Your Role in Supporting School Readiness HCPSS Summer Institute June 2010.
The role of readiness factors in E-Learning outcomes: An ... · PDF fileThe role of readiness...
-
Upload
duongtuyen -
Category
Documents
-
view
220 -
download
2
Transcript of The role of readiness factors in E-Learning outcomes: An ... · PDF fileThe role of readiness...
The role of readiness factors in E-Learning outcomes: An empirical study
Abbas Keramati*
* Corresponding author
Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran
Masoud Afshari-Mofrad
Information Technology Management Department, Tarbiat Modares University, P.O. Box 14115-111,
Tehran, Iran
Ali Kamrani
Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran
Reference to this paper should be made as follows: Keramati, A., Afshari-Mofrad, M. and Kamrani, A.
(2011) ‘The role of readiness factors in E-Learning outcomes: An empirical study’, Computers &
Education, Vol. 57, pp.1919-1929.
Abstract
Although many researchers have studied different factors which affect E-Learning outcomes, there is little
research on assessment of the intervening role of readiness factors in E-Learning outcomes. This study
proposes a conceptual model to determine the role of readiness factors in the relationship between E-
Learning factors and E-Learning outcomes. Readiness factors are divided into three main groups including:
technical, organizational and social. A questionnaire was completed by 96 respondents. This sample
consists of teachers at Tehran high schools who are utilizing a technology-based educating. Hierarchical
regression analysis is done and its results strongly support the appropriateness of the proposed model and
prove that readiness factors variable plays a moderating role in the relationship between E-Learning factors
and outcomes. Also latent moderated structuring (LMS) technique and MPLUS3 software are used to
determine each variable’s ranking. Results show that organizational readiness factors have the most
important effect on E-Learning outcomes. Also teachers’ motivation and training is the most important
factor in E-Learning. Findings of this research will be helpful for both academics and practitioners of E-
Learning systems.
Keywords:
E-Learning; Readiness factors; Outcomes; Evaluation methodologies; Iran
1. Introduction
During recent years designing and implementing web-based education (E-Learning) systems have grown
dramatically (Hogo, 2010) and this type of education is playing an important role in teaching and learning
(Franceschi, 2009). It is implementing as a new method of training which complements traditional methods
(Vaughan, 2004) and its final ambition is to build an advanced society for citizens and support creativity
and innovation (Kim, 2005). In fact this new paradigm shifts education from teacher-centered to learner-
centered (Lee et al., 2009). Advantages like cost reduction, elimination of time and space constraint and
assisting the traditional instruction, make it important and popular (Chao, 2009). In addition, quality of
education in distance education systems depends on quality of electronic knowledge sources and other
didactic materials instead of depending on teacher quality and his ability to share knowledge (Cohen,
2006). The growth rate of E-Learning market is about 35.6% (Sun et al., 2008) and recent researches have
shown that only in America, nearly $40 billion is spent annually on technology based training (Johnson,
2009). According to such a heavily investing in this system, it is essential to evaluate its different aspects
and understand factors which influence its effectiveness (Schreurs, 2008). Success in implementing and
using this system is crucial because an unsuccessful attempt will be clearly revealed in terms of the return
of investment (Govindasamy, 2001). One of the most important variables which can have a critical effect
on E-Learning successful outcomes is readiness factor and schools have to improve and upgrade their
readiness to use this system (Wang et al., 2009). Although there are many researches evaluating different
E-Learning aspects, there is little research on assessment of the intervening role of readiness factors in E-
Learning outcomes. To overcome this shortcoming, in this study the role of readiness factors in the
relationship between E-Learning factors and outcomes is assessed. E-Learning readiness factors are divided
into three main categories including: technical, organizational and social factors.
The following two main questions of this research are considered:
• Do readiness factors have a moderating effect on the relationship between E-Learning factors and
E-Learning outcomes?
• How much is the moderating effect for different aspects of readiness factors (which aspects are
more important)?
2. Research background
With the increasing investment in E-Learning systems, it is essential to design and empirically investigate
the factors which affect E-Learning outcomes (Cukusic, et al., 2010). Many researchers have studied
several aspects of E-Learning and many different approaches were adopted (AbuSneineh, 2010). Most of
these researches have revealed that E-Learning is a suitable method for education and it allows easy
acceptance of novel strategies to improve learning outcomes (Wan et al., 2008). Despite these researches,
some other researchers demonstrated that E-Learning courses still have shortcomings and most E-Learning
courses have high dropout rate (Welsh et al., 2003; Stonebraker & Hazeltine, 2004; Means et al., 2009).
Since most of universities and organizations are going to use this system, it is important for both
practitioners and researchers to better understand how these shortcomings can be overcome and how
organizations could be ready to use this system. Some researchers studied different aspects of e-readiness
to help organizations for using E-Learning more effectively. E-readiness refers to the capabilities of the
organization for effective and efficient application of electronic media (Machado, 2007). Assessing E-
Learning readiness can help managers and policy makers to adopt the most appropriate policies and
facilities for better implementation and utilization of E-Learning system (Kaur, 2004).
One of the best ways to understand how E-Learning limitations can be overcome is developing models of
E-Learning (Alavi & Leidner, 2001). In order to do so, several researchers have proposed several models
which address how different features can influence E-Learning outcomes (Alavi & Leidner, 2001; Piccoli
et al., 2001; Johnson et al., 2008; Chen & Jang, 2010). For instance, Piccoli et al. (2001) believe that
human dimension (including students and instructors) and design dimension (including learning model,
technology, learner control, content and interaction) affect E-Learning effectiveness. Also Johnson et al.
(2008) assert that creating a shared, peer-centered learning environment is important in E-Learning success.
Table 1 displays some previous researches on E-Learning outcomes.
