Methodological triangulation as a research strategy in educational innovation processes: case study of

the b-learning methodology in the university context

Dra.MªCruzSánchezGómez(mcsago@usal.es)Dra.AnaIglesiasRodríguez(anaiglesias@usal.es)Dr.AntonioV.MarCnGarcía(avmg@usal.es)

Facultad de Educación Universidad de Salamanca

INTRODUCTION Triangulation is a research strategy to validate the information using, on the one hand, quantitative analysis methods; and on the other hand, qualitative analysis methods and prove that both methods lead to the same results without making mistakes of interpretation. This process is called ‘multi-method approach’. And It has been used in the research that we are presenting here.

TRIANGULATION

Quantitative

Qualitative

MULTI-METHOD APPROACH

OBJECTIVES

General Objective: !  Identify models of adoption of info-communications

technology in university teaching.

Specific Objectives: !  Identify and define the determinants that influence the intention

of implementation of b-learning of info-communications and technology university professors.

!  Establish a theoretical model in which the relationship between the variables involved (intention, attitude toward the behavior, subjective norm, conductural perceived control, perceived behavioral beliefs, normative beliefs and beliefs of control) and its effect on the intended use of defined b-learning.

!  Analyze phases of adoption.

TECHNOLOGY ACCEPTANCE MODEL (TAM)

But above all It suggests that there is a causal relationship between •  the Perceived Usefulness Use this methodology and technology will help to improve his or her academic and professional performance; • And Perceived Ease of Use The use of the technology will entail less effort on his or her part

INTENTION TO USE

TAM Model give great importance to two aspects:

Attitude toward Behavior and

Subject Standard (TAM2, TAM3)

DESIGN: TAM theoretical model proposed for adoption innovation

In our proposal, we evaluate, using a multimethod approach, university teachers’ intention to use b-learning.

To that end, we have analyzed different variables such as:

Social Image (Proffesional)

Perceived Usefulness

Social Standard

Facilitating Conditions

Perceived Ease of Use

Intended Use b-Learning

Use

H7 H4

H5

H1

H2

H6 H3

METHODOLOGY

Our study used a non-experimental design or ex-post-facto because we study natural groups and teachers from the University of Castile and León.

In particular, we have made a descriptive study to reach a comparative-causal model that seeks to discover relationashisps between variable cause and effect.

DESIGN

Sociodemographic Variables and

Identification data

•  Gender •  Age •  Professional category

•  University where he/she teaches •  Field of Knowledge •  Taught courses Ph.D.

•  Teaching experience •  Directors and/or management

charges

Variables of the Model proposed

•  Behavioral intention, •  Attitude toward behavior, •  Perceived behavioral control, •  Subject Standard, •  Behavioral beliefs •  Control beliefs •  Facilitators and barriers •  Perceived Usefulness •  Perceived Ease of Use

VARIABLES INCLUDED IN THIS STUDY

Design of specific scales (likert type)

• Ficha de registro de uso de TI (b-learning). • Escala de medición de la Intención Conductual; • Escala de medición de Actitudes hacia la Conducta; • Escala de medición del Control Conductual Percibido; • Escala de medición de la Norma Subjetiva; • Escala de medición de las Creencias Conductuales Percibidas; • Escala de medición de las Creencias Normativas; • Escala de medición de las Creencias de Control, C. Facilitadoras

• Escala de medición de Utilidad Percibida • Escala de medición de Facilidad de Uso Percibido • Escala de medición de Imagen Social • Escala de medición de Compatibilidad

VARIABLES INCLUDED IN THIS STUDY

All of them have been measured with likert-type scales.

SAMPLING PROCEDURE AND SAMPLE CHARACTERISTICS

To carry out data collection, we have made a sampling stratified by clusters, with the primary units of sampling (universities) and secondary units (facultes) selected on a porportional and random routes and sex quotas.

The strata were formed based on the following five areas of knowledge: Art and Humanities Sciences; Health Sciences; Social Sciencies and Law; and Architecture and Engineering.

