E learning 2011
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This document summarizes a study that examines the role of readiness factors in E-learning outcomes. The study proposes a conceptual model to determine how readiness factors moderate the relationship between E-learning factors (such as student, teacher, IT, and support factors) and E-learning outcomes. A survey was administered to 96 teachers to test the model. Hierarchical regression and latent moderated structural equation modeling found that organizational readiness factors had the strongest effect on outcomes. Overall, the findings support that readiness factors play an important intervening role in the relationship between E-learning implementation factors and outcomes.
E learning 2011
- 1. Computers & Education 57 (2011) 1919–1929 Contents lists available at ScienceDirect Computers & Education journal homepage: www.elsevier.com/locate/compedu The role of readiness factors in E-learning outcomes: An empirical study Abbas Keramati a, *, Masoud Afshari-Mofrad b, Ali Kamrani a a Industrial Engineering Department, University of Tehran, P.O. Box 11155-4563, Tehran, Iran b Information Technology Management Department, Tarbiat Modares University, P.O. Box 14115-111, Tehran, Iran a r t i c l e i n f o a b s t r a c t Article history: Although many researchers have studied different factors which affect E-Learning outcomes, there is Received 6 October 2010 little research on assessment of the intervening role of readiness factors in E-Learning outcomes. This Received in revised form study proposes a conceptual model to determine the role of readiness factors in the relationship between 19 March 2011 E-Learning factors and E-Learning outcomes. Readiness factors are divided into three main groups Accepted 13 April 2011 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. Keywords: Hierarchical regression analysis is done and its results strongly support the appropriateness of the E-Learning Readiness factors proposed model and prove that readiness factors variable plays a moderating role in the relationship Outcomes between E-Learning factors and outcomes. Also latent moderated structuring (LMS) technique and Evaluation methodologies MPLUS3 software are used to determine each variable’s ranking. Results show that organizational Iran 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. Ó 2011 Elsevier Ltd. All rights reserved. 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, Lee, Zanakis, & Hinds, 2009). It is implementing as a new method of training which complements traditional methods (Vaughan & MacVicar, 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, Yoon, & Lee, 2009). Advantages like cost reduction, elimination of time and space constraint and assisting the traditional instruction, make it important and popular (Chao & Chen, 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 & Nycz, 2006). The growth rate of E-Learning market is about 35.6% (Sun, Tsai, Finger, Chen, & Yeh, 2008) and recent researches have shown that only in America, nearly $40 billion is spent annually on technology-based training (Johnson, Gueutal, & Cecilia, 2009). According to such a heavily investing in this system, it is essential to evaluate its different aspects and understand factors which influence its effectiveness (Schreurs, Sammour, & Ehlers, 2008). Success in implementing and using this system is crucial because an unsuccessful attempt will be clearly revealed in terms of the return of investment (Govindasamy, 2001). One of the most important variables which can have a critical effect on E-Learning successful outcomes is readiness factor and schools have to improve and upgrade their readiness to use this system (Wang, Zhu, Chen, & Yan, 2009). Although there are many researches evaluating different E-Learning aspects, there is little research on assessment of the intervening role of readiness factors in E-Learning outcomes. To overcome this shortcoming, in this study the role of readiness factors in the relationship between E-Learning factors and outcomes is assessed. E-Learning readiness factors are divided into three main categories including: technical, organizational and social factors. The following two main questions of this research are considered: Do readiness factors have a moderating effect on the relationship between E-Learning factors and E-Learning outcomes? How much is the moderating effect for different aspects of readiness factors (which aspects are more important)? * Corresponding author. E-mail addresses: keramati@ut.ac.ir (A. Keramati), m.afshari@ut.ac.ir (M. Afshari-Mofrad), kamrani.ali@gmail.com (A. Kamrani). 0360-1315/$ – see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.compedu.2011.04.005