Table 1
In general, two major approaches toward readiness factors can be determined in the literature. In the first
approach, many researchers tried to extract different readiness factors for implementing an effective E-
Learning system. For instance, Kaur (2004) presented the factors of technological readiness, own level of
readiness, etc. Aydin and Tasci (2005) have focused on human resource readiness as an important variable
in E-Learning effectiveness in emerging countries. Darab and Montazer (2011) proposed an eclectic model
for assessing E-Learning readiness. Fathian et al. (2008) have evaluated e-readiness factors in Iran’s
environment. They have extracted organizational features, ICT infrastructures, ICT availability and
security as critical issues for e-readiness assessment. In the second approach, some researchers have
considered readiness factors as an independent variable which affects E-Learning outcomes directly. For
instance, Piccoli et al. (2001) and Sun et al. (2008) have considered some readiness factors (such as:
technology dimension and design dimension) as independent variables which affect E-Learning outcomes
directly. Also Johnson et al. (2009) have assessed the effect of technology characteristics on E-Learning
outcomes.
In this study, readiness factors are considered as a moderating variable because of two main reasons.
Firstly, a moderating variable refers to a variable which can affect the strength and direction of
relationships between other variables. In this research we have considered some readiness factors (for
instance: organizational rules) which don’t have a direct influence on E-Learning outcomes (for instance:
students’ motivation) but can influence the relationship between E-Learning factors and outcomes.
Secondly, Albadvi et al. (2007) considered the intervening role of organizational readiness in the
relationship between IT and firm performance. As the results of this work imply, to have a better level of
performance, it is essential to invest on intervening variables in addition to invest in IT. Accordingly, it is
important to know which aspects of IT and readiness factors have the greater effect on performance. Such
knowledge could be used to make wiser investments in IT. Since E-Learning is an IT-enabled system, this
paper aims to assess the role of E-Learning readiness factors in the relationship between E-Learning factors
and outcomes based on aforementioned research.
3. Research model
Conceptual model of this study is depicted in figure 1. As shown in this figure, the model is composed
from three variables including: E-Learning factors, readiness factors and E-Learning outcomes. As
mentioned earlier, this research tries to examine moderating effect of readiness factors on the relationship
between E-Learning factors and E-Learning outcomes.
3.1. E-Learning Factors
Selim (2007) grouped E-Learning critical success factors into 4 categories namely instructor, student,
Information Technology (IT) and university support. In this research, these factors were used to determine
their effect on E-Learning outcomes.
The role of teacher is critical in effectiveness and success of all kinds of education (Piccoli, 2001).
Especially in distance education, teachers’ conception of E-Learning and its usefulness plays a vital role
(Zhao, 2009) and their positive attitude toward using this system as a teaching assisted tool is required for
E-Learning success (Liaw et al., 2007). Previous research has shown that three instructor characteristics
affect E-Learning success: attitude, teaching style and IT competency (Webster, 1997). In this research,
based on previous researches, four major characteristics of teachers are used to measure this factor
including: motivation, attitude, training and teaching style.
Student is the most important participant in E-Learning (Aydin & Tasci, 2005). Since E-Learning is a
student-centered environment, high motivated and self-confident students can result in better E-Learning
outcomes (Baeten, 2010). In addition, students should be familiar with computer skills to be successful in
this system. Prior research has presented that if E-Learning facilitates students’ learning and eliminates
time and space constraints, they tend to use it (Papp, 2000). Also Liaw et al. assert that self-paced, teacher-
led, and multimedia instructions are major factors influencing learners’ conceptions toward E-Learning as
an effective tool for education (Liaw et al., 2007). Motivation, attitude and computer self-efficacy are the
most important factors which are used in this research to measure ‘student’ factor.
Distance education is a result of information technology explosion and IT is the engine of E-Learning
revolution (Selim, 2007). The role of IT in this type of education is vital and it is essential to prepare
suitable IT skills for success of E-Learning. Information technology has reshaped the process of
acquisition, communication and dissemination of knowledge within the educating process (Darab &
Montazer, 2011). It allows both the teacher and student to be separated in terms of time, place, and space.
Thus an appropriate use of IT in course content delivery is critical (Lim, 2007). In this research IT is
measured by the ability of E-Learning system to provide a good quality website design, the possibility of
interact with classmates through the web, well structures/presented information and the possibility of
online registration for participants. These measurements exist in appendix 1.
Top management commitment and support is a critical success factor in many IT projects (Huang, 2010)
and IT projects face failure because of lack of this factor (Soong, 2001). Since E-Learning is an IT-based
project, top manager should know this system and believe in its advantages. Top management support,
commitment and knowledge about E-Learning advantages are measures for ‘support’ variable in this
research.
3.2. Readiness factors
Many researchers have examined the role of readiness factors in E-Learning outcomes (Zhao, 2009). Prior
research has proved that technical readiness is one of the most important factors influencing E-Learning
outcomes (Brush, 2003) and it is crucial to match the right technology with the right learning objective
(Kidd, 2010). In this research based on literature, authors’ experiences and interviewees’ statements,
readiness factors categorized into three groups namely technical, organizational and social factors.
Technical factors include: Hardware, software, content, internet access, bandwidth and school’s space.
Organizational factors include: experts, organizational rules, organizational culture and management
permanence.
Social factors include: society’s conception of E-Learning, governmental rules and administrative
instructions.
3.3. Outcomes
Assessing E-Learning outcomes is important because individuals who are less satisfied with this system
have fewer tendencies for enrolling in future E-Learning courses (Carswell, 2001). Several models have
proposed to examine E-Learning outcomes (Piccoli, 2001; Wan, et al., 2008; Johnson et al., 2009; Cukusic
et al., 2010). In this research based on previous researches three important factors have been examined
including: Teachers progress, students progress and access to education for all.