The sample size was 445 practising university teachers from public Universities in the Castile and León (Salamanca, Valladolid, Burgos and León).

Questionnaires were applied through a personal interview in the teacher’s offices. On the other hand, we did 56 expended interviews for qualitative analysis.

In the quantitative data analysis,

!  First of all, we use a descriptive analysis.

!  Secondly, we carry out Confirmatory Factor Analysis (AFC)

•  To e n s u r e i n t e r n a l c o n s i s t e n c y a n d t h e unidimensionality of the scales.

!  And in third place, we did a causal analysis

•  In accordance with the structural equation modelling (SEM) approach with parameter stimation using the maximum likelihoud (ML) method. For these purposes, we used (version 16.0) of the AMOS statistical package.

Quantitative data

Results quantitative data

The results show that teachers attach great importance to variables such as social image, social norm and perceived uselfulness. In other words, teachers’ intention is determinated by two factors: on the one hand, the subject’s perception of the uselfulness that this innovation can have in improving his or her teaching practice. And, on the other, the existence of favourable conditions (the human and technical resources and equipament available at the university).

This result is very interesting because it shows that in a highly professionalized context such as the university the perception of effort t h a t c a n c o m e w i t h t h e introduction of an innovation does not play a significant conditioning role at the home of assuming or incorporating use of that new teaching element.

Phas

es o

f th

e in

nova

tion

-de

cisi

on M

odel

Knowledge

Attitude

Decision

Aplication of the innovation

Integration

Qualitative analysis

In the same way, if we analyze the teachers’ discourse, we can see that we get the same relevant ideas as those described previously in the quantitative analysis. To this end, we present the content according to the phases described in Rogers’s innovation decision model:

• Among teachers who are training themselves in b-learning, almost half are teachers in the social sciences and humanities, compared to nearly a third who are in “Sciences”

Category 1 KNOWLEDGE

(Learning about the b-learning innovation)

•  The majority of those who have undergone training in it have a positive attitude towards b-learning methodology.

Category 2 ATTITUDE

(configuration of a positive or negative attitude towards the

b-learning innovation)

• The majority of the teachers interviewed have the intention to apply b-learning in a short space of time; very few said that they do not. Here also a main cause of motivation appears to be training in b-learning received.

• Differences are established according to “sex”. Female teachers expressed to a greater extent an intention to use b-learning relative to male teachers.

Category 3 DECISION

(intention to use b-learning)

Qualitative analysis

• A high number of teachers stated that they had undertaken a teaching activity in which in-person class activities were combined with online activities and viceversa.

Category 4 APPLICATION of the

innovation (regular use of b-learning)

• Almost the half of the teachers have advanced proficiency in b-learning that allows them to execute a creative use of it and apply it in any type of teaching context.

• The reason that best classifies subjects in this phase is “prior training in b-learning”

Category 5 INTEGRATION

(creative application and generalization to different

contexts)

Qualitative analysis

CONCLUSIONS !  Both the qualitative and quantitative methodology have shown that

performance expectancy is the most powerful category with regard to intention to use the b-learning system among university teachers.

!  The most part intention to use b-learning (IU) comes about through teachers’ positive perception with regard to the academic results than can be obtained when this methodology is applied in their classes.

!  In contrast, the effect of the Effort Expectancy construct is notably weak or ineffective in relation to intention to use b-learning, both in the general model and as analysed in the different subgroups according to individual characteristics. It is very likely that the type and profile of the sample (university teachers who can be assumed to have a high level of specialization and training, with appropiriate chances of having access and knowledge related to the use of technology applied to teaching innovation) explain the absence of efeect of this factor.

Methodological triangulation as a research strategy in educational innovation processes: case study of

the b-learning methodology in the university context

Dra.MªCruzSánchezGómez(mcsago@usal.es)Dra.AnaIglesiasRodríguez(anaiglesias@usal.es)Dr.AntonioV.MarCnGarcía(avmg@usal.es)

Facultad de Educación Universidad de Salamanca