted that there are cognitive, affective, and physiological

elements in the overt behavior of the students which may indicate how they learn best. Collectively,

there learning behaviors are called students’ learning styles.

Local Studies

Costales (2006) stated in Valesques (2007), Learning styles have a bearing on academic

performance of the students. It can increase their general average and can contribute to their motivation

to learn. She also revealed in her study the converger type of learners get higher grade that of the

diverger and accommodator.

Also, Monta (1997) stated that learning style has no significant relationship with the acadeimic

performance of student. He observed that even students use different learning styles, they are likely to

perform the same in the mathematics achievement.

In contrary, Leolette (2000) revealed that learning style has no significant relationship with

reading competence of the students. He further stated that learning styles had no direct influence on the

students test result.

Budy (1988) studied the learning styles and brain dominance of students in Tobacco National

High School. After analyzing and interpreting the study, she found out that more secondary students

perceived information by sensing and feeling rather than thinking; that he processed information by

watching rather than acting. Learning style was a stable entity by sex, mental ability, curriculum year,

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socio-economic status and qualitatively, by a function of individual differences, was the conclusion.

Different students have different learning styles. A student’s learning style is unique to himself.

In addition, Atos et al. (1994) conducted a study on the relationship of pre-conditions of learning

to learning styles preferences. The researcher used the standardized learning style inventory adopted

from Kolb and the learning style of Tenedero. The study revealed that the respondents being diverger

would learn more by using their sense of sight and imagination. They would prefer concrete experience

and reflective observations.

More so, Celestino et al. (2012) also found out that at 5% level of significance, there was an

evidence to suggest that the learning styles were related to the year level. This means that the behavioral

approaches and specific ways of the respondents on how to interact with the environment were related to

the year level or stages of learning of the respondents who are the students of Smarties Academy in Sta.

Maria, Bulacan and they also reported that again, at 5% level of significance, there was an evidence to

suggest that the Mathematics performance were related to the learning styles. This means that

mathematics performance or measure of the extent of respondents understanding as well as retention of

some mathematics concepts were related to the respondents’ behavioral approaches and specific ways

on how to interact with the environment.

Chapter III

Methods of Research and Procedures

The study was conducted to determine the relationship of mathematics performance of Civil

Engineering students of Polytechnic University of the Philippines- Sta. Maria Bulacan Campus. This

chapter covers the following: the research method used the method of collecting data and development

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of research instrument, the sampling design, and the statistical treatment. The research design is given in

this chapter.

Method of Research

The descriptive method of research was used by the researchers to easily interpret the factual

information and data to determine the learning styles of the respondents. Descriptive method of

research is used to describe characteristics of a population or phenomenon being studied. It does not

answer questions about how/when/why the characteristics occurred. Rather it addresses the "what"

question (What are the characteristics of the population or situation being studied?). The characteristics

used to describe the situation or population is usually some kind of categorical scheme also known as

descriptive categories. Since the research is about the investigation regarding the idea of individuals on

learning styles, the descriptive research suited it. The method was the advisable to use as if with this, the

researchers was able to transform quantitative information gathered and expressed this in numerical

values. The versatility of the method in terms of data interpretation, through reducing a large mass of

raw data into small and manageable form, made it appropriate, the most.

Method of Collecting Data and Development of Research Instrument

The researchers used the descriptive survey. Systematically, the researchers gave a draft of

questionnaire with the verification of the experts in the field validating questions.

Engineering Research Professor

After the verification process, the researchers went to the selected year levels. Their total

population was used as respondents of the study. There were 139 students in 3 year levels of Civil

Engineering Department.

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The respondents were given the questionnaire and analyzed their preferred learning styles. In

every question was put two possible answers that explain their perceptions on learning styles. The

respondents chose a single choice that best described them most. In every learning style, the choices

were “Very much like me” and “Not at all like me.” It would determine the style of the respondents of

the study.

All fulfilled questionnaires were retrieved, prepared, organized, and compiled for

analysis of data.

Research Instrument

In this study, the instrument used was Learning Style Inventory (LSI) developed by Kolb and a

survey questionnaire. The questionnaire was structured in a way that the respondents would able to

answer it well. The Kolb LSI addressed the preferences for learning modes.

