Predictors of attitudes to e-learning of Australian health care students

University of GlamorganCardiff • Pontypridd • Caerdydd

PREDICTORS OF ATTITUDES TO E-LEARNING OF AUSTRALIANHEALTH SCIENCE STUDENTS

Ted Brown, Brett Williams, Shapour Jaberzadeh, Louis Roller,Claire Palermo, Lisa McKenna, Caroline Wright, Marilyn Baird,Michal Schneider-Kolsky, Lesley Hewitt, Tangerine HoltMonash University

Maryam ZoghiUniversity of Melbourne

Jenny SimRMIT University

For author biographies, please refer to page 76.

Correspondence to:Ted BrownDepartment of Occupational TherapyFaculty of Medicine, Nursing and Health SciencesMonash University (Peninsula Campus)Frankston, [email protected]

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • pp59–76January 2010

© University of Glamorgan 2010ISSN: 1758-1184

Journal website: http://jarhe.research.glam.ac.ukJournal correspondence to: [email protected]

Ted Brown et al

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 60

PREDICTORS OF ATTITUDES TO E-LEARNING OF AUSTRALIANHEALTH SCIENCE STUDENTS

Ted Brown, Brett Williams, Shapour Jaberzadeh, Louis Roller,Claire Palermo, Lisa McKenna, Caroline Wright, Marilyn Baird,Michal Schneider-Kolsky, Lesley Hewitt, Tangerine HoltMonash University

Maryam ZoghiUniversity of Melbourne

Jenny SimRMIT University

Abstract

COMPUTERS and computer-assisted instruction are being used with increasing frequency in the area ofhealth science student education, yet students’ attitudes towards the use of e-learning technology andcomputer-assisted instruction have received limited attention to date. The purpose of this study was toinvestigate the significant predictors of health science students’ attitudes towards e-learning and com-puter-assisted instruction. All students enrolled in health science programmes (n=2885) at a largemulti-campus Australian university in 2006-2007, were asked to complete a questionnaire. This includedthe Online Learning Environment Survey (OLES), the Computer Attitude Survey (CAS), and the AttitudeToward Computer-Assisted Instruction Semantic Differential Scale (ATCAISDS). A multiple linear regres-sion analysis was used to determine the significant predictors of health science students’ attitudes toe-learning. The Attitude Toward Computers in General (CASg) and the Attitude Toward Computers inEducation (CASe) subscales from the CAS were the dependent (criterion) variables for the regression analy-sis. A total of 822 usable questionnaires were returned, accounting for a 29.5% response rate. Threesignificant predictors of CASg and five significant predictors of CASe were found. Respondents’ age and OLES

Equity were found to be predictors on both CAS scales. Health science educators need to take the age ofstudents and the extent to which students perceive that they are treated equally by ateacher/tutor/instructor (equity) into consideration when looking at determinants of students’ attitudestowards e-learning and technology.

Key words: Health science students, technology, teaching, professional education.

Introduction

THE USE of technology, computer-based learning, web-based

training, and computer-assisted instruction (CAI) in higher

education for teaching and learning is increasing, across all

disciplines, fields of study, and university faculties (Chang,

1984; Devitt and Palmer, 1999; Fleming et al, 2003;

Greenhalgh, 2001).

The motivations for the development of this style of teaching

and learning are varied. Increasing accessibility, portability, effi-

ciency, and consistency, institutional needs, economies of scale,

a move towards distance education, globalisation of higher

education, and rationalisation of teaching are all cited as driv-

ers for the increasing use of technology in teaching and

learning (Federico, 2000; Oliver, 2005; Shaw & Marlow, 1999).

According to Liegl and Janicki (2006, p886): “Academicians

are placing more and more course material online to supple-

ment their traditional in-class instructions”. However,

web-based course management software, such as

Blackboard, Moodle, and Sakai, provide a general ‘one-size-

fits-all’ approach to e-learning and do not take into account

the needs and attitudes of individual learners.

There is already a large and developing body of literature on

the design and development of e-learning programmes and

student online learning experiences (Hohne and Schumann,

2004; Karim-Qayumi and Qayumi, 1999; Lu et al, 2003;

Pfund, 2005; Shoham and Gonen, 2008; Stephenson, 2001).

According to Steele and co-workers:

Much of the research to date on CAI has focused

on the comparison of outcomes when content is

offered using standard education formats (for

example, lecture or text) vs. providing the same

content in a computerised learning environment.

Steele et al (2002, p225)

There is also extensive literature on CAI techniques used for

tutorial assistance and support for students (Salmon, 2000).

However, literature focusing specifically on student percep-

tions of their personal experience of e-learning has only

emerged in the last few years (Daugherty and Funke, 1998;

Hayward and Cairns, 2001). There is little or no published lit-

erature on predictors of students’ attitudes towards

e-learning and computer/technology assisted learning, and

limited empirical research published about the attitudes of

specific groups of students (for example, nursing students,

law students, engineering students) regarding the use of

e-learning strategies. The purpose of this study was to inves-

tigate the significant predictors of health science students’

attitudes towards e-learning and CAI.

Literature review

THE USE of technology and e-learning strategies are becom-

ing more prevalent in health sciences education (Cook,

2005; Lynch et al, 1998; Olgilvie et al, 1999).

While e-learning theoretically allows for the adaptation of

educational content to meet student learning needs,

the majority of research in this area has been confined to

standard instructional formats (such as lecture or text) and

accompanying e-learning material. Walter et al (2000)

examined the views of staff employed in the mental

health service sector about computers by investigating

their patterns of use, and the attitudes and expectations of

staff before and after the purchase of new equipment and

training. Most respondents, especially those with com-

puter experience or who had worked in mental health for

less than five years, viewed computers favourably. At the

same time, half the respondents felt they did not have

sufficient access to a computer at work and the vast major-

ity had not received any hands-on experience (Walter et

al, 2000).

Other studies have indicated that age and experience (using

computers and technology) are indicators of attitudes

towards e-learning and CAI (Hegney et al, 2006; Schumacher

et al, 1997; Webster et al, 2003). Bojanczyk and Lanphear

(1994) demonstrated that e-learning in a medical school set-

ting was an effective means of delivering educational content

regardless of students learning preferences. They also showed

that learning outcomes were unaffected by students’ atti-

tudes towards computers and their use in education. Link and

Marz (2006) studied attitudes towards e-learning of medical

students finding that age, computer use, and previous expo-

sure to computers were more important predictors than

gender. Steele et al (2002) found that a group of surgical res-

idents rated a CAI programme as efficient and effective, and

were positive about the programme’s content, clarity, organ-

isation and ease of use. However, they also found that many

medical students still indicated a strong preference for lec-

ture and text-book learning and were concerned that:

“computers will supplant student-teacher contact” (Steele et

al, 2002 p2002).