Many studies have conducted to evaluate the relationship between E-Learning factors and its outcomes but
reviewing the literature has shown that they do not always effectively predict learning transfer per se
(Colquitt, 2000). In this research, the role of readiness factors in the E-Learning outcomes is assessed.
Therefore, the main hypothesis of this research can be argued as follows:
• Readiness factors play a moderating role in the relationship between E-Learning factors and E-
Learning outcomes.
Figure 1 depicts the conceptual model of this research.
Figure 1
4. Research Methodology
Data gathering method, measurement instrument and method of analysis are described in this section.
Hierarchical regression analysis has been used to confirm the moderating role of readiness factors and
latent moderated structuring (LMS) technique is used to rank each variable.
4.1. Data
Case studies and empirical researches are appropriate ways for IT researches (Baroudi, 1989). This
research is an empirical study by means of questionnaire in Iranian high schools. To recognize variables
affecting E-Learning outcomes and identify research variables, 9 interviews were conducted of E-Learning
specialists in the IT sector of Training Bureau in Tehran. Based on an extensive review of interview
transcripts and reviewing the literature, research variables were recognized. An initial set of questions was
developed to measure each variable. 2 academic experts viewed each of the items on the questionnaire for
its content, scope and purpose.
The participants of the study filled out a 36-item questionnaire. The questionnaire was of a likert scale one
containing five levels extending from 1 (Not at all) to 5 (Extreme). 96 respondents answer research
questionnaires but four questionnaires were ignored since they were not complete. This sample consists of
teachers at Tehran high schools who are utilizing a technology-based educating. Demographic information
of respondents are presented in the following tables.
Table 2
Table 3
Table 4
4.2. Measurement instrument
In this section we will operationally define research variables and then introduce their measuring
instruments. In this study, we tried to use different references for our research and in some cases tried to
use different aspects of a statement from different references in order to create a comprehensive
measurement instrument which has the most fitness with our purpose. Thus, our questions are partially
selected from different references. As stated before, we asked 2 academic experts to validate the
questionnaire.
4.2.1. E-Learning factors
As mentioned before, to assess this variable, based on Selim (2007) we have used four factors including:
student, teacher, IT and support. “Student” refers to attitudes of students toward E-Learning, their
motivation to use this system and their computer self-efficacy. “Teacher” indicates the motivation of
teachers for using E-Learning, their attitude toward this system, their teaching style and training for using
this new paradigm of education. “IT” refers to some dimensions of Information Technology –like online
registration, information management, etc- which affect E-Learning outcomes. Finally, “support” is used to
determine the effect of top management commitment and support for utilizing E-Learning.
Questions of the first two factors are taken from similar works like: Gattiker and Hlavka (1992), Thurmond
et al. (2002), Webster and Hackley (1997) and Liaw et al. (2007). Questions of the last two factors are
customized from Sun et al. (2008) and Albadvi et al. (2007).
4.2.2. Readiness factors
In this research we have categorized readiness factors into three groups namely: technical, organizational
and social. Technical factors refer to the technical dimension of E-Learning like: hardware, software,
bandwidth, etc. Organizational factors stand for factors like: organizational culture, rules, etc. Finally,
social factors are used to determine the impact of some aspects of society and government –like social
culture, governmental regulations, etc- on E-Learning.
To our knowledge, there is no study to provide empirical study for intervening role of readiness factors in
the relationship between E-Learning and outcomes. Thus questions in this section of questionnaire are
adapted from semi-similar works like: Arbaugh (2000), Nagi et al (2007) and Sun et al. (2008).
4.2.3. Outcomes
To assess E-Learning outcomes we have defined measures in relation with teachers’ progress, students’
progress and access to instruction for all. “Progress” refers to satisfaction, effectiveness and productivity of
teachers and students. Since the final ambition of E-Learning is to build an intelligent society, we have
used access to instruction for all as an outcome of this system.
Teachers’ progress questions are taken from Webster and Hackley (1997), Thurmond et al (2002) and Sun
et al (2008). Students’ progress questions are adapted from Johnson et al (2009). Finally 2 questions about
access to instruction for all are self-development based on Kim (2005).
4.3. Reliability and validity analysis
We have presented the reliability and validity analysis of the questionnaire in this section.
4.3.1. Reliability analysis
The reliability analysis of a questionnaire determines its ability to yield the same results on different
occasions and validity refers to the measurement of what the questionnaire is supposed to measure (Cooper
and Schindler 2003). For reliability analysis, Cronbach’s alpha is calculated by SPSS. As demonstrated in
table 5, all variables have an alpha greater than 0.7 and we can conclude that the questionnaire is reliable.
4.3.2. Validity analysis
In this research, construct validity, content validity and predictive validity were analyzed to ensure the
validity of the instruments (Nunnally and Bernstein 1994).
Construct validity shows the extent to which measures of a criterion are indicative of the direction and size
of that criterion (Flynn et al. 1994). It also shows that the measures do not interfere with measures of the
other criteria (Flynn et al. 1994). Construct validity of measurement instrument is analyzed through factor
analysis. The most common decision-making technique to obtain factors is to consider factors with
eigenvalue of over one as significant (Olson et al. 2005). Thus factor analysis is done and KMO and
Bartlett’s significance levels are calculated to show validity of the questionnaire. Table 5 presents these
measurements. As shown in this table, the questionnaire is valid and reliable.
Table 5
Content validity indicates meeting the specific range of contents that have been selected (Nunnally and
Bernstein 1994). It also shows that measurement instruments have elements that cover all aspects of
variables under measurement. Content validity cannot be numerically measured, but we can measure it
subjectively and judgmentally. Basically, content validity depends on the appropriateness of the content
and the method of rendering (Nunnally and Bernstein 1994). Since the selection of research variables is
based on an intensive survey of literature and all the elements are supported by authentic research, the
instrument has content validity. Furthermore, 2 academic experts have examined the content of the
questionnaire during the pre-testing.