This Learning Style Inventory determined the preferred learning styles of the respondents, in

every question was put two possible that explain their perceptions on learning styles. The respondents

chose a single choice that best described them most. “Very much like me” will be the X and “Not at all

like me” will be the Y. After getting the raw data, get the total sum of X and Y. To find out the learner

type, the one having the highest value among X and Y will be used.

X= total number of “Very much like me” answers

Y= total number of “Not at all like me” answers

Learner Type = the highest value between X and Y

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The selected students in this study accomplished a survey questionnaire about their profile and

learning style inventory to determine the preferred learning style. The results of the survey were

processed by computing the total X and Y. The calculated results were compared to data interpretation.

To determine the respondents’ performance in Mathematics, the performance rating pattern was

used and was given descriptions as follows: from (2.875-3.00) is low, (2.375-2.875) is satisfactory,

(1.875-2.375) is good, (1.375-1.875) is very good and (1.00-1.375) is described as excellent.

Sampling Design

The researchers used a kind of non-probability sampling technique known as Purposive

Sampling. It is a sampling technique in which the main goal is to focus on particular characteristics of

the population that are of interest, which will best enable the researchers to answer the research

questions. The population of students in three year levels of Civil Engineering constituted the sample of

research.

Statistical Treatment

1. Mean was used as major determinant of the mathematics performance of the respondents.

Where:

x = weighted mean

∑ x = summation of the frequency

N = total number of respondents

2. To determine the mathematics performance of the respondents, weighted mean and percentage

formula were used:

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ρ= fn

x100

Where: ρ = Percentage

ƒ = frequency of respondents

n = total number of respondents

3. Chi-square test of independence was used to determine the significant difference between learning

styles of the respondents when they are grouped according to year level.

x2=∑ (o−e)2

e

Where: o = observed frequencies

e = expected frequencies

4. The Analysis of Variance (ANOVA) was used to determine the significant difference between the

Mathematics performances when the respondents are grouped according to their learning styles.

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Chapter IV

Presentation, Interpretation and Analysis

This chapter presents the data obtained through the survey conducted, and the analysis of data using the

appropriate statistical treatment and the interpretation of the results obtained.

Problem 1: What is the profile of the respondents in terms of:

1.1 Gender

1.2 Year Level

Table 1

Distribution of Respondents by Gender

Gender Frequency Percentage

Male 76 54.68

Female 63 45.32

Total 139 100

As shown in Table 1, the number of male respondents dominate with 54.68% while female respondents with

45.32%. These numbers show a good representation because majority of Civil Engineering students are male. In addition,

males are more interested with the principles that the Civil Engineering offers as compared to females.

Table 2

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The Frequency of the Number of Civil Engineering Students when grouped

According to Year Level

Table 2 shows the number of students in 3 different year level in Civil Engineering Department.

Majority of the respondents belong to 5th year with 43.17%.

Problem 2: What is the Mathematics Performance of the students?

Table 3

The Frequency and Percentile Distribution of the Respondents on their

Mathematics Performance

Description Frequency Percent Rank

Excellent (1.00-1.375) 1 0.72 1

Very Good (1.375- 1.875) 9 6.47 3

Good (1.875-2.375) 46 33.09 4

Satisfactory (2.375-2.875) 77 55.40 5

Low (2.875-3.00) 6 4.32 2

TOTAL 139 100 Mean = 2.44

Shown in the table, majority of the students’ mathematics performance is satisfactory, which range from

2.375 – 2.875 and good, within the range of 1.875 – 2.375. The obtained overall mean of the students’

mathematics performance is 2.44, which is in the satisfactory range. It shows the performance of CE students of

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Year Level Number Percentage

3rd Year 31 22.30

4th Year 48 34.53

5th Year 60 43.17

Total 139 100

PUPSMB in mathematics is satisfactory. Only 0.72% of the respondents got an excellent performance and

4.32% got a low performance.

Problem 3: What are the perceived learning styles of the respondents?

