EFFECTIVE INTEGRATION OF E-LEARNING TOOLS

EFFECTIVE INTEGRATION OF E-LEARNING TOOLS
AMONG LECTURERS IN A TERTIARY INSTITUTION: A
PERCEPTUAL SURVEY

A study conducted by

ASST. PROF. DR. NOR AZIAN MOHD. NOOR
Institute of Education
International Islamic University Malaysia
[norazian@iiu.edu.my]

ABDULHAMEED KAYODE AGBOOLA (Ph.D)
Institute of Education
International Islamic University Malaysia
[kayode68@hotmail.com]

ABSTRACT
This study investigated the diffusion of e-learning innovation among the
lecturers of the International Islamic University Malaysia (IIUM). The collection
of data in this study took two approaches: In the first approach, a Lecturer Elearning
Perceptual Survey Questionnaire (LEPSQ) was used to collect data
from the academic staff of IIUM, based on a proportional stratified random
sampling. 98% response rate was attained totaling 324 respondents in all. The
second approach used E-learning Readiness Survey questionnaire, which was
adapted for collecting of data from 26 Deans, acting Deans and heads of
departments, who were administrators in each of the faculty of the University.
Analysis revealed five factors, the regression analyses showed two factors as
significant; that is, e-learning training and e-learning confidence, for both elearning
adoption and e-learning readiness. The significant factors have
practical importance and were replicated for this study. In addition, the elearning
sustainability survey revealed that the academic staff was making
progress, but more efforts would be worthwhile to overcome some hindrances,
which were infrastructure related and personal incapability. Finally, some issues
emerged from the opinions, views and suggestions of the respondents. Among
these issues were: that e-learning will not take over conventional learning, that
users needed to be educated, and the provision of adequate infrastructure,
administrative, technical and mental supports to all users were very crucial for a
successful implementation of e-learning in the University, IIUM.
Keywords: Lecturers' Perceptions; E-Learning Implementation; Predicting
Factors; E-Learning Adoption; Gender Effects; Adoption Variables.
INTRODUCTION
According to John Chambers (as cited in Rosenberg, 2001, p. xv), “the
biggest growth in the Internet, and the area that will prove to be one of the
biggest agents of change, will be in e-learning.” The world’s global economy of
financial transactions, and commerce in goods and services which increasingly
occurs through high-speed computers and telecommunications have changed
the scenario for manpower needs from labour intensive industries to jobs that
call workers to be highly skilled in computers and telecommunications. The
demand for a well-educated workforce has driven many countries to rethink
their education systems. An education system has to be suited to the demands
of the technological age so that a competitive edge can be maintained. Such
demand for technology savvy workforce is reflected in Alvin Toffler’s declaration
(as cited in Rosenberg, 2001), that “the illiterate of the 21st century will not be
those who cannot read and write, but those who cannot learn, unlearn, and
relearn”, p. 3). An ancient proverb says:- “if we don’t change our direction, we’ll
end up exactly where we are headed” (cited in Rosenberg, 2001, p. 41). This
indicates that learning institutions will have to constantly change and adapt in
their environments if they are not to lag behind.
The advantages that technology provides to training and learning
include not only the possibility of one-on-one interaction for every learner, the
ability to simulate new ideas, the chance to try things out at one’s own pace and
to fail in private without the fear of ridicule from other students (Galagan, 2002).
The Internet has also become an important instructional tool to facilitate the
transfer of many types of information from one computer to another, and is
rapidly becoming an effective means of communication in schools and colleges.
Internet-based instruction has been manifested in one-to-one (e.g. tutor-tostudent),
one-to-many (e.g. tutor-to-group) and many-to-many (e.g. group-togroup)
approaches to instruction. The forms of communication may be
synchronous, which occurs in real-time, with all parties communicating within
the same time frame; or it may be asynchronous, where there may be a time
delay between the communicators when sending, receiving and replying to any
given communicative event (Webb, Jones, Barker & Schaik, 2004).