- 2. 1920 A. Keramati et al. / Computers Education 57 (2011) 1919–1929 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, Alfirevic, Granic, Garaca, 2010). Many researchers have studied several aspects of E-Learning and many different approaches were adopted (AbuSneineh Zairi, 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, Wang, Haggerty, 2008). Despite these researches, some other researchers demonstrated that E-Learning courses still have shortcomings and most E-Learning courses have high drop out rate (Means, Toyama, Murphy, Bakia, Jones, 2009; Stonebraker Hazeltine, 2004; Welsh, Wanberg, Brown, Simmering, 2003). 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 short- comings 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; Chen Jang, 2010; Johnson, Hornik, Salas, 2008; Piccoli, Ahmad, Ives, 2001). 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. 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, Akhavan, and Hoorali (2008) have evaluated e-readiness factors in Iran’s environment. They have extracted organiza- tional 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 char- acteristics 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 Table 1 Selected works on E-learning outcomes. Researcher(s)/year Dependent variable (s) Methodology Results Alavi (1994) Perceived skill, self-reported Evaluated effectiveness and efficiency Technology-mediated learning learning and grades of computer-mediated collaborative environments may improve learning by an empirical study students’ achievement Schutte (1997) Students’ scores on exam Compared a traditional classroom and Virtual class scored an average of a virtual classroom by an experimental 20% higher than the traditional one research Maki, Maki, Patterson, Students’ scores on exam Evaluated the online format relative to The students in the online sections and Whittaker (2000) the traditional lecture-test format, using expressed appreciation for course a pretest-posttest nonequivalent control components and the convenience group design of the course, but th e lecture sections received higher ratings on course Piccoli et al. (2001) Performance (e.g. achievement, Presented a framework of Virtual Learning Two classes of determinants including recall), self-efficacy and satisfaction. Environment (VLE) effectiveness and human dimension and design compared it to a traditional classroom dimension affect VLE effectiveness Lim et al. (2007) Learning performance (to what degree Proposed a model on variables affecting There is a positive relationship the trainees learn?) and Transfer performance E-Learning performance and confirmed between individual, organizational (how well the trainees applied what they it using an empirical study and online training design constructs learned to their job tasks) and training effectiveness constructs Wan et al. (2008) Learning effectiveness and satisfaction Using a survey, confirmed their hypothesizes Virtual competence and prior which are asserted that prior experience experience with ICT affect with ICT and virtual competence are two E-Learning outcomes significant factors that affected E-Learning outcomes Johnson et al. (2009) Perception of course utility, course satisfaction Proposed a model of variables influencing Technology characteristics, trainee and course grade as E-Learning outcomes E-Learning outcomes and assessed it by a survey characteristics and Metacognitive activity affect E-Learning outcomes Chu and Chu (2010) Perceived learning, persistence and satisfaction A survey to prove a proposed model to Internet Self-Efficacy fully mediates evaluate E-Learning outcomes for adult learners the relationship between peer support and E-Learning outcomes. Chen and Jang (2010) Engagement, Achievement, Proposed and tested a model for the effect The direct effect and indirect effects Learning and satisfaction of online learner motivation and contextual of contextual support exerted support on online learning outcomes opposite impacts on learning outcomes
- 3. A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1921 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, Keramati, and Razmi (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 Fig. 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 et al., 2001). Especially in distance education, teachers’ conception of E-Learning and its usefulness plays a vital role (Zhao, McConnell, Jiang, 2009) and their positive attitude toward using this system as a teaching assisted tool is required for E-Learning success (Liaw, Huang, Chen, 2007). Previous research has shown that three instructor characteristics affect E-Learning success: attitude, teaching style and IT competency (Webster Hackley, 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, Kyndt, Struyven, Dochy, 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, Lee, Nam, 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 possi- bility 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, Chan, Chua, Loh, 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 et al., 2009). Prior research has proved that technical readiness is one of the most important factors influencing E-Learning outcomes (Brush et al., 2003) and it is crucial to match the E-Learning Factors - Student - Teacher - IT - Support Outcomes Interaction Teachers Progress Students Progress Access to instruction Readiness Factors - Technical - Organizational - Social Fig. 1. Research’s conceptual model.