Table 4

Percentile Distribution of the perceived Learning Styles of the Respondents

Very much like me Not at all like me

Diverging 51.71% 47.57%

Assimilating 39.48% 59.80%

Converging 47.75% 51.53%

Accommodating 38.40% 60.88%

The table shows that the perceived learning style of the students is diverging. 51.71% of the respondents

said that they have that learning style, while 47.57% said that they don’t have that type of learning style.

Meaning, most of the students are best at viewing concrete situations from many different points of view.

Second one is the converging which got 47.75%, and assimilating which got 39.48%. The accommodating got

the lowest percentage among all the learning styles, having 38.40%, which is the ability to learn from primarily

hands-on experience.

Problem 4: Is there a significant difference between learning styles of the respondents when they are

grouped according to year level?

Table 5

Significant Difference between Learning Styles According to Year Level Using Chi-Square

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  Chi-Square StatisticTextual

Interpretation

Learning Styles 6.18 Not Significant

Output: Learning styles (at 0.05 level of significance)

Critical Value: If P <12.59 Difference is not significant

Table 5 shows a significant difference between learning styles of the respondents when they are

grouped according to year level using Chi-square. The computed chi-square value was 6.18 which

greater than the critical value of 12.59 which implied that there was no significant difference between

learning styles of the respondents when they are grouped according to year level.

At 5% level of significance, there was an evidence to suggest that the learning styles were not

related to the year level. This means that the behavioral approaches and specific ways of the respondents

on how to interact with the environment were not related to the year level or stages of learning of the

respondents.

Table 6

Significant Difference between the Mathematics Performances When the Respondents are grouped according to their Learning styles using Analysis of Variance (ANOVA)

  Computed F-valueTextual

Interpretation

Learning Styles 2.96 Significant

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Output: Mathematics Performance (at 0.05 level of significance)

Critical Value: If f ≥ 2.67 Difference is significant

Table 6 shows the significant difference between the mathematics performances when the

respondents are grouped according to their learning styles using Analysis of variance (ANOVA). The

computed F value was 2.962535 which were greater than the critical value of ≥ 2.671676 which implied

that there was significant difference between the mathematics performances when the respondents are

grouped according to their learning styles.

At 5% level of significance, there was an evidence to suggest that the Mathematics Performance

were related to the learning styles. This means that Mathematics Performance or measure of the extent

of respondents understanding as well as retention of some Mathematics concepts were related to the

respondents’ behavioral approaches and specific ways on how to interact with the environment.

Chapter 5

Summary, Conclusions, and Recommendations

This chapter presents the summary of findings of the study, the conclusions drawn, and the

recommendations made.

Summary

The aim of this study was to determine the perceived learning styles and Mathematics

performance of the Civil Engineering students of Polytechnic University of the Philippines Santa Maria

Campus. The descriptive method was used in the study and the normative survey method for gathering

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data. A questionnaire was used as the research instrument of the study. The total population of the

students was chosen to be the respondents of the study with the aid of Purposive sampling technique.

The research was conducted during the school year 2014-2015.

Problem no. 1

What is the profile the respondents in terms of?

1.1 Gender

Based on the result in distribution of respondents in terms of gender showing the percentages of

the respondents according to their gender, majority of the selected samples are males.

1.2 Year Level

Based on the result in distribution of respondents in terms of year level showing the percentages

of the respondents according to their year level, majority of the respondents belong to 5th year.

Problem no. 2

What is the Mathematics Performance of the respondents for the school year 2013-2014?

The mean rating of the respondents in Mathematics is 2.44 which implied that the respondents’

performance in Mathematics is satisfactory.

Problem no. 3

What are the perceived learning styles of the respondents?

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The Learning Style Inventory showed that 51.71% of the respondents are diverging, 47.75% of

the respondents are converging, and 39.48% of the respondents are assimilating and 38.40% of the

respondents are accommodating. Most learning style of the respondents was described as diverging.

Problem no. 4

Is there a significant difference between learning styles of the respondents when they are

grouped according to year level?

The computed chi-square value of 6.18 and at 5% level of significance revealed that there is no

significant difference between learning styles of the respondents and their year level. On how they

understand a topic is related to their stages of learning.

Problem no. 5

Is there a significant difference between Mathematics performances of the respondents

when they are grouped according to their learning styles?