According to Murphy and Greenwood (1998), “research findings
suggest that Information and Communication Technology is significantly underused
by students and teachers. The problem is worldwide and many
explanations were offered for it” (p. 415). Among them were the unavailability
and / or inaccessibility of resources in schools (Veen, 1993; Byard, 1995; Wild,
1996; Dearing, 1997). The scarcity of opportunity to use computers has been
cited as reasons why students and teachers were slow in the ICT uptake
(Blackmore, Stanley, Cole, Hodgkinson, Taylor & Vaughan, 1992); Dunn &
Ridgway, 1991), teacher early year school pressure (Wild, 1996), and the lack
of experience and training at the pre-service level in using ICT (Oliver, 1994;
Wild, 1995). Also, the lack of teacher or teacher trainers’ encouragement to
students on using ICT in schools and the lack of confidence on the part of
student teachers and their trainers in computing skills were cited as reasons for
the low ICT use (Dunn & Ridgway, 1991; Downes, 1993; McDonald, 1993a;
Collison & Murray, 1994).
Murphy and Greenwood (1998) added that conflicting reports have
hinted that age and gender effects could be the factors in determining the extent
of the low student teacher ICT uptake (Woodrow, 1991; Blackmore et al., 1992;
Lienard, 1995). Some reports from Summer (1990) and McMahon and Gardner
(1995) have suggested that male students experience less anxiety about ICT
and make more frequent use of it. Other studies have underscored that female
students have shown lower confidence or knowledgeability than males about
using computers (Oliver, 1993; Van Braak, 2001).
Many other studies have agreed with the claim that there are no
significant differences between the attitudes of male and female students
regarding ICT use (Koohang, 1989; Kay, 1989; Hunt & Bohlin, 1993; Marshall &
Bannon, 1986; Woodrow, 1991). The age phase for which students are being
trained to teach has been implicated as significant in ICT uptake because
studies have shown that the students trained for primary schools demonstrated
more anxiety and used computers less than the students trained for secondary
schools (Blackmore et al., 1992; Oliver, 1994). Also, the students’ area of
specialization has been pinpointed as having a strong influence on their ICT
use. For example, Summers and Easdown (1996) mentioned subject specialism
of student teachers, and also the lecturers’ area of discipline as factors that may
influence their extent of ICT use.
This study specifically aimed to survey the perceptions of academic
staff of the International Islamic University Malaysia (IIUM) to assess their
opinions, readiness, and sustainability based on the following classification of elearning
implementation constructs, which are: 1). E-Learning Confidence, 2).
E-Learning Training, 3). E-Learning Adoption, 4). E-Learning Readiness, 5). ELearning
Consequences, and, 6). E-Learning Sustainability.
METHOD
The study employed two types of questionnaire detailed as follows: a
self-constructed questionnaire, titled: Lecturer E-learning Perceptual Survey
Questionnaire (LEPSQ), which was comprised of 35 items placed on a 7-point
Likert scale ranging from “very strongly disagree” to “very strongly agreed”, was
used to collect data from a proportional stratified random sample of 324
academic staff of the International Islamic University Malaysia.
The second instrument that was used in the present study was the “ELearning
Readiness Survey” questionnaire, which was adapted from Marc
Rosenberg (2000). It comprises of 20 items with short answers that were
designed along a qualitative research method, which was used to collect data
from 26 Deans or Heads of department in each Kulliyyah of the University. The
data that was was analysed qualitatively based on the analytic procedures that
were provided by Marc Rosenberg (2000). Construct validity of items was
tested for its reliability using an internal consistency method, which indicated a
value of 0.8. The pilot study was conducted on the academic staff of the
International Islamic University Malaysia.