Some studies have found that a negative attitude towards

e-learning and computers is correlated with resistance to

computerisation whilst others have found that attitude

towards computers has no significant effect on performance

by inexperienced users of technology (Liegle and Janicki,

2006; Lynch et al, 2001). Studies to date have exhibited

mixed results. Harriot et al (2004), for example, reported that

dietetics students reacted positively in general to the com-

puter-assisted instruction programme and they considered it

Predictors of attitudes to e-learning of Australian health science students

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 61

Ted Brown et al

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 62

to be effective in preparing them for the practical compo-

nent; however, there was a reluctance to accept it as the sole

teaching method.

The use of e-learning is viewed as a way of providing profes-

sional education that aims to produce graduates with

ongoing relevance, innovation, flexibility, creativity, cost-effec-

tiveness, and enhanced quality of service in the health care

industry. Researchers have suggested that some students may

lack the necessary skills to use web-based learning platforms

effectively and therefore do not exhibit any interest in engag-

ing with e-learning approaches (Hohne and Schumann,

2004; Link and Marz, 2006; Popovich et al, 2008).

The cited advantages of e-learning and CAI include: the abil-

ity to transit text, graphics, audio, video and data; the

potential for interaction (real time versus delayed time)

among many individuals over considerable distances, since

students and teachers do not have to be present at the same

place and time for instruction; the ability to connect people

effectively and efficiently throughout the world; the ability to

provide relatively inexpensive online instruction; and the

potential to tailor instruction for self study (Czaja and Sharit,

1998; Federico, 2000; Vogal and Wood, 2002). Teaching can

occur on local or global networks, and educational material

can be distributed electronically.

Despite the growth of e-learning and CAI, there remains a

lack of empirical evidence about its effectiveness and predic-

tors of attitudes towards it (Steele et al, 2002; Vogal and

Wood, 2002). Given that e-learning technologies are being

used with increasing frequency in health science education

contexts (both nationally and internationally), the specific aim

of this study was to examine the attitudes to e-learning of a

group of health science students and determine the signifi-

cant predictors of attitudes to e-learning within the group.

Method

Design

A non-experimental cross-sectional survey using a sample of

convenience was completed.

Participants

All students enrolled on health science courses at a large,

multi-campus Australian university in 2006-2007 were sur-

veyed (n=2885). The number of participants in each

programme was as follows: Pharmacy (900), Physiotherapy

(215), Occupational Therapy (134), Nursing (375), Paramedic

Studies (170), Radiation Therapy and Radiography (240),

Social Work (606), Dietetics and Nutrition (162), and

Midwifery (83). The programmes varied in regard to the type

of degree conferred, prior educational requirements neces-

sary for admission, and the maximum number of students

that could be accepted for enrolment. Most health science

programmes offer an undergraduate programme that is three

or four years in length and involve students completing clin-

ical fieldwork education placements in practice settings; the

two exceptions are Radiation Therapy (which is offered as a

two year graduate entry course) and Social Work (which

offers both undergraduate and graduate entry programmes).

A power analysis indicated that a minimum of 500 partici-

pants were required to complete the proposed data analyses,

indicating a required minimum response rate of 20% (Stein

and Culter, 2000).

Instrumentation

A self-report questionnaire was used to obtain demographic

information about each participant which included pro-

gramme, year/ level, gender, and age. Three scales were used

to obtain data about the attitudes of health science students

toward e-learning: the Online Learning Environment Survey

(OLES) (Trinidad et al, 2004), the Computer Attitude Survey

(CAS) (Startsman and Robinson, 1972; Wagman, 1983), and

the Attitude Toward Computer Assisted Instruction Semantic

Differential Scale (ATCAISDS) (Allen, 1986).

The OLES is a dual format instrument where students are

asked to rate the ‘actual’ learning environment experienced

in a unit/course/subject/module along with their ‘preferred’

learning environment using a five-point rating scale (almost

never, seldom, sometimes, often, almost always) (Trinidad

et al, 2004). It was designed to provide educators using e-

learning with a mechanism to reflect on the learning

environment provided based on the results gained from stu-

dent feedback. The OLES incorporates scales from five

existing instruments: the ‘What Is Happening In this Class?

questionnaire’ (WIHIC) (Fraser et al, 1996); the

‘Constructivist Learning Environment Survey’ (CLES) (Taylor

et al, 1997); the ‘Distance Education Learning Environments

Survey’ (DELES) (Jegede et al, 2002); the ‘Technology-Rich

Outcomes-Focused Learning Environment Instrument’

(TROFLEI) (Aldridge et al, 2003); and the ‘Test of Science-

Related Attitudes’ (TSRA) (Fraser, 1981). Each scale has been

used in previous empirical studies and has been shown to be

reliable and valid (Trinidad et al, 2004).

The OLES is made up of nine subscales comprising 54 items.

Three scales from the WIHIC questionnaire were selected,

namely; Teacher Support (TS), Student Autonomy (SA), and

Equity (EQ). Four scales from the DELES were selected, namely;

Authentic Learning (AL), Student Interaction and

Collaboration (SIC), and Asynchronicity (AS). Finally, one scale

from each of the CLES and TROFLEI were selected, namely;

Personal Relevance (PR) and Computer Usage (CU), respec-

tively. To assess students’ satisfaction with their e-learning

environment, an Enjoyment (EN) scale was adapted from the

TSRA. Examples of items from each subscale are reported in

Table 1. Estimation of reliability for OLES was found to be sat-

isfactory for both the actual and preferred OLES forms.

Internal consistency (Cronbach a reliability) was reported by

Trinidad et al (2004) as ranging from 0.86 to 0.96 for the

actual version and from 0.89 to 0.96 for the preferred ver-

sion. Factor analysis was used to confirm the subscale

structure of the OLES (Aldridge et al, 2003).

