Level of Commitment to Engineering Education in a ......Level of commitment to engineering education in a polytechnic in Singapore 1 Chapter 1: Introduction and Overview Singapore

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

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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

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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.

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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’


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


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




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

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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

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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


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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).


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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


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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


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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.


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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).


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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.


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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


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).


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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


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


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:


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.


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


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.


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


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


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


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


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.


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


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


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


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.


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.


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


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


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


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).


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).


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)


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


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.


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


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


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


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



Self‐efficacy Students are confident to

perform well academically

Excel in your engineering

programme over the next

semester


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).


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


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.



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.


41


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


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.


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).


44

Table 7

Scale description and sample item for each scale in the Situational Interest Survey.


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


(Feeling)

the extent to which the

material itself was

enjoyable and engaging

I like what we are learning in

this module this semester

Maintained


(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


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


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.


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:


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



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:


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?





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?


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


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


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.


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.


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


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


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


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.


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



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


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%


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%


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.


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.


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


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


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




65








time 1?




time 2?









Research Question 4



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


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


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


Interest

Maintained

Situational Interest ‐

Feeling

Maintained


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


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


Interest

Maintained


Feeling

Maintained


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


% of

Variance 25.6 25.5 25.7


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‐


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


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?


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.


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


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


% of

Variance 48.6 43.8



73

4.5 Relationships between Perception of the Learning Environment, students’ Self‐Efficacy, Situational Interest, Academic Performance and Level of Commitment



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.


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


academic performance and level of commitment at time 1 with those at time 2, there

were no substantial differences.


75

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* ‐‐


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* ‐‐


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


77

Comparing the correlation coefficients between the seven factors of the learning


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



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


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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


β


Lecturer Support 0.01

Involvement 0.07


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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


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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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


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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








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Research Question 2



time 1 and time 2?

Research Question 3



independent variables and level of commitment as dependent variable at time 1 and

time 2?

Research Question 4


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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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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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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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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5.3 Discussion


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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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


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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


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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.




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


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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


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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


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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.


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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).


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


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‐


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


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?


99

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122

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




123

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


124

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.


125

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.


questions that puzzle me.


the teacher’s questions.

I carry out investigations to answer

the lecturer’s questions.

31 I find out answers to questions by

doing investigations.


126

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.


127

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.


128

Appendix C: Comparison of the Wording of the Original and Modified Version of

Situational Interest Survey


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.


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.


130

Appendix D: Comparison of the Wording and Likert Scale Type of the Original and

Modified Version of College Persistence Questionnaire


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]


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]


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.


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


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


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


136


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


137


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


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


answer questions that puzzle

me.

1 2 3 4 5


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


138


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


projects in this class. 1 2 3 4 5

57 I learn from other students in

this class. 1 2 3 4 5


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


139


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


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


answer questions as other

students.

1 2 3 4 5


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


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


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.


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


Interest

Maintained


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


N = 402 students Factor loadings less than 0.40 had been omitted from the table


144

Table F2 Factor Loadings for Situational Interest ‐ Introduction to Engineering

Item

Factor Loadings


Interest

Maintained


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




145


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?


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


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



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


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



149


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?


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


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


Correlations computed using subsamples of students in Engineering Mathematics

and Introduction to Engineering to address research question 2: What are the


150

relationships between students’ perception of the learning environment, self‐

efficacy, situational interest, academic performance, and level of commitment?


Table F7 Pearson’s Correlation – Engineering Mathematics

Scale Self‐efficacy

Triggered Situational Interest

Maintained Situational Interest


Level of Commitment


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




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


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?


Table F9 Pearson’s Correlation – Engineering Mathematics

Scales Self‐

efficacy




Level of Commitment

Students with O' Level Qualification (N = 254)


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)


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)


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


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


153

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


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


154

Table F12 Regression Analysis at Time 2 for Students on Study Path A


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


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


155

Table F14 Regression Analysis at Time 2 for Students on Study Path B


β


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


156

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?


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)


157

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


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



159

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


160

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


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