The computed F value using Analysis of Variance (ANOVA) of 2.962535 and at 5% level of

significance that revealed the there is significant difference between Mathematics performances of the

respondents when they are grouped according to their learning styles. Their academic achievements are

related to their learning styles.

Conclusions

Based on the above findings of the study, the following conclusions were drawn:

1. Majority of the respondents were males and most of the samples belong to 5th year.

2. The respondents showed a satisfactory performance in Mathematics.

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3. The perceived learning style of the respondents is diverging. It showed that most of the respondents

are best at viewing concrete situations from many different points of view.

4. There was no significant difference between learning styles and year level of the respondents.

5. There was significant difference between the grades of the respondents when they are grouped

according to their learning styles.

Recommendations:

In the light of the aforementioned results and conclusions, the following are deemed priority

recommendations to further improve the Mathematics performances of the Civil Engineering students,

1. Learners should identify their own learning styles so that they could help themselves to find ways to

improve their understanding on the lessons easily not only in Mathematics but also in other subject

areas.

2. Parents should be knowledgeable of their children’s learning styles so they could motivate their

children to study their lessons in their own learning way and so they could design the study place of

their children on their homes according on how they could learn best.

3. School administrators should give motivating activities and programs which are suitable for the

teachers and students skills and knowledge.

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Appendix A

Survey Questionnaires

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“Relationship of Academic Performance of Civil Engineering Students of Polytechnic University of the Philippines – Sta. Maria Campus in Mathematics with their Learning

Styles”

Name (Optional): ______________________________

Gender : ( ) Male ( ) Female

Year Level : ______________________

Weighted Grades on College Algebra : _______Plane and Spherical Trigonometry : _______Analytic Geometry : _______Differential Calculus : _______Integral Calculus : _______

Learning Styles Inventory

Direction: Put a check mark ( ⁄ ) on the space provided that most closely describes you.

Diverging

Very much like me Not at all like me1. I am good at picking up hints and techniques from other people.2. I am practical and down to earth. 3. I like realistic, but flexible plans. 4. I try things out by practicing to see if they work.5. I am thorough and methodical.6. I enjoy watching people.7. I am careful and cautious.8. I investigate a new topic or process in depth before trying it.

27

Assimilating

Very much like me Not at all like me1. I ask probing questions when learning a new subject. 2. I am rational and logical.3. I plan events down to the last detail.4. I like to know the right answers before trying something new. 5. I draw up lists of possible courses of actions when starting a new project.6. I like to read and observe. 7. I am quiet and somewhat shy. 8. I make cautious and logical decisions.

Converging

Very much like me Not at all like me1. I analyze reports to find the basic assumptions and inconsistencies. 2. I prefer working alone.3. Others would describe me as serious, reserved, and formal.4. I use facts to make decisions.5. I often produce off-the-cuff ideas that at first might seem silly or half-baked.6. I am normally the one who initiates conversations.7. I am flexible and open-minded.8. I like to try new and different things without too much preparation.

Accommodating

Very much like me Not at all like me1. I rely upon others to give me the basic gist of reports.2. I enjoy working with others.3. Others would describe me as verbal, expressive, and informal.4. I use feelings to make decisions.5. I am happy to have a go at new things.6. I like to get involved and to participate.7. I am loud and outgoing.8. I make quick and bold decisions.

28

Appendix B

Computations

29

Control No.

Year Level Gender

College Algebra

Trigonometry

Analytic Geometry

Differential Calculus

Integral Calculus

1 3 M 2.5 2.5 2.5 2.5 2.25 2.43752 3 M 2 2.5 2.25 2.75 2.75 2.53 3 M 2.75 2.75 2 2.75 1.75 2.40634 3 M 2.25 2.25 2.25 2.75 2.75 2.55 3 M 1.5 1.75 1.75 2.5 2.75 2.14066 3 M 2.5 1.75 2.75 3 2.5 2.51567 3 M 2 1.75 2 2.25 2.5 2.14068 3 M 2 2 1.75 1.75 1.75 1.84389 3 M 2.25 2.25 2.25 3 2.5 2.5