A Principal component analysis (PCA) was applied on the data from
the respondents (N=324) in order to examine the dimensionality and the
appropriateness of the variables to be selected. Basically, Bartlett test of
sphericity is significant and that the Kaiser-Meyer-Olkin measure of sampling
adequacy is 0.912, which is far greater than the threshold of 0.6. The anti–
image correlation matrix revealed that all measures of sampling adequacy
ranged between 0.630 and 0.957, which were well above the acceptable value
of 0.5. This further proves the factorability of the items. Five factors were
extracted with eigenvalues greater than 1. These five factors accounted for 66%
of the total variance.
FINDINGS
In this study, the respondents’ background demographic characteristics
were classified into gender, age, nationality, faculty/center, teaching experience
and area of specialisation, and computer and internet skills respectively. The
results have shown that the majority of the respondents were males. As
indicated by the findings, males were the dominant gender within the
University’s academic staff population, whose age range was still within 25-44
years old. Individuals within this age group were considered to be active and
youthful individuals. Relatively, almost all of the respondents have high level of
teaching experience that range between 1-10 years, and many of them were
majoring in human sciences and pure science majors.
Majority of the respondents were skilled in the required computer
software programmes, such as, word processor, spreadsheets or excel,
databases, statistics package, presentation software, copy and transferring of
files, document scanning and creating PDF files. The distribution of the
respondents skillfulness in the Internet tools, such as web searching, web
evaluating, e-mail, World Wide Web browsing, newsgroup, creating
homepages/websites, chatting, and participating in
teleconference/videoconferencing was quite encouraging. Respondents have
indicated that they acquired their computer and internet trainings through formal
training, but it would be adequate if they could be improvised with extra ICT
skills. Lastly, the majority of the respondents indicated that they accessed the
Internet for 10 hours and above per week.
As for the age and experience, the table revealed that there were linear
relationships between them and e-learning adoption (p = 0.01), while software
skills correlated well with e-learning confidence, e-learning training and elearning
adoption (p = 0.01). As for the Internet skills, it correlated well with elearning
confidence and e-learning adoption (p = 0.01). These correlation
results indicated that there were statistically significant linear relationships
between age, experience, software skills and Internet skills. As for the analysis
of variance (ANOVA) to test for the influences of the strata groups (gender and
areas of specialization) on the extracted factors, the results revealed that
gender has a significant influence on the respondents’ perceptions of e-learning
confidence. For areas of specialisation and e-learning confidence, the result
showed that it has no statistically significant effects on e-learning confidence,
while gender and areas of specialisation had no interactive effects on the
respondents’ e-learning confidence. Also, gender and areas of specialization
had no influence on the respondents’ e-learning training at all. Finally, the
respondents’ gender and areas of specialisation had no interactive effects on elearning
consequences, e-learning readiness and e-learning adoption.
In terms of the regression analyses for e-learning adoption, summary of
the analysis of variance (ANOVA) revealed that the overall model was
statistically significant, and the set of independent variables explained 34 per
cent of the total variance in e-learning adoption. For the predictive power, two
predictors were statistically significant, namely; e-learning confidence and elearning
training, but e-learning training was the best predictor of e-learning
adoption with the highest Beta value (0.47). As for e-learning readiness, the
overall model was statistically significant, 32 per cent of the total variance was
explained by the independent variables in e-learning readiness. The predictive
power of the individual predictors indicated that only three predictors were
statistically significant, namely; gender, e-learning confidence and e-learning
training. They were significantly related to e-learning readiness, but e-learning
training was the best predictor of e-learning readiness with the highest Beta
value (0.47).
However, regression analyses have shown no effects of multicollinearity,
as revealed by the tolerance and variance inflation factors (VIF) values, which
were within the acceptable levels (1.05 to 2.41), and the computerised values of
the threshold indicated that the statistically significant predictors of e-learning
adoption and e-learning readiness had threshold values that were lower than
the values of the confidence interval in their lower and upper bounds. Therefore,
for e-learning adoption, both of the significant predictors; e-learning confidence
and e-learning training were of practical importance, while age, e-learning
confidence and e-learning training were of practical importance to e-learning
readiness, based on their threshold values.