Predictive validity is in fact the correlation between measurement instrument and an independent variable
taken from relating criteria (Nunnally 1978). This validity is only possible through correlation between the
predictor (independent variable) and criterion (dependent) variable (Nunnally and Bernstein 1994). In this
study, the results of two-variable and multi-variable correlation between “E-Learning factors” as
independent variables and “outcomes” as dependent variable have shown that there is significant
correlation between intended criteria under measurement in this study (Sig.: 0.000 and Correlation
coefficient: 0.425).
4.4. Method of analysis
There are different ways to test the moderating role of variables but in this research hierarchical regression
analysis is used. To do so, after developing a conceptual model, normality of data is tested and finally
regression analysis is performed.
4.4.1. Normality test
In order to test normality of data, two ways are used. Firstly, Kolmogorov-Smirnov (K-S) test is performed
(E-Learning variable = 0.085, Readiness factors = 0.917 and Outcomes = 0.559). Since non-significant
result (Sig value of more than .05) indicates normality, all variables (CSFs, Readiness factors and
Outcomes) have normal data. The other way is normal Q-Q plot. Figure 1 indicates these plots. This test
confirms that all variables have normal data.
Figure 2
4.4.2. Hierarchical regression
Moderating variable is generally defined as a variable (quantitative or qualitative) which affect direction or
strength of the relationship between a dependent variable and an independent variable. Figure 3
demonstrates a model in which there are three ways. Path A reveals the relationship between dependent and
independent variables. Path B reveals the relationship between dependent and moderating variables and
finally path C shows the relationship between dependent variable and the result of multiplying moderating
and independent variables.
Figure 3
Moderation hypothesis is proved if path C is statistically significant.
Table 6 presents the results of hierarchical regression of this research. As shown in this table, all three
paths are statistically significant.
Table 6
As shown in this table, hierarchical regression results proved that in 95% confidence level, readiness factor
variable plays a moderating role in the relationship between E-Learning factors and its outcomes.
Note that, we have assessed the mediating role of readiness factors but it was not significant statistically.
Moreover, Boyer et al. (1997) argues that a variable couldn’t play a moderating role and mediating role
simultaneously.
4.4.3. LMS technique
The LMS equations estimation method is particularly developed for the ML estimation of latent
interaction effects (Klein and Moosbrugger, 2000). In a structural equation, the latent variables usually
have a linear relationship in which the latent endogenous variables are linear functions of the latent
exogenous variables. But in some cases another exogenous variable might have a moderating effect on the
relationship between endogenous and exogenous variables. Therefore, a latent interaction effect influences
the latent model structure. In fact, by recognition of the new exogenous variable, the slope of the regression
of the endogenous variable on the exogenous variable will vary. The interaction effect is applied by adding
a product of latent exogenous variables in the structural equation. In other words, latent interaction models
include non-linear structural relationships in the structural equation. LMS executes alternative methods, for
instance: LISREL or 2SLS, with regard to statistical power, efficiency and the capability of detecting latent
interaction (Schermelleh-Engel et al., 2003). In this study, to run LMS technique, MPLUS3 software is
used.
4.4.4. Ranking of elements
Figure 4
A rank for each element is determined considering readiness factor variables as moderating factor.
For this purpose, using direct effect coefficients gained from the structural model, the real effect of each
factor is calculated considering all paths between that factor and outcomes (OC), and then the factors are
ranked in the order of calculated values for their effect on OC. The rankings are shown in Tables 7 and 8
using the results from the model. The tables show the rank of different factors based on the research model.
For example, Total effect of STUDENT is calculated as:
Total effect of STUDENT = 1.00*0.497 + 1.00*0.101 = 0.598
In the above formula, the effect of STUDENT is calculated by the summation of the two possible paths
from STUDENT to OC. The coefficient of 1.00 is gained from the measurement model, 0.497 is the direct
effect of ELNG on PER and 0.101 is the effect of interaction of ELNG and IST on PER, calculated from
the structural model.
Total effect of STUDENT, shows the impact coefficient of student factor on E-Learning outcomes. In fact,
0.497 and 0.101 are regression coefficients and 1.00 is the weight of “student factor” in comparison with
other factors which is calculated using LMS technique. Thus total effect is a weighted regression
coefficient which shows the effect of different E-Learning factors on E-Learning outcomes.
Table 7
In a similar procedure, different readiness factor (RF) elements are ranked based on their moderating
effects. Table 8 shows the results.
Table 8
5. Discussion and conclusion
Many researchers tried to evaluate readiness factors which affect E-Learning outcomes. For instance,
Aydin and Tasci (2005) have focused on human resource readiness as an important variable in E-Learning
effectiveness in emerging countries. Also Rhee et al. (2007) have assessed technological readiness for
implementing an effective E-Learning. Zoraini (2004) has mentioned some organizational readiness factors
such as culture and budget. Fathian et al. (2008) have evaluated e-readiness factors in Iran’s environment.
They have extracted organizational features, ICT infrastructures, ICT availability and security as critical
issues for e-readiness assessment. Finally, Piccoli et al. (2001) and Sun et al. (2008) have considered some
readiness factors (such as: technology dimension and design dimension) as independent variables which
affect E-Learning outcomes directly. Although aforementioned researchers have studied different factors
which affect E-Learning outcomes, there is little research on assessment of the intervening role of readiness
factors. By reviewing the literature, we have categorized readiness factors into three main factors including
technical, organizational and social factors. Also, Based on Albadvi (2007), we have tried to determine the
intervening role of readiness factors in the relationship between E-Learning factors and its outcomes.
Results of this study show that readiness factors play a moderating role and they strengthen the relationship
between E-Learning factors and E-Learning outcomes. To our knowledge, this is the first study which
categorized readiness factors into aforementioned three factors and also, this is the first study which
evaluated the intervening role of readiness factors in the relationship between E-Learning factors and
outcomes.