10 3 M 2 2 2 2.5 2.5 2.2511 3 M 2 2.25 2 2.5 2.5 2.296912 3 M 2.25 2.25 2 2.75 2.25 2.343813 3 M 2 2 2 2.75 3 2.437514 4 M 2.5 3 2.5 2.75 2.75 2.718815 4 M 3 3 3 3 2.75 2.937516 4 M 3 3 2.75 2.5 2.5 2.718817 4 M 3 3 2 2.5 2.5 2.62518 4 M 2.75 2.5 3 2.75 2.75 2.734419 4 M 3 3 2.25 2.75 2.75 2.781320 4 M 2.5 2.75 2.5 3 2.5 2.671921 4 M 2.25 2.75 2.5 2.5 2.5 2.522 4 M 1.75 1.5 1.5 2 1.25 1.609423 4 M 3 2.75 3 2.75 2.5 2.765624 4 M 2 2.25 2.5 3 2.75 2.546925 4 M 2.5 2.5 2.5 2.75 2.5 2.562526 4 M 1.5 2.75 2 3 2.5 2.421927 4 M 2.75 2 3 3 2.5 2.640628 4 M 1.5 2 2 2 3 2.156329 4 M 2.75 2.5 2.5 2.5 2.5 2.546930 4 M 3 3 2 1.75 3 2.562531 4 M 2 3 2.5 2.5 2.5 2.532 4 M 1.75 1.75 2.5 2.25 2.25 2.093833 4 M 3 3 3 3 3 334 4 M 1.5 1.5 1.25 2.25 2.75 1.968835 4 M 2 2 2 3 3 2.536 4 M 2.25 2 2.5 2.25 2.75 2.359437 4 M 2.75 2.5 2.25 2.25 2 2.328138 4 M 3 3 2.25 2 2.75 2.593839 4 M 2.75 2 2.75 2.5 2.5 2.484440 4 M 2.25 2.25 2.5 2.5 2 2.281341 5 M 2.75 2.75 2.75 2.75 2.75 2.7542 5 M 2.75 2.75 2.75 2.75 2.75 2.7543 5 M 3 3 3 3 2.25 2.812544 5 M 2.75 2 3 3 2 2.5156

30

45 5 M 2.5 2.5 2.5 3 2.75 2.687546 5 M 2.75 3 2.75 3 2.5 2.796947 5 M 3 3 3 3 3 348 5 M 2 2 2 3 3 2.549 5 M 2.5 2 2.5 2 2 2.156350 5 M 1.75 1.75 2.5 3 2 2.218851 5 M 2 1.5 2.75 3 2.25 2.312552 5 M 2 1.5 3 2.25 2 2.093853 5 M 2.25 2.5 3 2.25 2.25 2.390654 5 M 2.75 1.5 2.75 2.5 2.5 2.390655 5 M 2.75 2 2.5 2.25 2.25 2.328156 5 M 2.25 1.75 2.75 3 2.5 2.468857 5 M 3 2 2.75 2 2.75 2.468858 5 M 2.25 1.75 2.25 3 2.5 2.406359 5 M 3 2.75 2.5 3 2.75 2.828160 5 M 1.5 2.25 1.25 3 2.25 2.171961 5 M 2.75 2 2.25 2.5 2 2.296962 5 M 1.75 1.75 2.5 2.5 2.25 2.156363 5 M 1.5 1.25 2.5 2.75 2.25 2.078164 5 M 1.25 1.5 2 2 1.75 1.703165 5 M 2 2.5 2.75 2.75 1.75 2.312566 5 M 3 2.25 3 1.5 2.25 2.296967 5 M 3 2.5 2.75 2.5 2.5 2.62568 5 M 3 3 3 3 3 369 5 M 2.75 2 2.75 2 2.5 2.359470 5 M 1.75 1.5 2.75 2.75 2 2.140671 5 M 2.5 2 2 3 1.5 2.218872 5 M 1.5 1.5 1.75 1.25 1.75 1.531373 5 M 2 3 2.25 2 2.5 2.343874 5 M 1.75 2.5 2.25 3 3 2.578175 5 M 1.75 1.25 1.5 1.75 1.5 1.562576 5 M 2.5 2 3 3 2.5 2.5938

2.418

31

Control No.