Predictors of attitudes to e-learning of Australian health science students

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 63

Scale No. of Description Sample Item Originalitems Questionnaire

Computer Usage (CU) 6 TROFLEI

Teacher Support (TS) 8 WIHIC

Student Interaction & Collaboration (SIC) 6 DELES

Personal Relevance (PR) 5 CLES

Authentic Learning (AL) 5 DELES

Student Autonomy (SA) 5 DELES

Equity (EQ) 7 WIHIC

Enjoyment (EN) 6 TSRA

Asynchronicity (AS) 6 WIHIC

Note: What Is Happening In this Class? (WIHIC); Constructivist Learning Environment Survey (CLES); Distance EducationLearning Environments Survey (DELES); Technology-Rich Outcomes-Focused Learning Environment Instrument (TROFLEI);and Test of Science-Related Attitudes (TSRA)

Table 1: Examples of items from the nine Online Learning Environment Subscales (OLES)

The extent to which students usetheir computers as a tool tocommunicate with others and toaccess information.

The extent to which the teacherhelps, befriends, trusts and isinterested in students.

The extent to which students haveopportunities to interact with oneanother, exchange information andengage in collaboration.

The extent to which there is aconnection between students’ out-of-school experiences.

The extent to which students havethe opportunity to solve real-worldproblems that are authentic.

The extent to which students haveopportunities to initiate ideas andmake their own learning decisions,and the locus of control is studentoriented.

The extent to which students aretreated equally by the teacher.

The extent to which students aresatisfied with their e-learningenvironment.

The extent to which theasynchronous nature of thediscussion forum promotes reflectivethinking and the posting of messagesat times convenient to the students.

I use the computer toobtain informationfrom the Internet.

The teacher gives mevaluable feedback onmy assignments.

I share information withother students.

I can relate what I learn to my lifeoutside of this class.

I work on assignmentsthat deal with real-world information.

I make decisions aboutmy learning.

I am treated the sameas other students in this class.

I would enjoy myeducation if more ofmy classes were online

I read the postedmessages at times thatare convenient to me.

The CAS assesses students’ attitudes towards computers, as

well as their reaction to, and comfort with, CAI. The survey

consists of 26 questions and uses a 5-point rating scale rang-

ing from 5=strongly agree (very effective) to 1=strongly

disagree (very ineffective). Negatively-anchored items are

reverse scored before the data is analysed. Questions 1 to 16

assess attitude toward computers in general’ (CASg), devel-

oped by Startsman and Robinson (1972). Scores on this

subscale vary from 16 to 80 with low scores indicating posi-

tive perceptions regarding the use of computers in general.

The reported mean was 51.38 with a standard deviation of

9.67. Questions 17 to 26 evaluate attitude toward comput-

ers in education’ (CASe) using a subscale of the ‘Cybernetics

Attitude Scale’ developed by Wagman (1983). Scores on the

CASe range from 10 to 50 with low scores indicating positive

perceptions regarding the use of computers in education. The

reported mean for this subscale was 47.05 with a standard

deviation of 9.23. Examples of items from the two subscales

include: “I would rather have a computer solve a problem for

me than a mathematician” (CASg) and: “I would feel more

independent learning from a computer because I can work at

my own pace” (CASe). The CAS has been used in two previous

studies by Steele et al (2002) and Lynch et al (2001) with

medical students, and has demonstrated reliability and valid-

ity (Startsman and Robinson, 1972; Wagman, 1983).

The ATCAISDS is used to measure attitudes towards CAI. It is

composed of 14 bipolar adjective scales and provides an over-

all score of attitudes toward CAI as well as three subscales

that examine comfort, creativity and function (Allen, 1986).

Each bipolar adjective pair of the semantic differential scale is

measured on a 7-point scale that reflects attitudes ranging

from positive to negative. A score of 7 is associated with the

most positive response while a score of 1 is associated with

the most negative rating. Content validity and factor analy-

sis data have been published and support the contention that

the tool measures the evaluative component of attitudes

toward CAI (Allen, 1986). Content validity was established

via a set of five judges, four of whom were well known

American nursing researchers with expertise in computer

applications in nursing, while the final member of the group

was a psychometrician with expertise in constructing seman-

tic differential scales (Allen, 1986). Internal consistency of

0.85 for the ATCAISDS has been reported (Allen, 1986).

ATCAISDS has been used previously with nursing students

(Brudenell and Carpenter, 1990).

Data Analysis

The ‘Statistical Package for Social Sciences’ (SPSS, Version 10)

was used for data entry, storage, retrieval, and the calculation

of descriptive statistics. Mean scale scores and standard devi-

ations were calculated for the OLES, CAS and ATCAISDS. A

multivariate analysis of variance (MANOVA) was used to deter-

mine if any significant differences existed between the actual

and preferred scores for nine different OLES subscales. As data

was ordinal level a Spearman’s rho correlation was calculated

to determine if variables were associated with each other.

Correlation analysis determined the strength and direction of

the relationship between variables. Spearman’s rho is a sta-

tistical measure of correlation in non-parametric statistics and

provides a product moment correlation coefficient

(Minichiello et al, 2004). This does not provide causal infor-

mation, but allows for associative interpretation of the results.

Correlations provide details of association between variables

but not predictive or causal relationships.

In order to determine if variables were significant predictors

of health science students’ attitudes towards e-learning, a

multiple linear regression equation was utilised (Kielhofer,

2006). Student demographic variables (for example, year of

enrolment, age, gender), the nine OLES actual subscales

(Teacher Support, Student Autonomy, Equity, Authentic

Learning, Student Interaction and Collaboration, Personal

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Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 64

Health science Number %discipline of students

who returned questionnaires

Occupational 19 2.3Therapy

Physiotherapy 50 6.1

Paramedics 62 7.5

Social Work 116 14.1

Nutrition & Dietetics 129 15.7

Pharmacy 240 29.2

Radiation Therapy 36 4.4

Radiography 35 4.3

Nursing 82 10.0

Midwifery 41 5.0

Bachelor of 12 1.4Nursing / Bachelor of Emergency

Total 822 100.0

Table 2: Number of completed questionnaires received

per health science student discipline group (N=822)

Predictors of attitudes to e-learning of Australian health science students

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 65

Relevance, Computer Usage, Enjoyment, and Asynchronicity),

and the three ATCAISDS subscales (Comfort, Creativity, and

Function) were independent variables for the regression

analysis. The CASe and CASg subscales from the CAS were the

dependent (criterion) variables for the regression analysis.

Independent variables for the regression were identified from

the Spearman’s rho correlations and only those that signifi-

cantly correlated with the CASe and CASg subscales were

included in the regression analyses (Tabachnick and Fidell,

2001).

Procedures

Project approval was granted by the University’s Ethics

Committee prior to commencement of the project.

Permission was also sought from heads of department or pro-

gramme chairs before asking students to complete the survey

during a regularly scheduled class. Participants were given an

explanatory statement and brief overview of the project,

along with the self-report questionnaire and participation was

on a voluntary basis.