Finally, as for the cross-validation of the regression analysis for the
predictors of e-learning adoption and e-learning readiness, the analysis
revealed that e-learning confidence and e-learning training were statistically
significantly related with e-learning adoption and they were replicated, and both
were of practical importance in the cross-validation analysis. While e-learning
training was still the best predictor for both e-learning adoption and e-learning
readiness.
Comments, Views, Opinions and Suggestions of the Critical Mass
Based on the respondents’ comments, views and opinions, three themes
can be deduced from them, namely; that (1) e-learning will not take over
conventional learning, (2) users needed to be well-educated through the
provision of adequate professional development plans, (3) and the provision of
appropriate infrastructure, administrative, technical and mental supports to all
users were very crucial for a successful implementation of e-learning in the
University, IIUM.
DISCUSSIONS
According to the results, the findings revealed that e-learning confidence
and e-learning training were statistically significant for both e-learning adoption
and e-learning readiness. Murphy and Greenwood (1998) reported that younger
lecturers showed a significant level of confidence than older ones on the use of
computers in teaching. But contrarily, Muse (2003) found that computer
confidence had no effect on the criterion variables of his study on ICT use.
However, in Osborn’s study (as cited in Muse, 2003), it was reported that
if users of ICT valued the tools very well, they would develop their confidence
for it. Wigfield (as cited in Schunk, 2000) mentioned that valuing a task can lead
to greater self-regulatory efforts. Therefore, to improve the perceptions towards
e-learning implementation, it was suggested that users should be encouraged
to increase their confidence in computing skills. Lack of confidence was
reported as a reason for low ICT uptake (Murphy & Greenwood, 1998).
The regression analyses showed that e-learning training were statistically
significant, and the best predictor for e-learning adoption and e-learning
readiness. This is an indication that the lecturers need basic knowledge
upgrading on the use of ICT tools. The cross-validation analyses had shown
that e-learning training was replicated in both the estimation and crossvalidation
sub-samples.
Further more, Veen (1993) suggested that the lack of initial training of
teachers was an anathema to ICT use and implementation. In a study
conducted by Murphy and Greenwood (1998), it was reported that the lecturers
felt that they were not well-trained and exposed to ICT tools as compared to
their students. Thus, this findings suggested that more ICT trained and
confidence building in the area would be worthwhile in enhancing their abilities
to teach with e-learning tools. Also, Jonassen (1996) mentioned that educators
need to experience the personal value embedded in the technology as both
productivity tools to increase efficiency and as mind-tools for providing learning
opportunities to students. On this note, Fabry and Higgs (1997) admonished
that educators must experience the power of technology to implement it, while
training is considered as a critical factor in the successful implementation and
integration of technology.
Additionally, Ertmer, Bai, Dong, Khalil Park and Wang (2002) highlighted
that efforts to provide professional development for teachers were increasing.
Flynn (as cited in Education Week, 2001) related that conversation around
professional development has gained more attention and focus recently as
compare to talks about hardware and infrastructure, and funding for
professional development has increased manifolds, and several surveys have
proven that teachers were now participating in a variety of professional
development activities that were available. But, despite these increases in
resources and training opportunities, teachers were still struggling to achieve
high levels of integration (Becker, 2000; NCES, 2000).
Although argument riffed that the use of computers was incompatible
with the traditional requirements of teaching, whereas, others claimed that
placement of computers within the reach of teachers and within supportive
school cultures was very important so that teachers can improve their ICT
potential (Cuban, 1993). But Anderson and Dexter (2000) conceived otherwise,
they argued that unrestricted access and training would not amount to effective
use of computers if teachers were not encouraged, or expected to use
computers in meaningful ways. In this regard, they suggested that strong
leadership is critical to computer integration and ICT implementation in general.