Year Level Gender

College Algebra Trigonometry

Analytic Geometry

Differential Calculus

Integral Calculus

1 3 F 2.25 2.5 2.5 3 2.5 2.578132 3 F 2.25 2.75 2.25 3 2 2.468753 3 F 1.25 2 1.25 1.25 1 1.328134 3 F 2 2 2.5 2.75 2.75 2.43755 3 F 1.25 1.75 1.75 2.75 3 2.218756 3 F 1.5 2.25 2 2.25 2.25 2.078137 3 F 2 1.75 1.5 2.5 2.25 2.078138 3 F 2 2 2 3 2.75 2.43759 3 F 2.25 2.25 2 0 0 1.09375

10 3 F 1.5 2.75 2.25 2.75 2.25 2.3281311 3 F 1.75 2.25 2 2.5 2.75 2.312512 3 F 2 2.25 2.25 2.5 2.5 2.3281313 3 F 2 2 1.75 3 2.5 2.3437514 3 F 2.5 2.75 2.5 3 2.5 2.6718815 3 F 2 2.5 3 3 3 2.7187516 3 F 2.5 2.25 2.5 3 2.75 2.6406317 3 F 2 1.5 2.5 3 2.75 2.2518 3 F 2 2 2 2.5 2.5 2.2519 3 F 2.5 2.75 2.5 2 2 2.2968820 5 F 2.5 1.5 2.5 2.5 2 2.187521 5 F 2.75 2.25 2.25 2.25 2.25 2.3437522 5 F 2.5 1.25 2.5 2 2 2.0156323 5 F 2.5 2.5 2.5 3 2.75 2.687524 5 F 2 2 2 3 3 2.525 5 F 3 1.5 2.75 2.5 1.75 2.2526 5 F 3 2 2 2.25 2 2.2527 5 F 2 1.5 2 2 2 1.9062528 5 F 2 3 3 3 3 2.812529 5 F 2.75 1.75 2.5 3 2.25 2.4687530 5 F 2.5 2.25 2.5 3 2.5 2.5781331 5 F 1 1.25 2 2 2 1.6718832 5 F 2.25 2 3 2.5 2 2.2968833 5 F 1.25 1.5 1.5 2.25 2 1.7656334 5 F 2.75 2.5 2.75 3 2.5 2.7031335 5 F 2.5 1.5 2.5 2.25 2 2.12536 5 F 2.25 2.75 3 2.75 2.25 2.562537 5 F 2.25 1.75 3 3 2 2.37538 5 F 2.5 1.5 2.75 2.75 2.5 2.4062539 5 F 1.5 1.25 3 2.75 2.5 2.2031340 5 F 2 2 2.25 2.5 2.5 2.2812541 5 F 1.75 1.75 3 2.5 2.5 2.2812542 4 F 2.75 2.75 2 2 2 2.2812543 4 F 3 3 2 2.25 2.5 2.562544 4 F 2.75 3 2.75 3 3 2.92188

32

45 4 F 2.5 3 2.75 2.5 2.5 2.62546 4 F 2 1.75 2 2.75 2.5 2.2656347 4 F 1.75 2.25 2.25 2.75 2.75 2.4062548 4 F 3 3 3 3 2.5 2.87549 4 F 3 3 2.25 2.5 2.75 2.7187550 4 F 3 3 2.75 3 2.25 2.7812551 4 F 1.5 1.25 1.75 2 2.25 1.7968852 4 F 3 2.5 2.75 2.5 2.25 2.562553 4 F 2.25 2.5 2.75 2.5 3 2.6093854 4 F 2.5 2.75 3 3 3 2.8593855 4 F 2.75 2.75 2.5 2.25 2 2.4062556 4 F 2.75 2.5 2.5 2.5 3 2.6718857 4 F 2.25 2.75 2.5 3 2.5 2.62558 4 F 3 2.75 2.75 2.25 3 2.7343859 4 F 2.75 2.5 2.5 2.5 3 2.6718860 4 F 2 2.25 2.75 3 2.5 2.5156361 4 F 3 2.5 2.5 2 2.25 2.4062562 4 F 2.75 2.25 3 2.5 2.75 2.62563 4 F 2.25 2.75 2.5 3 3 2.75

Mean 2.38418

33

Ctrl No.