Results

Participant results

A total of 835 questionnaires were returned of which 13

incomplete questionnaires were excluded from the analysis.

The number of completed questionnaires per health science

discipline is presented in Table 2. It can be seen that 29.3%

Age of Number Percentageparticipants of students

at each age range

15-19 years 291 35.4

20-24 years 341 41.5

25-29 years 67 8.2

30-34 years 40 4.9

35-39 years 34 4.1

40-44 years 24 2.9

44-49 years 13 1.6

50 years or older 12 1.5

Total 822 100.0

Table 3: Age range of participants

Mean SD

Computer usage — actual 3.3002 0.6371

Computer usage — preferred 3.6760 0.7249

Teacher support — actual 3.5546 0.6645

Teacher support — preferred 4.6011 0.4200

Student interaction — actual 3.8972 0.7220

Student interaction — preferred 4.2084 0.6638

Personal relevance — actual 3.7234 0.7147

Personal relevance — preferred 4.2864 0.5632

Authentic learning — actual 3.7608 0.7471

Authentic learning — preferred 4.3477 0.5521

Student autonomy — actual 4.2078 0.5958

Student autonomy — preferred 4.6533 0.4026

Equity — actual 4.2741 0.6428

Equity — preferred 4.6686 0.4835

Enjoy — actual 2.8427 0.9454

Enjoy — preferred 3.2245 1.0383

Asynchronicity — actual 3.5925 0.9524

Asynchronicity — preferred 3.9617 0.9058

General 2.9305 0.4245

Education 2.9298 0.4418

Total 2.9296 0.3671

Comfort 18.410 5.4300

Ceativity 15.1100 5.2300

Function 19.2100 5.4000

Note: Online Learning Environment Survey (OLES);Computer Attitude Survey (CAS), and Attitude TowardComputer Assisted Instruction Semantic DifferentialScale (ATCAISDS)

Table 4: Descriptive statistics of the OLES, CAS, and

ATCAISDS (N=822)

ATC

AI

CAS

OLES

of completed questionnaires were from pharmacy students.

The sample contained more females (n=671) than males

(n=151). Over 40% of the students who completed the ques-

tionnaires were between 20-24 years old (Table 3) while 50%

of the students entered the health science programme

directly from high school.

Participant raw scale scores

The raw mean scores for the OLES, CAS, and ATCAISDS are

reported in Table 4. The mean item scores for health science

students’ actual and preferred OLES scores are shown in Figure

1. Statistical testing (MANOVA for repeated measures) was

completed to determine if any significant differences existed

between the actual and preferred scores on the nine OLES

subscales. The results indicated that there was a significant

difference between the actual and preferred scores for all

nine OLES subscales (*p<0.001).

Regression analysis results

According to Pallant (2007), regression can be: “used to

explore the relationship between one continuous dependent

variable and a number of independent variables or predic-

tors” (Pallant, 2007, p146). It is based on correlation,

however allows for a more sophisticated examination of the

interrelationship among a set of variables. Standard multi-lin-

ear regression was used to establish which, if any,

demographic variables, OLES actual subscales, or ATCAISDS

subscales (independent variables) were found to significantly

predict the scores of the CASe and CASg subscales from the

CAS (dependent variables). To meet the regression equation

inclusion criterion, variables had to significantly correlate with

the dependent variable.

The independent variables that met the significant correla-

tion criterion (p<0.05 and p<0.01) for the CASg dependent

variable are listed in Table 5. The following independent vari-

ables were significantly correlated with the CASg dependent

variable: current year level of enrolment (Enrol), age,

percentage of academic work time that involved use of a

computer (% comp. work), computer usage (CU), teacher

support (TS), personal relevance (PR), authentic learning

(AL), student autonomy (SA), equity (EQ), and asynchronic-

ity (AS). The following independent variables were not

significantly correlated with the CASg dependent variable:

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Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 66

Figure 1: Mean subscale scores for health science students’ actual and preferred scores on the Online Learning

Environment Survey (OLES). CU: Computer usage; TS: Teacher support; SIC: Student interaction & collaboration; PR:

personal relevance; AL: Authentic learning; SA: Student autonomy; EQ: Equity; EN: Enjoyment. AS: Asynchronicity.

MANOVA results indicated that there was a significant difference between the actual and preferred scores for all

nine subscales (* p < .001)

Online Learning Environment Survey

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

CU TS SIC PR AL SA EQ EN AS

Actual

Preferred

Subscales

Average item

mean

gender, enjoyment (EN), student interaction and collabora-

tion (SIC), and the three ATCAISDS subscales (comfort,

creativity and function).

The independent variables that met the significant correlation

criterion (p<0.05 and p<0.01) for the CASe dependent variable

are listed in Table 6. The following independent variables were

significantly correlated (p<0.01) with the CASe dependent vari-

able: age, computer usage (CU), teacher support (TS), personal

relevance (PR), authentic learning (AL), student autonomy (SA),

equity (EQ), asynchronicity (AS), and enjoyment (EN). The fol-

lowing independent variables were not significantly correlated

with the CASe dependent variable: gender, current year level

of enrolment (Enrol), percentage of academic work time that

involves use of a computer (% comp. work), student interac-

tion and collaboration (SIC), and the three ATCAISDS subscales

(comfort, creativity and function).

In order to utilise regression analyses, certain assumptions

need to be accounted for to ensure that the conclusions

drawn from the results and the relationships between inde-

pendent variables are accurate (Tabachnick and Fidell, 2007).

These assumptions relate to multicollinearity, homoscedas-

ticity, normality, linearity, and outliers. Multicollinearity is a

condition in which the independent variables are so highly

correlated with each other (usually above 0.9 according to

Pallant, 2007) that they indicate they are measuring the same

phenomenon or construct. As can be seen in Table 5, the cor-

relations between the independent variables ranged from

-0.028 (CU: Actual and Age) to 0.598 (PR: Actual and AL:

Actual). In Table 6, the correlations between the independent

variables ranged from -0.022 (CU: Actual and Age) to 0.599

(PR: Actual and AL: Actual) indicating that multicollinearity is

unlikely to be an issue for the regression analyses involving

the CASe and CASg dependent variables.

Homoscedasticity is the assumption that the variability in

scores for one variable is approximately equal at all values of

the other variable. Homoscedasticity, linearity, and normality

were determined by examination of the residual plots.