In this research, firstly, we considered technical infrastructures as a readiness factor. Some factors such as:
proper software and hardware or bandwidth, can play a crucial role in E-Learning outcomes. Internet low
speed and having problems while using the system may result in dissatisfaction and drop out of students
from the course. One of the most important difficulties in using E-Learning in Iran is the speed of internet
(Darab & Montazer, 2011).
Secondly, we regarded organizational readiness factors. Factors such as: organizational rules, culture and
experts, are considered in these factors. These factors can lay the ground for an appropriate move from
traditional education system to E-Learning system. It means that educating organizations should try to
adapt their rules and culture and train sufficient E-Learning experts to empower E-Learning outcomes after
implementation.
Social readiness factor is another factor which can moderate the relationship between E-Learning factors
and E-Learning outcomes. E-Learning not only affects students and teachers life, but also has an important
effect on parents and society. Therefore it is essential to consider social rules on E-Learning outcomes.
Factors like: society’s conception of E-Learning, governmental rules and administrative instructions, are
the most important social factors.
Our interviewees believe that one of the most difficulties in implementing and using E-Learning in Iran is
lack of readiness in teachers. Thus it is important to train them for using this system. Also, interviewees
argue that top managers in training bureau don’t believe in advantages of E-Learning yet. Another
difficulty in using E-Learning is management permanence. Some managers who were investing on
implementation of E-Learning have been changed and new managers don’t continue their programs. Our
experts also remark some concerns about E-Learning budget, cultural readiness and internet bandwidth.
Based on Selim (2007), E-Learning factors grouped into 4 categories namely: Student, teacher, IT and
support. Also based on previous researches and authors’ experiences readiness factors grouped into three
groups including: Technical, Organizational and Social. A survey conducted and 96 teachers In Tehran
high schools filled research questionnaire. Using hierarchical regression analysis, it is proved that
“readiness factors” variable plays a moderating role in this relationship. For instance, Organizational
readiness factor has a moderating effect. This means that organizational factors like management
permanence and organizational rules cannot have a direct effect on E-Learning outcomes like teachers’
motivation but these factors can affect E-Learning outcomes indirectly by influencing top management
support (which is an E-Learning factor in our model). In fact, management permanence can assure
managers to implement E-Learning which is an expensive and lengthy project appropriately. A successful
implementation of E-Learning will result in its effectiveness and motivates teachers to use this system.
The moderating effect of social readiness factors implies that some social factors (such as governmental
regulations) cannot play a direct role in E-Learning outcomes but these factors may affect the strength of
this relationship. In the other hand, some technical factors (such as school’s space) don’t have a direct
impact on teachers’ productivity but a better environmental condition will affect E-Learning outcomes
indirectly.
Finally, LMS technique and MPLUS 3 software were used to rank the effects of different aspects of
variables. Results demonstrated that organizational readiness factors are the most important aspects
influencing E-Learning outcomes. Technical and social readiness factors are in lower levels of importance.
The results of the ranking of different E-Learning elements show that in spite of previous researches who
have shown that student is the most important element of E-Learning (Aydin & Tasci, 2005), teachers’
motivation and training factor plays the most important role in E-Learning outcomes in Iranian high
schools. This result revealed that to shift learning environment from teacher-centered to learner-centered,
the first step is to train teachers and clarify advantages of this new paradigm for them.
5.1 Managerial recommendations
Based on results of the questionnaire and interviews, there are two important problems in utilizing E-
Learning in Iran. The first problem is management support. Our interviewees stated that many managers
still don’t believe in this system and therefore they don’t support it. The other major problem is teacher’s
conception of E-Learning. Our interviewees believe that teachers attitude toward this system is not
appropriate. Also they believe that students are more familiar and comfortable with new technologies like
internet and multimedia. Therefore it is important to first convince managers and second train teachers and
clarify advantages of this new paradigm for them. The results of LMS technique emphasize on this
argument. Also, LMS technique reveals that organizational readiness factor is the most important factor to
achieve better E-Learning outcomes. Thus managers should provide a good organizational environment to
implement and use E-Learning system more effectively.
5.2 Limitations and future research directions
This study has some limitations that should be taken into consideration, especially due to the fact that this
study is based on Iranian high schools. The suggested model might show different results in other
countries. Subsequent research can explore these issues using a broader research sample. Also some
researchers believe that E-Learning factors are more than 4 elements which can be taking into account for
future researches.
Acknowledgement
The authors would like to thank the reviewers for their constructive and invaluable comments.
References
AbuSneineh, W. & Zairi, M. (2010). An Evaluation Framework for E-Learning Effectiveness in the Arab
World. International Encyclopedia of Education , pp. 521-535.
Alavi, M. (1994). Computer-Mediated Collaborative Learning: An Empirical Evaluation. MIS Quarterly,
18(2), 159-174.
Alavi, M. & Leidner, D.E. (2001). Technology mediated learning: a call for greater depth and breadth of
research. Information Systems Research, 12(1), 1-10.
Albadvi, A., Keramati A. & Razmi, J. (2007). Assessing the impact of information technology on firm
performance considering the role of intervening variables: organizational infrastructures and business
processes reengineering. International Journal of Production Research, 45(12), 2697–2734.
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA
courses. Journal of Management Education, 24(1), 32-54
Aydin, C. H. & Tasci, D. (2005). Measuring readiness for E-Learning: reflections from an emerging
country. Educational Technology & society, 8(4), 244-257.
Baeten, M., Kyndt, E., Struyven, K. & Dochy, F. (2010). Using student-centred learning environments to
stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness.
Educational Research Review, In Press.