Diverging AssimilatingVery much like

meNot at all like

meVery much like

meNot at all like

me1 5 3 2 62 2 6 1 73 6 2 3 54 3 5 3 55 4 4 3 56 5 3 2 67 6 2 3 58 2 6 6 29 5 3 2 6

10 2 6 6 211 7 1 1 712 2 6 3 513 5 3 2 614 6 2 3 515 5 3 3 516 4 4 6 217 2 6 6 218 8 0 7 119 2 6 6 220 2 6 3 521 6 2 3 522 6 2 3 523 4 4 2 624 3 5 5 325 3 5 2 626 3 5 5 327 5 3 3 528 5 3 3 529 7 1 4 430 2 6 5 331 6 2 3 5% 53.62903226 46.37096774 43.9516129 56.0483871

Sum 133 115 109 139

32 7 1 1 733 2 6 3 534 4 4 5 335 2 6 6 236 7 1 2 637 3 5 5 338 5 3 5 339 3 5 2 640 1 7 2 641 7 1 2 642 7 1 0 843 0 8 1 744 5 3 6 245 3 5 5 346 7 1 2 6

Converging Accommodating

Very much like me Not at all like meVery much like

meNot at all like

me5 3 4 44 4 2 68 0 3 55 3 6 25 3 2 66 2 1 77 1 5 38 0 7 15 3 3 55 3 4 46 2 3 56 2 1 74 4 2 63 5 2 62 6 2 63 5 5 35 3 1 73 5 2 63 5 4 46 2 1 75 3 2 66 2 1 75 3 1 72 6 2 66 2 5 32 6 2 62 6 2 64 4 1 73 5 2 67 1 2 63 5 2 6

58.06451613 41.93548387 33.06451613 66.93548387144 104 82 166

2 6 3 57 1 2 63 5 6 23 5 1 75 3 3 57 1 2 63 5 2 62 6 5 36 2 5 37 1 2 65 3 5 36 2 5 32 6 1 72 6 2 63 5 3 5

34

Summary Statistics for Analysis of Variance (ANOVA)

35

Diverging Assimilating Accommodating Converging Total

sum of x 54.4757 135.4675 53.109375 92.94075 335.99333

sum of x2 131.693575 344.93355 129.73169 219.6925879 826.0514

n 23 54 22 40 139

mean2.368508696 2.508657407 2.4140625 2.32351875

SS2.667405848 5.092002662 1.522339414 3.74301264

Summary Table for Analysis of Variance (ANOVA)

36

Source df SS Mf F

between 30.857473674 0.285824558 2.96253548

within 13513.02476056 0.096479708

 

total 13813.88223423

   

JOHN PAUL D. CATALAN344 Pascual St., Bagbaguin, Sta. Maria, Bulacan Contact: 0935-126-6352E-mail address: johnpaulcatalan18@yahoo.com

PERSONAL INFORMATION:

Nickname : PopoyGender : MaleAge : 19Civil Status : SingleDate of Birth : June 18, 1995Height : 5’7”Weight : 55 kgs.Religion : Roman Catholic

SKILLS: Able to opearate the Microsoft Office applications. Knowledgeable in AutoCAD and Estimates

EDUCATIONAL BACKGROUND:

COLLEGE POLYTECHNIC UNIVERSITY OF THE PHILIPPINES – STA. MARIA CAMPUS

Pulong Buhangin, Sta. Maria, Bulacan3rd Year College - Bachelor of Science in Civil Engineering, 2012-PRESENT

HIGH SCHOOL STA. MARIA AGRO-INDUSTRIAL HIGH SCHOOL

Bagbaguin, Sta. Maria, Bulacan2008-2012

ELEMENTARY: BAGBAGUIN ELEMENTARY SCHOOL

Bagbaguin, Sta. Maria, Bulacan

37

JASON LAREZA 574 Fortunato F. Halili Ave. St., Bagbaguin, Sta. Maria, BulacanContact: 0936-740-8031E-mail address: lareza_jason25@yahoo.com