According to Pallant (2007), the normal probability plot

should illustrate a reasonably straight diagonal line from bot-

tom left to top right, and the residuals should be roughly

rectangularly distributed within the scatterplot. The normal

probability plots and scatterplots for the CASe and CASg sub-

Predictors of attitudes to e-learning of Australian health science students

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 67

CASg Enrol Age % CU: TS: PR: AL: SA: EQ: AS: comp Actual Actual Actual Actual Actual Actual Actualwork

CASg 1.000 .091** .233** -.076* -.146** .115** .149** .169** .102** .231** -.069**

Enrol .091** 1.000 .442** .073* .028 .087** .027 .159** .001 .066* .110**

Age .233** .442** 1.000 .055* -.028 .109** .201** .207** -.012 .100** -.019

% comp. work -.076* .073* .055* 1.000 .065* -.003 -.042 .038 .034 -.041 .066*

CU: Actual -.146** .028 -.028 .065* 1.000 .172** .156** .197** .161** .105** .357**

TS: Actual .115** .087** .109** -.003 .172** 1.000 .314** .396** .185** .397** .153**

PR: Actual .149** .027 .201** -.042 .156** .314** 1.000 .598** .300** .248** .085**

AL: Actual .169** .159** .207** .038 .197** .396** .598** 1.000 .280** .315** .091**

SA: Actual .102** .001 -.012 .034 .161** .185** .300** .280** 1.000 .287** .163**

EQ: Actual .231** .066* .100** -.041 .105** .397** .248** .315** .287** 1.000 .152**

AS: Actual -.069** .110** -.019 .066* .357** .153** .085** .091** .163** .152** 1.000

Note: * = Correlation is significant at the p< 0.05 level; ** = Correlation is significant at the p< 0.01 level; AttitudesToward Computers in General scale (CASg), current year level of enrolment (Enrol), age, percentage of academic worktime that involves use of a computer (% comp. work), Computer Usage (CU), Teacher Support (TS), Personal Relevance(PR), Authentic Learning (AL), Student Autonomy (SA), Equity (EQ), and Asynchronicity (AS)

Table 5: Independent variables that significantly correlated with the CASg dependent variable

scale dependent variables were visually examined. Both sets

of plots indicated that the distribution of residuals were

acceptable and that the sample was linear, normally distrib-

uted, and homoscedastic.

Outliers were detected through inspection of the Mahalanobis

distances. According to Pallant (2007), depending on the

number of independent variables, the critical chi-square value

can be determined. This value states the maximum

Mahalanobis distance any case can have before it is deemed

an outlier. Using a p<0.001 criterion for Mahalanobis distance,

15 extreme multivariate outliers were identified. Pallant (2007,

p157) states that: “it is not uncommon to find a number of

outlying residuals” and if only a few outliers exist: “it may not

be necessary to take any action”. Therefore, it was decided

not to exclude the 15 outliers since the sample size was 822

participants. This indicates that the data is suitably correlated

with the dependent variable for examination through multi-

linear regression to be reliably undertaken.

CASg regression analysis

Table 7 shows the unstandardised regression coefficients (B)

and unstandardised regression coefficients standard error (SE

B), the standardised regression coefficients (ß), the semi-par-

tial correlations (sri²), the significance (p), R² and adjusted R²

when regressing significantly correlating demographic fac-

tors, and OLES subscales on the CASg subscale. The regression

analysis results indicate that 13% of the variance in the CASgsubscale score was predicted by the independent variables.

The largest ß values (irrelevant of positive or negative sign)

indicate the strongest unique contributors to the dependent

variable (Pallant, 2007).

From table 7, it is evident that respondents’ age, the OLES

actual Equity subscale, and the OLES actual Computer Usage

made the strongest unique contributions (p<0.000) to the

CASg subscale as a the dependent variable (2.8.%, 2.6%, and

2.3% of the 13% total variance respectively).

CASe regression analysis

Table 8 shows the regression results of the CASe subscale

dependent variable. The findings indicate that 13.9% of the

variance in the CASe subscale score was predicted by the inde-

pendent variables. It is evident that respondents’ age and four

of the OLES actual subscales (student autonomy, equity, asyn-

chronicity, and enjoyment) made the strongest unique

contributions to the CASg subscale as the dependent variable.

age, student autonomy, equity, asynchronicity, and enjoyment

accounted for 1.9%, 0.5%, 0.7%, 0.7%, and 5.5% of the

13.9% total variance respectively and therefore made a sig-

nificant unique contribution at p<0.05.

Ted Brown et al

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 68

CASe Age CU: TS: PR: AL: SA: EQ: AS: EN: Actual Actual Actual Actual Actual Actual Actual Actual

CASe 1.000 .150** -.141** .084** .096** .107** .088** .132** -.182** -.268**

Age .150** 1.000 -.022** .119** .197** .201** -.014 .102** -.012 .112**

CU: Actual -.141** -.022 1.000 .178 .149 .204 .160 .110 .349 .332

TS: Actual .084** .119** .178** 1.000 .309** .400** .184** .399** .155** .209**

PR: Actual .096** .197** .149** .309** 1.000 .599** .298** .251** .088** .046

AL: Actual .107** .201** .204** .400** .599** 1.000 .277** .316** .101** .146**

SA: Actual .088** -.014** .160** .184** .298** .277** 1.000 .294** .165** .056**

EQ: Actual .132** .102** .110** .399** .251** .316** .294** 1.000 .160** .104**

AS: Actual -.182** -.012 .349** .155** .088** .101** .165** .160** 1.000 .412**

EN: Actual -.268** .112** .332** .209** .046 .146** .056 .104** .412** 1.000

Note: * = Correlation is significant at the p< 0.05 level; ** = Correlation is significant at the p< 0.01 level; AttitudesToward Computers in Education (CASe), Age, Computer Usage (CU), Teacher Support (TS), Personal Relevance (PR),Authentic Learning (AL), Student Autonomy (SA), Equity (EQ), Asynchronicity (AS), and Enjoyment (EN)

Table 6: Independent variables that significantly correlated with the CASe dependent variable

Discussion

Predictors of health science students’attitudes toward computers in general

The CASg scale was designed to assess student attitudes

towards computers in general. An example of a CASg item

was “If it were not for computers, we would probably be 10

years behind our present technological place”. The regres-

sion analysis results indicated that significant predictors of

CASg for health science students were their age, the OLES

actual Equity subscale, and the OLES actual Computer Usage.