- 4. 1922 A. Keramati et al. / Computers Education 57 (2011) 1919–1929 Table 2 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 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, Agarwal, Sambamurthy, 2001). Several models have proposed to examine E-Learning outcomes (Cukusic et al., 2010; Johnson et al., 2009; Piccoli et al., 2001; Wan et al., 2008;). 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, LePine, NOE, 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. Fig. 1 depicts the conceptual model of this research. 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 Orlikowski, 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. Demo- graphic information of respondents are presented in the following tables (Tables 2–4). 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. Table 3 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
- 5. A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1923 Table 4 Respondents’ web-based educating background. 1 Year 2 Years 3 Years 4 Years Percentage %54 %25 %12 %9 Questions of the first two factors are taken from similar works like: Gattiker and Hlavka (1992), Liaw et al. (2007) and Webster and Hackley (1997). Questions of the last two factors are customized from Albadvi et al. (2007) and Sun et al. (2008). 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, Poon, and Chana (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 Sun et al. (2008), and Webster and Hackley (1997). 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 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 Bernstein, 1994). Construct validity shows the extent to which measures of a criterion are indicative of the direction and size of that criterion (Flynn, Schroeder, Sakakibara, 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, Slater, Hult, 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. Content validity indicates meeting the specific range of contents that have been selected (Nunnally 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 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 Bernstein, 1994). This validity is only possible through correlation between the predictor (independent variable) and criterion (dependent) variable (Nunnally Bernstein, 1994). In this study, the results of two-variable and multi-variable correlation between Table 5 Reliability and validity analysis. Variable Reliability analysis Descriptive results Factor analysis 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
- 6. 1924 A. Keramati et al. / Computers Education 57 (2011) 1919–1929 Fig. 2. Q-Q plots.
- 7. A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1925 “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 ¼ 0.085, Readiness factors ¼ 0.917 and Outcomes ¼ 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. Fig. 1 indicates these plots. This test confirms that all variables have normal data (Fig. 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. Fig. 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. 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. 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, Leong, Ward, and Krajewski (1997) argue 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 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 rela- tionship 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 Moosbrugger, 2003). In this study, to run LMS technique, MPLUS3 software is used. 4.4.4. Ranking of elements A rank for each element is determined considering readiness factor variables as moderating factor (Fig. 4). 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 ¼ 1:00*0:497 þ 1:00*0:101 ¼ 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. Fig. 3. Moderating effect model.
- 8. 1926 A. Keramati et al. / Computers Education 57 (2011) 1919–1929 Table 6 Hierarchical regression results. Step t-Test F-test Measurement scale B Statistics Sig. R Square DR 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 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. In a similar procedure, different readiness factor (RF) elements are ranked based on their moderating effects. Table 8 shows the results. 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, Verma, Plaschka, and Kickul (2007) have assessed technological readiness for implementing an effective E-Learning. Zoraini (2004) has mentioned some orga- nizational 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 et al. (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). Student 1.00 Teacher 1.506 Teachers Prog 1.00 0.497 IT 0.888 0.814 Students Prog ELNG OC 0.818 Support 0.837 Access 0.101 0.123 ELNG*R Technical 1.00 Organizational 1.365 RF 0.726 Social Fig. 4. Complete structural model.
- 9. A. Keramati et al. / Computers Education 57 (2011) 1919–1929 1927 Table 7 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 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 MPLUS3 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. Table 8 Results of ranking different RF elements. Rank Readiness factors element Total effect 1 Organizational 0.137 2 Technical 0.101 3 Social 0.073
- 10. 1928 A. Keramati et al. / Computers Education 57 (2011) 1919–1929 Acknowledgement The authors would like to thank the reviewers for their constructive and invaluable comments. The authors also would like to declare their appreciation to University of Tehran for the financial support for this study (Grant Number 7314812/1/03). 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 ¼ not at all, to 3 ¼ moderate effect, to 5 ¼ 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 online 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 References AbuSneineh, W., Zairi, M. (2010). An evaluation framework for E-learning effectiveness in the Arab World. International Encyclopedia of Education, 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, 5(3), 243–260. 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.
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