PERSONAL INFORMATION:

Gender : MaleAge : 18Civil Status : SingleDate of Birth : December 25, 1995Height : 5’4”Weight : 55 kgs.Religion : Roman Catholic

SKILLS: Able to operate the Microsoft Office applications. Knowledgeable in AutoCAD and Estimates

EDUCATIONAL BACKGROUND:

COLLEGE POLYTECHNIC UNIVERSITY OF THE PHILIPPINES – STA. MARIA CAMPUS

Pulong Buhangin, Sta. Maria, Bulacan3rd Year College - Bachelor of Science in Civil Engineering, 2012-PRESENT

HIGH SCHOOL EARLY CHRISTIAN SCHOOL

Poblacion, Sta. Maria, Bulacan

ELEMENTARY: BAGBAGUIN ELEMENTARY SCHOOL

Bagbaguin, Sta. Maria, Bulacan2002-2008

38

ACEZON S. JOAQUIN Sitio Perez, Pulong Buhangin, Sta. Maria, BulacanContact: 0916-5044-830E-mail address: acezon.joaquin@yahoo.com

PERSONAL INFORMATION:

Gender : MaleAge : 19Civil Status : SingleDate of Birth : June 3, 1995Height : 5’9”Weight : 60 kgs.Religion : Roman Catholic

SKILLS: Able to operate the Microsoft Office applications. Knowledgeable in AutoCAD and Estimates

EDUCATIONAL BACKGROUND:

COLLEGE POLYTECHNIC UNIVERSITY OF THE PHILIPPINES – STA. MARIA CAMPUS

Pulong Buhangin, Sta. Maria, Bulacan3rd Year College - Bachelor of Science in Civil Engineering, 2012-PRESENT

HIGH SCHOOL NORZAGARAY NATIONAL HIGH SCHOOL

Poblacion, Norzagaray, Bulacan

ELEMENTARY: KANYAKAN ELEMENTARY SCHOOL

Matictic, Norzagaray, Bulacan2002-2008

39

JOHN MICHAEL B. LAPIG#1710 Mapayapa St,Sta. Cruz Village, Sta. Maria, Bulacan Contact: 0906-817-7104E-mail address: xbind24@gmail.com

PERSONAL INFORMATION:

Gender : MaleAge : 18Civil Status : SingleDate of Birth : October 12, 1995Height : 5’10”Weight : 55 kgs.Religion : Roman Catholic

SKILLS: Able to operate the Microsoft Office applications. Knowledgeable in AutoCAD and Estimates

EDUCATIONAL BACKGROUND:

COLLEGE POLYTECHNIC UNIVERSITY OF THE PHILIPPINES – STA. MARIA CAMPUS

Pulong Buhangin, Sta. Maria, Bulacan3rd Year College - Bachelor of Science in Civil Engineering, 2012-PRESENT

HIGH SCHOOL SACRED HEART ACADEMY

Poblacion, Sta. Maria, Bulacan

ELEMENTARY: STA. MARIA ELEMENTARY SCHOOL

Poblacion, Sta. Maria, Bulacan2002-2008

40

STEVEN E. DAILO Dr. Teofilo St., Poblacion, Sta. Maria, BulacanContact: 0935-810-7140E-mail address: dailosteven@yahoo.com

PERSONAL INFORMATION:

Gender : MaleAge : 18Civil Status : SingleDate of Birth : June 18, 1995Height : 5’6”Weight : 53 kgs.Religion : Roman Catholic

SKILLS: Able to operate the Microsoft Office applications. Knowledgeable in AutoCAD and Estimates

EDUCATIONAL BACKGROUND:

COLLEGE POLYTECHNIC UNIVERSITY OF THE PHILIPPINES – STA. MARIA CAMPUS

Pulong Buhangin, Sta. Maria, Bulacan3rd Year College - Bachelor of Science in Civil Engineering, 2012-PRESENT

HIGH SCHOOL SACRED HEART ACADEMY

Poblacion, Sta. Maria, Bulacan

ELEMENTARY: STA. MARIA ELEMENTARY SCHOOL

Poblacion, Sta. Maria, Bulacan2002-2008

41