Age and the CASg were only moderately correlated with each

other (r = 0.233; p<0.00) suggesting that attitudes to

towards CASg became more positive with age. This is not sur-

prising as university students gain more experience in their

third and fourth years of professional education, and they

will no doubt have more exposure to e-learning and CAI in

classes and tutorials. This notion is supported by the Gen Y

factor or Neomillennials where students from this era are

likely to be more technologically-savvy. In the case of health

science students, they are more likely to have the opportu-

nity to use computers and technology during fieldwork

education placements they complete. With increased expo-

sure and experience using computer assisted learning as a

mode of education, students are more likely to have positive

attitudes towards CASg.

In a study by Czaja and Sharit (1998), age differences in atti-

tudes toward computers as a function of experience with

computers and computer task characteristics was examined.

Their findings indicated no age differences in overall atti-

tudes, however there were age effects for the dimensions of

comfort, efficacy, dehumanisation, and control. In general,

older people perceived less comfort, efficacy, and control over

computers than did other younger participants. Overall, Czaja

and Sharit’s found that experience with computers resulted

in more positive attitudes for all participants across most atti-

tude dimensions.

Brumini et al (2005) investigated the influence of gender,

age, education, and computer usage on the attitudes of a

group of 1,081 hospital nurses towards computers. They

Predictors of attitudes to e-learning of Australian health science students

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 69

Variable B SE B ß p sri²

Enrol -1.954E-02 .193 -.004 .919 -.004

Age .171 .036 .193 .000 .167

% comp. work -.116 .062 -.068 .062 -.067

CU: Actual -1.743 .408 -.167 .000 -.153

TS: Actual 7.341E-02 .420 .007 .861 .006

PR: Actual .258 .435 .028 .553 .021

AL: Actual .669 .422 .076 .113 .057

SA: Actual .655 .439 .058 .137 .053

EQ: Actual 1.858 .414 .183 .000 .160

AS: Actual -.344 .276 -.049 .213 -.045

Note: R² = .144; Adjusted R² = .130; B = unstandardised regression coefficients; SE B = unstandardised regressioncoefficients standard error; ß = standardised regression coefficients; sri² = semi-partial correlations indicate the uniquevariance predicted by the independent variable; p = significance; Attitudes Toward Computers in General scale (CASg),current year level of enrolment (Enrol), age, percentage of academic work time that involves use of a computer (% comp.work), Computer Usage (CU), Teacher Support (TS), Personal Relevance (PR), Authentic Learning (AL), Student Autonomy(SA), Equity (EQ), and Asynchronicity (AS).

Table 7: Summary of standard regression analysis for variables predicting correlations between independent

variables and CASg dependent variable

found that nurses below the age of 30 had more positive

attitudes towards computers and computer usage than

those older. Webster et al (2003), in a survey of 590

Australian nurses, found that computer use was influenced

by education, nursing seniority, age, and length of time

working as a nurse and, to a lesser extent, gender. Link and

Marz (2006) studied the attitudes towards e-learning of

medical students and found that age, computer use, and

previous exposure to computers were more important pre-

dictors than gender. As can be seen, age and previous

experience working with computers appear to be significant

influencing factors related to computer use with health care

staff. This is comparable to the results of this study where

respondent age was a significant predictor of CASg.

Two other independent factors found to significantly predict

CASg were the OLES actual Equity subscale, and the OLES

actual Computer Usage. The OLES Equity subscale was

referred to as: “the extent to which students are treated

equally by the teacher” (Trinidad et al, 2004, p5). In relation

to the Equity independent factor, if students perceive a

teacher’s instructional style to be open and equitable in

e-learning and CAI contexts, this would positively influence

their attitudes towards computers (Zisow, 2000). Koukel

(2005) investigated how the teaching styles of university fac-

ulty were related to computer use in the classroom; they

found increased classroom computer use among those fac-

ulty members who rated their attitudes toward computer

-based instruction as supportive. If teachers were supportive

of students in relation to e-learning and CAI, this would be

similar to the OLES Equity variable.

The OLES Computer Usage subscale was defined by Trinidad

et al (2004, p.5) as: “the extent to which students use their

computers as a tool to communicate with others and to

access information”. The OLES actual Computer Usage inde-

pendent factor relates to students experience with using

technology and computers in educational contexts.

Computer Usage as a predictor of CASg makes intuitive sense

in that if a student has had more experience with using com-

puters, then their attitudes are likely to be more positive.

Several studies have reported findings related to computer

usage; Popovich et al (2008) investigated the changes in atti-

tudes towards computer usage over a longitudinal period by

comparing the attitudes of undergraduates in 2005 with

Ted Brown et al

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 70

Variable B SE B ß p sri²

Enrol -1.954E-02 .193 -.004 .919 -.004

Age 8.476E-02 .022 .148 .000 .138

CU: Actual -.411 .272 -.060 .130 -.053

TS: Actual .472 .276 .071 .087 .061

PR: Actual -5.352E-02 .281 -.009 .849 -.007

AL: Actual .358 .273 .062 .190 .046

SA: Actual .573 .284 .079 .044 .071

EQ: Actual .652 .273 .098 .017 .084

AS: Actual -.454 .189 -.098 .017 -.085

EN: Actual -1.278 .193 -.278 .000 -.234

Note: R² = .150; Adjusted R² = .139; B = unstandardised regression coefficients; SE B = unstandardised regressioncoefficients standard error; ß = standardised regression coefficients; sri² = semi-partial correlations indicate the uniquevariance predicted by the independent variable; p = significance; Attitudes Toward Computers in Education (CASe), Age,Computer Usage (CU), Teacher Support (TS), Personal Relevance (PR), Authentic Learning (AL), Student Autonomy (SA),Equity (EQ), Asynchronicity (AS), and Enjoyment (EN)

Table 8: Summary of standard regression analysis for variables predicting correlations between independent

variables and CASe dependent variable

those in 1986; in both cases, the amount of time spent using

a computer was positively related to computer attitudes. In an

earlier study, Brumini et al (2005) found that nurses who had

used computers more than five hours per week and who had

attended computer science courses had more positive atti-

tudes towards computers than those who had used

computers less frequently and who had not attended a com-

puter course. Schumacher et al (1997) studied computer

anxiety and attitudes of physical, occupational, and speech

therapists in a large urban teaching hospital before and after

the implementation of a computerised documentation sys-

tem. Fifty-three therapists were surveyed with a

pre-installation questionnaire and reported mild computer

anxiety and generally good attitudes about the planned com-

puter system. A greater amount of previous computer use

and better self-related computer skills were consistent with

less computer anxiety. A post-installation follow-up survey

completed six months after the computers were in place

revealed a reduction in therapists’ reported computer anxi-

eties. Shoham and Gonen (2008) investigated the attitudes of

a random sample of 411 registered nurses’ related to intent

to use computers in the hospital setting as a predictor of their

future behaviour. The study findings suggested that the

threat and challenge that are involved in computer use were

shown as important mediating variables to the understand-

ing of the process of predicting attitudes and intentions

toward using computers.