Baroudi, J.J. & Orlikowski, W.J. (1989). The problem of statistical power in MIS research. MIS Quarterly,
13(1), 87-106.
Boyer, K.K., Leong, G.K., Ward, P.T. & Krajewski, L.J. (1997). Unlocking the potential of advanced
manufacturing technologies. Journal of Psychology Management, 15, 331–347.
Brush, T., Glazewski, K., Rutowski, K., Berg, K., Stromfers, C. & Van-Nest, M.H. (2003). Integrating
technology in a field-based teacher training program: The PT3@ASU project. Educational Technology
Research and Development, 51(1), 57–72.
Carswell, A.D., Agarwal, R. & Sambamurthy, V. (2001). Development and validation of media property
perception measures. paper presented at the Americas Conference on Information Systems, Boston, MA.
Chang, T.Y. & Chen, Y.T. (2009). Cooperative learning in E-Learning: A peer assessment of student-
centered using consistent fuzzy preference. Expert Systems with Applications, 36, 8342-8349.
Chao, R.J. & Chen, Y.H. (2009). Evaluation of the criteria and effectiveness of distance E-Learning with
consistent fuzzy preference relations. Expert Systems with Applications, 36, 10657-10662.
Chen, K. C. & Jang, S. J. (2010). Motivation in online learning: Testing a model of self-determination
theory. Computers in Human Behavior, 26, 741–752.
Chu, R.C. & Chu, A.Z. (2010). Multi-level analysis of peer support, Internet self-efficacy and e-learning
outcomes – The contextual effects of collectivism and group potency. Computers & Education, 55, 145-
154.
Cohen, E.B. & Nycz, M. (2006) Learning Objects and E-Learning: an Informing Science Perspective.
Interdisciplinary Journal of Knowledge and Learning Objects, 2, 23-34.
Colquitt, J.A., LePine, J.A. & Noe, R.A. (2000). Toward an integrative theory of training motivation: a
meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85 (5), 678-707.
Cooper, D.R. & Schindler, P.S. (2003). Business Research Methods, 8th ed., 23–240, (McGraw Hill: New
York).
Cukusic, M., Alfirevic, N., Granic, A. & Garaca, Z. (2010). E-Learning process management and the E-
Learning performance: Results of a European empirical study. Computers & Education, 55(2), 554-565.
Darab, B. & Montazer, G.A. (2011). An eclectic model for assessing e-learning readiness in the Iranian
universities. Computers & Education, 56(3), 900-910.
Fathian, M., Akhavan, P. & Hoorali, M. (2008). E-readiness assessment of non-profit ICT SMEs in a
developing country: The case of Iran. Technovation, 28(9), 578-590.
Flynn, B.B, Schroeder, R.G. & Sakakibara, S. (1994). A framework for quality management research and
an associated measurement instrument. Journal of Operation Management, 11, 339–366.
Franceschi, K., Lee, R.M., Zanakis, S.H. & Hinds,D. (2009). Engaging Group E-Learning in Virtual
Worlds. Journal of Management Information Systems, 26(1), 73-100.
Gattiker, U. E., & Hlavka, A. (1992). Computer attitudes and learning performance: Issues for management
education and training. Journal of Organizational Behavior, 13(1), 89–101.
Govindasamy, T. (2001). Successful implementation of E-Learning: Pedagogical considerations The
Internet and Higher Education, 4(3-4), 287-299.
Hogo, M.A. (2010). Evaluation of E-Learning systems based on fuzzy clustering models and statistical
tools. Expert Systems with Applications, In Press.
Huang, M. (2010). The Dynamic Effect of the Top Management Support and Departmental Manager
Knowledge on IT Application Maturity. Advanced Materials Research, 121-122, 769-774.
Johnson, R.D., Hornik, S.R. & Salas, E. (2008). An empirical examination of factors contributing to the
creation of successful e-learning environments. International Journal of Human-Computer Studies, 66,
356-69.
Johnson, R.D., Gueutal, H. & Cecilia, M.F. (2009). Technology, trainees, metacognitive activity and E-
Learning effectiveness. Journal of Managerial Psychology , 24(6), 545-566.
Kaur, K. (2004). An assessment of e-learning readiness at the Open University Malaysia, International
conference on computers in education.
Kidd, T. (2010). Online Education and Adult Learning: New Frontiers for Teaching Practices, Information
science reference, Hershey, New York.
Kim, C.J. (2005). Construction of E-Learning Environments in Korea. ETR&D, 53(4), 108-115.
Klein, A. & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the
LMS method. Psychometrika, 65, 457-474.
Lee, B.C., Yoon, J.O. & Lee, I. (2009). Learners’ acceptance of E-Learning in South Korea: Theories and
results. Computers & Education, 53(4), 1320-1329.
Liaw, S.S., Huang, H.M. & Chen, G.D. (2007). Surveying instructor and learner attitudes toward E-
Learning. Computers & Education, 49(4), 1066-1080.
Lim, H., Lee, S.G. & Nam, K. (2007). Validating E-Learning factors affecting training effectiveness.
International Journal of Information Management, 27(1), 22-35.
Machado, C. (2007). Developing an e-readiness model for higher education institutions: results of a focus
group study. British Journal of Educational Technology, 38(1).
Maki, R. H., Maki, W, S., Patterson, M. & Whittaker, P. D. (2000). Evaluation of a Web-Based
Introductory Psychology Course: L Learning and Satisfaction in On-Line Versus Lecture Courses.
Behavior Research Methods, Instruments, and Computers, 32(2), 230-239.
Means, B., Toyama, Y., Murphy, R., Bakia, M. & Jones, K. (2009). Evaluation of Evidence-Based
Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. U.S. Department
of Education, Washington, D.C.
Nagi, E., Poon, J. & Chana, Y. (2007). Empirical examination of the adoption of WebCT using TAM.
Computers & Education, 48(2), 250-267.