Therefore, Ertmer et. al (2002) mentioned that few educators today
would argue with the premise that the principal plays an important role in
facilitating technology use in the schools. Crystal (2001) admonished that
building administrators for technology leadership is the nexus through which all
issues flow. But many of the faculty leaders and administrators were novice
technology users, who use the computers only for the basic functions, such as,
word processing and power-point presentation, they had gained little experience
or training in the knowledge and skills needed to be effective technology
leaders.
According to Schmelzer (2001), a broad experience is important for
administrators (Deans and Heads of departments), who could be considered as
the technology leaders. Their vast experiences will help them to develop an
understanding of how technology can improve instructional practices and
provide a repertoire of strategies for supporting teachers’ efforts to use
technology in the classroom. Leaders’ vision and adequate planning are crucial
necessity to embark on this objective. Contrarily though, research has
documented that strategies that promote the status or image of teachers who
were advanced in their use of ICT were not likely to have effect on the adoption
behaviours of other teachers (Jebeile & Reeve, 2003). Nevertheless, the
researcher would strongly suggest that addressing the factors that were found
to be significant in this study would be worthwhile for the e-learning
implementation this University.
Moreover, the results of the cross-validation of the predictors of elearning
adoption with the data from the second sub-sample (n=162) produced
e-learning confidence and e-learning training as statistically significant
predictors of the e-learning adoption and e-learning readiness. Thus, e-learning
training and e-learning confidence as a significant predictor of e-learning
adoption were replicated in the cross-validation.
For the second phase of the study, which is the E-learning Sustainability
survey, the overall descriptive analysis revealed that the majority of the
respondents answered that initiatives for e-learning implementation were still
underway with relatively apparent e-learning sustainable success. While handful
number of minority answered that, they could see few substantive evidences.
This means that there were potentially visible opportunities that e-learning will
be supported and sustained, if it is finally implemented in the University.
However, comparing the results from both of the instruments (selfdeveloped
and the adapted sustainability survey questionnaire), the researcher
concluded that the results were quite congruent with one another, though they
were designed and followed different analytical methods. Also, it has been
evidently substantiated that the results corresponded well with each other. For
example, the regression analyses, where e-learning confidence and e-learning
training were concluded as statistically significant and replicated predictors for
e-learning readiness and e-learning adoption were in consonance and reflective
of the thematic deductions from the views, comments and opinions of the Dean,
Head, Acting Heads of various faculties and departments in the University,
IIUM. This means that there were high levels of indication from the regression
analyses that the respondents, who were academic staff, would implement elearning
for instructional delivery in their teaching activities providing they were
equipped with necessary skills through professional training and other support.
In the thematic deductions, themes number 2 and 3 highlighted on the needs for
training and other supports from the IIUM authority for the academic staff and
faculty administrators.
CONCLUSION
In summary, the regression analyses for e-learning adoption and elearning
readiness showed that e-learning confidence and e-learning training
were their statistically significant predictors, but e-learning training was their best
predictor. There were no effects of multicollinearity, the tolerance and variance
inflation factors (VIF) values were within the acceptable levels. The threshold
values indicated that the threshold values were within the acceptable range.
Both e-learning confidence and e-learning training were of practical importance
in predicting e-learning adoption and e-learning readiness.
In addition, the cross-validation regression analyses revealed that e-learning
confidence and e-learning training were statistically significantly related with elearning
adoption and e-learning readiness, and both were replicated and were
of practical importance for the predictors of e-learning adoption and e-learning
readiness. And e-learning training remained as the best predictor for both elearning
adoption and e-learning readiness.
For E-learning Sustainability survey, the respondents thought that
initiatives for e-learning implementation were still underway with relatively
apparent e-learning sustainable success, but some of them thought that they
could see few substantive evidence, which they hoped would be sustained,
should the University implement e-learning at the long run.
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