Using a standardised questionnaire, Brown and Coney exam-

ined the anxiety of 51 medical interns about computer use

and their attitudes toward medical computer applications.

Factors that commonly emerged as predictive of anxiety

about computer use included self-rated skills, typing ability,

and computer attitudes. Predictive factors of positive atti-

tudes toward computers included self-rated skills, typing

ability, frequent prior computer use, computer ownership,

and computer anxiety. Factors that were not predictive of

computer anxiety or attitudes toward computers included

age, gender, and physical input of data. Interestingly, age was

not predictive of attitudes towards computers as they were in

this study. It appears that most of the studies related to atti-

tudes towards computers have found that age and previous

computer use experience are important influencing factors

related to attitudes towards computers. Again, this concurs

with the CASg regression analysis results.

Predictors of health science students’attitudes toward computers in education

The CASe was designed to assess students’ attitudes towards,

and comfort with, computers in education. An example of a

CASe item was: “I would like learning from a computer

because I can work at my own pace.” The regression analy-

sis results indicated that significant predictors of the CASg for

health science students were their age, and four of the OLES

actual subscales; student autonomy; equity; asynchronicity;

and enjoyment. According to Trinidad et al (2004, p5), on the

OLES, student autonomy is defined as: “the extent to which

students have opportunities to initiate ideas and make their

own learning decisions, and the locus of control is student

oriented” while enjoyment is considered to be: “the extent to

which students are satisfied with their e-learning environ-

ment”. Asynchronicity in the OLES context is: “the extent to

which the asynchronous nature of the discussion forum pro-

motes reflective thinking and the posting of messages at

times convenient to the students” (Trinidad et al, 2004, p.5).

As with the CASg, age was a significant predictor of CASe.

Several studies have reported similar findings related to age

and health care professionals’ attitudes towards e-learning

and CAI (Brumini et al, 2005; Link and Marz, 2006; Webster

et al, 2003). Equity was also a significant predictor of health

sciences students’ perceptions.

Previous studies have demonstrated the impact of teachers’

instructional approaches, commitment and interactional style

with students in e-learning environments. Mendez Cruz

(2002), for example, investigated the relationship between

students‘ predominant learning profile and faculty teaching

preferences in an American university nursing programme.

Most nursing students reported that they had a sensing-

thinking learning style whereas most nursing academic

teaching staff reported a sensing-feeling or intuitive-thinking

teaching style. Therefore, it is possible that the reason the

equity independent factor (as defined by the OLES) is predic-

tive of CASe is related to educators’ teaching style. Another

study indicated that staff teaching nursing are more teacher-

centred than student-centred in both the instructional

methods they use and in their stated philosophies of teach-

ing and learning (Schaefer and Zygmont, 2003).

OLES Student Autonomy, Enjoyment, and Asynchronicity all

relate to students’ experiences of using e-learning and CAI

in their educational contexts. Student autonomy can be facil-

itated by promoting gradual independence in relation to

learning goals and activities. This is largely the philosophy of

problem-based learning (PBL) and patient-centred learning

(PCL) that has been adopted in many medical and health sci-

ence education programs. The same underlying principles of

student-centred learning that underpin PBL and PCL can also

be used to promote student autonomy in e-learning and CAI

environments. Some health science education programs are

promoting student autonomy as part of their teaching phi-

losophy using PBL and PCL concurrently with e-learning and

CAI (Hohne and Schumann, 2004; Lechner et al, 2001;

Pfund, 2003; Roesch et al, 2003). In a 2007 study, Costa et

Predictors of attitudes to e-learning of Australian health science students

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 71

al compared the use of didactic lectures with that of interac-

tive discussion sessions in undergraduate teaching of

orthopaedics and trauma to a group of medical students. The

medical students in the ‘interactive discussion group’ rated

the presentation of their teaching more highly than those in

the ‘didactic lecture group.’ Study results indicated that inter-

active teaching styles were more popular than didactic

lectures in undergraduate orthopaedic and trauma teaching

and that knowledge retention was better following an inter-

active teaching style. It would appear that student autonomy

and enjoyment were more common in contexts where stu-

dents were actively engaged in their learning and taking

responsibility for it.

Asynchronicity in the CASe context is utilised by various

online computer programs such as Blackboard and Sakai as

well as when students are able to access podcast or vodcast

lectures, download lecture notes, access unit readings, and

complete online literature searches all from the confines of

their own computer in isolation from the university campus

context. Asynchronicity in the CASe health contexts is also

used in some simulation programs (e.g. MicroSim) in health

science education contexts (Karim-Qayumi and Qayumi,

1999; Welk et al, 2008). Where e-learning and CAI can

include components of asynchronous learning, it appears

that the education needs of health science students can be

better met (Federico, 2000).

Study limitations

THERE ARE several inherent limitations with this study.

Convenience sampling was used to recruit participants there-

fore respondent bias may be an issue. Only students enrolled

in health science programs from one university were included

in the sample and this limits the generalisability of the results.

Only a limited number of independent variables from three

valid and reliable scales were included in the regression analy-

sis, hence other significant predictors may not have been

accounted for. However, a balance between reasonable

respondent burden and eliciting students’ opinions had to be

achieved. Therefore, only a limited number of self-report

scales were included in the questionnaire.

Recommendations for futureresearch

THERE ARE several recommendations for future research

related to this study. Firstly, a similar study could be com-

pleted with health science students from a broader sampling

base. For example, students enrolled in other health care pro-

fessions such as audiology, optometry, medicine, chiropractic,

orthoptics, podiatry, and prosthetics and orthotics could be

included in a larger sample. Having a much larger data set

would allow for comparisons and contrasts to be explored

between different health science student groups. As well, stu-

dents from multiple universities could be included to ensure

adequate geographical representation. Secondly, a similar

study could be completed comparing health science students

with other student cohorts such as law, engineering, infor-

mation technology, business, or education. Thirdly, other

questionnaires examining other attitude constructs could be

included to try and establish other significant predictors to

e-learning and computer-assisted instruction. Finally, student

participants could be randomly selected to take part in the

study to minimise the issue of respondent bias.

