Predictors of attitudes to e-learning of Australian health care students
Transcript of 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).
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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