Nunnally, J.C. & Bernstein, I.H. (1994). Psychometric Theory, 3rd ed., 214–286, (McGraw Hill: New
York).
Olson, E.M., Slater, S.F. & Hult, G.T.M. (2005). The performance implication of fit among business
strategy, marketing organization structure, and strategic behavior. Journal of Marketing, 69, 49-65.
Papp, R. (2000). Critical success factors for distance learning. Paper presented at the Americas Conference
on Information Systems, Long Beach, CA, USA.
Piccoli, G., Ahmad, R. & Ives, B. (2001). Web-based virtual learning environments: a research framework
and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401-426.
Rhee, B., Verma, R., Plaschka, G.R. & Kickul, J.R. (2007). Technology Readiness, Learning Goals, and
eLearning: Searching for Synergy. Decision Sciences Journal of Innovative Education, 5(1), 127-149.
Schreurs, J., Sammour, G. & Ehlers, U. (2008). ERA - E-Learning Readiness Analysis: A eHealth Case
Study of E-Learning Readiness. WSKS, CCIS, 267–275.
Selim, H.M. (2007). Critical success factors for E-Learning acceptance: Confirmatory factor models.
Computers & Education, 49(2), 396-413.
Shaw, R.S. (2010). A study of learning performance of E-Learning materials design with knowledge maps.
Computers & Education, 54(1), 253-264
Schermelleh-Engel, K. & Moosbrugger, H. (2003). Evaluating the Fit of Structural Equation Models: Tests
of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online,
8(2), 23-74
Schutte, J. G. (1997). Virtual Teaching in Higher Education: The New Intellectual Superhighway or Just
Another Traffic Jam?, California State University, Northridge, CA.
Soong, B.M.H., Chan, H.C., Chua, B.C. & Loh, K.F. (2001). Critical success factors for on-line course
resources. Computers & Education, 36(2), 101–120.
Stonebraker, P.W. & Hazeltine, J.E. (2004). Virtual learning effectiveness. The Learning Organization,
11(2-3), 209-225.
Sun, P.C., Tsai, R.J., Finger, G., Chen, Y.Y. & Yeh, D. (2008). What drives a successful E-Learning?: An
empirical investigation of the critical factors influencing learner satisfaction. Computers & Education,
50(4), 1183-1202
Vaughan, K. & MacVicar, A. (2004). Employees’ pre-implementation attitudes and perceptions to E-
Learning: A banking case study analysis. Journal of European Industrial Training, 28(5), 400-413.
Wan, Z., Wang, Y. & Haggerty, N. (2008). Why people benefit from E-Learning differently: The effects of
psychological processes on E-Learning outcomes. Information & Management, 45(8), 513-521.
Wang, Q., Zhu, Z., Chen, L. & Yan, H. (2009). E-Learning in China. Campus-Wide Information systems,
26, 47- 61.
Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning.
Academy of Management Journal,. 40(6), 1282–1309.
Welsh, E.T., Wanberg, C.R., Brown, K.G. and Simmering, M.J. (2003). E-learning: emerging uses,
empirical results and future directions. International Journal of Training and Development, 7, 245-58.
Zhao, J., McConnell, D. & Jiang, Y. (2009). Teachers' conceptions of E-Learning in Chinese higher
education: A phenomenographic analysis. Campus-Wide Information Systems, 26(2), 23-35.
Zoraini, A. (2004). E-Learning Readiness: A Crucial Factor in Managing Teaching and Learning.
Education Management Through Technology Conference, Malaysia.
Figures list
Figure 1. Research’s conceptual model
Figure 2. Q-Q plots
Figure 3. Moderating effect model
Figure 4. Complete structural model
Tables list
Table1. Selected works on E-Learning outcomes
Table2. Respondents’ computer skills
Table3. Respondents’ educating background
Table4. Respondents’ web-based educating background
Table5. Reliability and validity analysis
Table6. Hierarchical regression results
Table7. Results of ranking different ELNG elements
Table8. Results of ranking different RF elements
Fig 1. Research conceptual model
E-Learning Factors
- Student
- Teacher
- IT
- Support
Readiness Factors
- Technical
- Organizational
- Social
Outcomes
- Teachers Progress
- Students Progress
- Access to instruction
Interaction
Fig. 3. Moderating effect model (Adapted from Albadvi et al. 2007)
Fig. 4. Complete structural model
0.726
0.497
0.101
1.365
1.00
0.818
0.888
1.506
1.00
0.814
1.00
ELNG
RF
ELNG*R
OC
0.123
Teachers Prog
Students Prog
Access
Student
Teacher
IT
Support 0.837
Technical
Organizational
Social
Table1. Selected works on E-Learning outcomes
Researcher(s) year Dependent variable (s) methodology results
Alavi 1994 Perceived skill, self-reported learning and grades
Evaluated effectiveness and
efficiency of computer-
mediated collaborative
learning by an empirical study
Technology-mediated learning
environments may improve
students' achievement
Schutte 1997 Students’ scores on exam Compared a traditional
classroom and a virtual
classroom by an experimental
research
Virtual class scored an average
of 20% higher than the
traditional one
Maki et al. 2000 Students’ scores on exam Evaluated the on-line format
relative to the traditional
lecture-test format, using a
pretest-posttest nonequivalent
control group design
The students in the on-line
sections expressed appreciation
for course components and the
convenience of the course, but
the lecture sections received
higher ratings on course
Piccoli et al. 2001 Performance (e.g. achievement, recall), self-efficacy and satisfaction.