Conclusion

HEALTH SCIENCE students enrolled in health science courses

(Occupational Therapy, Physiotherapy, Nursing, Midwifery,

Dietetics and Nutrition, Pharmacy, Social Work, Radiation

Therapy, Radiography and Paramedic Studies) at a large

multi-campus Australian university were asked to complete

three standardisd scales about their attitudes towards

e-learning and educational technology. Multiple linear

regression analysis indicated that significant predictors of

health science students’ ‘attitudes toward computers in gen-

eral’ were students’ age, students’ perceptions of being

treated equitably by their teachers/instructors, and the extent

to which students used their computers as a tool to com-

municate with others and to access information. Significant

predictors of health science students’ ‘attitudes toward com-

puters in education’ were students’ age, students’ sense of

autonomy, students’ perception of equitable treatment by

their teachers/instructors, the extent to which the asynchro-

nous nature of the discussion forum promoted reflective

thought among students, and the extent to which students

were satisfied with their e-learning environment. Educators

need to be cognisant of these factors when using e-learning

strategies and techniques with health science students.

Acknowledgments

WE WOULD like to thank all of the health science students

from Monash University who volunteered their time and

input to complete the survey. Acknowledgments are

extended to the Monash University Faculty of Medicine,

Nursing and Health Sciences Learning and Teaching

Performance Fund—Project Grants Scheme—that provided

the funding for this project.

Ted Brown et al

Journal of Applied Research in Higher EducationVolume 2 • Number 1 • 72

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Author Biographies

TED BROWN is Associate Professor and Postgraduate Coordinator in the Department of Occupational Therapy atMonash University – Peninsula Campus. Ted has 18 years clinical experience as a paediatric occupational therapist andcompleted his PhD at the University of Queensland in 2003. His research interests include occupational therapypractice (with children and families), education research in the health sciences, professional issues, and evidence-basedpractice. He has published over 75 peer-reviewed journal manuscripts and authored 5 book chapters. He currentlysupervises six PhD and three Masters’ students.

MARYAM ZOGHI is a Research Officer in the Rehabilitation Sciences Research Centre at the University of Melbourne.She is also a practising physiotherapist and researcher with expertise in neurological rehabilitation. Maryam completedher PhD in Neurophysiology at the University of Adelaide and has published 25 peer-reviewed papers and abstracts.

BRETT WILLIAMS is a Senior Lecturer in the Department of Community Emergency Health and Paramedic Practice atMonash University. He is currently undertaking his PhD which is examining the graduate attributes for undergraduateparamedic students. Brett’s research and teaching interests are focused on the paradigm of student-centred learning(CBL/PBL), educational technology, innovative learning and teaching strategies, clinical education and inter-professionaleducation.

SHAPOUR JABERZADEH is a Senior Lecturer in Physiotherapy at Monash University where he established the MotorControl of Human Movements Laboratory in 2008. Shapour completed his PhD in the field of health sciences at theUniversity of South Australia in 2002 and went on to obtain a Graduate Certificate in Health Professional Education atMonash University in 2007. He has published more than 65 peer-reviewed papers and abstracts.

LOUIS ROLLER has been an academic at Monash University for 46 years. Louis ‘humanised’ the pharmacy program byintroducing a course in psychosocial sciences, the first of its kind in a pharmacy programme in Australasia, andemphasising the patient over the product. Louis was on the Pharmacy Board of Victoria for 22 years, has significantlycontributed to various pharmaceutical compendia, and has authored hundreds of scientific and professional articles.

CLAIRE PALERMO is a Lecturer in Nutrition and Dietetics at Monash University and an accredited practising dieticianand nutritionist. Since moving into academia she has become passionate about teaching and learning. Her mainresearch interests are public health nutrition workforce development and competency assessment in public healthnutrition practice. Claire is currently completing her PhD on the evaluation of a mentoring circle intervention for postgraduate professional development.

LISA MCKENNA is an Associate Professor in the School of Nursing and Midwifery, and Associate Dean (Learning &Teaching) in the Faculty of Medicine, Nursing and Health Sciences at Monash University. Her research predominantlyfocuses around nursing and midwifery education, particularly in the area of clinical education - including mentorship,use of technology and simulation, and professional socialisation. Lisa has widely published and presented outcomesfrom her previous research.

CAROLINE WRIGHT is a Senior Lecturer in the Department of Medical Imaging and Radiation Sciences, MonashUniversity, where she convenes the Master of Medical Radiations. Caroline's clinical interests include head and neckcancer and the role of advanced practice in radiation therapy. Caroline's research interests include fitness to practice inmedical radiation science and the educational development of advanced practice roles. In 2006, Caroline was awardedthe Faculty of Medicine, Nursing and Health Sciences Dean's prize for teaching excellence.

MARILYN BAIRD is an Associate Professor and Foundation Head in Medical Imaging and Radiation Sciences at MonashUniversity. Her research interests include improving clinical teaching and learning. She is President of the MedicalRadiation Practitioners Board for Victoria, Australia.

MICHAL SCHNEIDER-KOLSKY is Deputy Head and Senior Lecturer in Medical Imaging and Radiation Sciences atMonash University. Michal’s research interests focus on developing novel methods for the early detection of cancer, aswell as response of cancer to therapy using PET/CT and MRI. She also concurrently investigates improved ways todeliver and assess educational outcomes in the higher education sector. Michal currently supervises four PhD and fourMPhil students.

LESLEY HEWITT is a Lecturer in the Department of Social Work, Monash University. Lesley has received a number ofFaculty and University awards for her teaching, in particular for the development of on-line materials to enhancestudent learning outcomes and for honours supervision. Lesley's research interests include family violence and sexualassault. She is currently a member of an inter-faculty team looking at how students from non-traditional backgroundssucceed at University.

JENNY SIM is the Stream Leader in Medical Imaging and Senior Lecturer at RMIT University. Jenny’s research interestsinclude online learning, reflective practice, continuing professional development, and learning and teaching in HigherEducation. Her current research has an inter-disciplinary focus, and involves working collaboratively with colleaguesfrom other education institutions.

TANGERINE HOLT serves as Director of International Education with the Office of the Deputy Vice-Chancellor(International and Marketing) at Monash University. Prior to this, she was a Senior Lecturer in the Centre for Medicaland Health Sciences Education, Monash University. Tangerine’s academic leadership has focused on developingexcellence through innovative models in medical and health professional education and research (at bothundergraduate and post-graduate levels) in Australia and internationally.

Ted Brown et al

Journal of Applied Research in Higher EducationVolume 1 • Number 2 • 76