Presented a framework of
Virtual Learning Environment
(VLE) effectiveness and
compared it to a traditional
classroom
Two classes of determinants
including human dimension and
design dimension affect VLE
effectiveness
Lim et al. 2007 Learning performance (to what degree the trainees learn?) and Transfer performance (how well the trainees applied what they learned to their job tasks)
Proposed a model on variables affecting E-Learning performance and confirmed it using an empirical study
There is a positive relationship between individual, organizational and online training design constructs and training effectiveness constructs
Wan et al. 2008 Learning effectiveness and satisfaction
Using a survey, confirmed
their hypothesizes which are
asserted that prior experience
with ICT and virtual
competence are two
significant factors that
affected E-Learning outcomes
Virtual competence and prior
experience with ICT affect E-
Learning outcomes
Johnson et al. 2009 Perception of course utility, course satisfaction and course grade as E-Learning outcomes
Proposed a model of variables
influencing E-Learning
outcomes and assessed it by a
survey
Technology characteristics,
trainee characteristics and
Metacognitive activity affect E-
Learning outcomes
Chu & Chu 2010 Perceived learning, persistence and satisfaction
A survey to prove a proposed model to evaluate E-Learning outcomes for adult learners
Internet Self-Efficacy fully
mediates the relationship
between peer support and E-
Learning outcomes.
Chen & Jang 2010 Engagement, Achievement, Learning and satisfaction
Proposed and tested a model for the effect of online learner motivation and contextual support on online learning outcomes
The direct effect and indirect effects of contextual support exerted opposite impacts on learning outcomes
Table2. Respondents’ computer skills
I don’t use computer at all I am familiar with computer I am a professional user of computer
Percentage %5 %75 %20
Table3. Respondents’ educating background
0-5 (yrs) 5-10 (yrs) 10-15 (yrs) 15-20 (yrs) 20-25 (yrs) 25-30 (yrs)
Percentage %13 %09 %18 %25 %21 %14
Table4. Respondents’ web-based educating background
1 year 2 years 3 years 4 years
Percentage %54 %25 %12 %9
Table5. Reliability and validity analysis
Reliability Analysis Descriptive results Factor Analysis
Variable No. of questions
Cronbach’s Alpha
N Mean Std. Deviation
Extraction Eigenvalue KMO and Bartlett’s sig
% of Variance
E-Learning 15 0.700 92 3.549 0.504 0.607 2.982 0.000 61.96
Readiness factors
13 0.828 87 3.498 0.506 0.624 4.864 0.000 19.45
Outcomes 8 0.732 92 3.522 0.562 0.627 2.944 0.000 18.58
Table6. Hierarchical regression results
t-test
F-test
Step Measurement Scale B Statistics Sig. R Square ∆R
square Statistics Sig.
Technical: TECH
1 TECH 0.374 1.974 0.000 0.099 0.099 8.240 0.000
2 ELNG 0.467 4.238 0.000 0.182 0.083 9.472 0.000
3 TECH*ELNG 0.711 4.564 0.000 0.345 0.163 14.73 0.000
Organizational: ORG
1 ORG 0.515 4.735 0.000 0.209 0.209 22.418 0.000
2 ELNG 0291 2.409 0.018 0.260 0.051 14.744 0.000
3 ORG*ELNG 1.114 7.111 0.000 0.540 0.280 32.486 0.000
Social: SOC
1 SOC 0.236 4.802 0.000 0.159 0.158 16.659 0.000
2 ELNG 0.366 3.348 0.001 0.255 0.096 14.920 0.000
3 SOC*ELNG 0.297 2.998 0.021 0.264 0.009 10.266 0.000
Readiness factors: RF
1 IST 0.491 4.522 0.000 0.196 0.196 20.451 0.000
2 ELNG 0.323 2.789 0.007 0.265 0.069 14.940 0.000
3 RF*ELNG 6.046 1.018 0.000 0.491 0.227 26.409 0.000
Table7. Results of ranking different ELNG elements
Rank E-Learning element Total effect
1 Teacher 0.900
2 Student 0.598
3 IT 0.531
4 Support 0.489
Table8. Results of ranking different RF elements
Rank Readiness factors element Total effect
1 Organizational 0.137
2 Technical 0.101
3 Social 0.073
Appendix 1: Questionnaire
Based on your valuable experiences in using E-Learning, please indicate the extent to which each
following element affects E-Learning outcomes, ranging from 1 to 5: (1 = not at all, to 3 = moderate effect,
to 5 = extreme effect)
Question Not at
all
1
2
3
4
Extreme
5
Readiness factors
1- Appropriate hardware in school
2- Appropriate software in school
3- Appropriate course content
4- The speed of internet
5- Proper school space for E-Learning system
6- Cultural readiness for change in learning style
7- Adequate skilled employees in E-Learning
8- Appropriate organizational rules
9- Managerial readiness to implement the system
10- Sufficient budget and investment in E-Learning
11- society’s conception of E-Learning
12- Appropriate governmental rules and regulations
13- Appropriate administrative recipe
Question Not at
all
1
2
3
4
Extreme
5
E-Learning Factors
14- Teachers’ motivation for using E-Learning
15- Teachers training for using this system
16- Teachers’ attitude toward the technology
17- Teachers’ teaching style
18- Students attitude toward E-Learning
19- Students motivation to use this system
20- Students ability to use a computer to display or
present information in a desired manner
21- Existence of required IT experts in school
22- The possibility of interact with classmates
through the web
23- Good design of website
24- Well structured/presented information
25- The possibility of registering courses on-line
26- Top management knowledge about advantages of
the system
27- Top management support
28- Top management commitment
Please indicate the extent to which using E-Learning system affects each following criterion
Question Not at all
1
2
3
4
Extreme
5
1- Enhancing students effectiveness in learning
2- Improving students satisfaction with the course
3- Enhancing students productivity
4- Enhancing teachers effectiveness in educating
5- Improving teachers satisfaction with the educating
environment
6- Enhancing teachers productivity
7- Access to instruction for all
8- Build an advanced society for citizens to support
creativity and innovation