Students’ Attitudes Toward E-learning in Iranian universities Dr Faranak omidian Islamic Azad University, Dezfool, IRAN Abdolhossen omidian Reasearch Scholar, Panjab University, INDIA E-learning is becoming increasingly prominent in higher education, with universities increasing provision and more students signing up. This paper examines factors that predict students' attitudes to adopt elearning at the Khuzestan province iran . Data was collected through a survey of 400 post graduate students at the University of Tehran, yazd , isfahan , khoramabad , dezful , shooshtar , chamran, from different faculties .The technology adoption model put forward by Davis is utilized in this study. Two more independent variables are added to the original model, namely, resources availability and e-learning stressors . The results show that there are four factors that can be used in modeling students' attitudes to adopt e-learning. These factors are intention toward e-learning, perceived usefulness of e-learning, elearning stressors , and the availability of resources needed to use e-learning. Keywords : e-learning , adoption , intention , ease of use, , usefulness, Introduction Technology is challenging the boundaries of the educational structures that have traditionally facilitated learning .Recent advances in computer technology and the diffusion of personal computers , productivity software , multimedia and network resources over the last decade ,heralded the development and implementation of new and innovative teaching strategies. Educators who advocate technology integration in the learning process believe it will improve learning and better prepare students to effectively participate in the 21 century workplace (Butzin ,2000 ; Hopson , Simms & Knezek , 2002 ) . The information society requires the use of technology enhanced learning and teaching at different educational forms and levels . In today’s global and competitive environment which is marked by the coming of information society , using the technologies of e-learning becomes a widely accepted way of training because of the flexibility and the standardization of the overall educational process they offer . The concept of e-learning is described in a lot of literature sources . The basics of these definitions of e-learning are combination , implementation and relationship of the activities for learning and teaching via different electronic media such as in distance and open learning , etc (Tuparova ,Tuparov , Karastranova and Peneva ,2006 ) . As bates (2001) argues , e-learning is being used in three main ways in universities and colleges : 1) Technology- enhanced classroom : First , the web and the internet have been integrated into classroom teaching in the same way as previous technologies . Teachers may build a course Webpage , with links through the internet to relevant resources on other Websites . Instructors can convert their 1 powerpoint slide presentations to pdf files . (electronic documents) , which students can download and print from a website , or teacher’s own papers and research materials such as photographs or slides , as well as links to other relevant sources . Teachers may also use other web sites for illustration within their classroom lectures . And students may be asked to participate in on-line discussion forums , to discuss the lecture afterwards amongst themselves . Enhancing classroom teaching is still by far the most prevalent use of the web in post – secondary education . 2) Distance education : Major and highly reputable universities with large on-campus teaching programs , such as Queen University in Canada , the University of London in the United Kingdom, and the University of Wisconsin in the USA , have been offering distance education programs for over 100 years . Institutions such as these that offer both campus-based and distance education programs are called “ dual – mode “.These institutions have tended to develop a distance education operation to complement their on – campus teaching . For such institutions , extension beyond the campus has been critical to their mandate , and hence distance education is one of the several means used to reach out to farmers , working professionals , and those who cannot afford to move to a campus away from their homes or jobs . In contrast to the dual – mode institutions , Daniel (1998) has described the large , dedicated distance education institutions (single – mode ) such as the British open university . These are characterized by very large enrolments ( usually more than 100,000 students ) , and the use of mass communications technology , such as print and broadcasting . 3) Distributed learning : With both technology –enhanced classroom teaching and distance education , the move to on-line learning could be seen as evolutionary , a natural next step forward in two long but separate historical processes .The potentially revolutionary development is in distributed learning , because this will radically change the way that traditional campus institutions operate . Distributed learning describes a mix of deliberately reduced face- toface teaching and on-line learning (for instance one face- to- face lecture or seminar a week , with the rest of the teaching and learning done on –line , replacing the traditional three face -to- face lectures a week ) . Unfortunately , especially in the USA , the term “ distributed learning “ is also commonly used to include fully distance courses taught totally on – line . It might be more helpful to describe the mix of reduced face – to – face teaching and on-line teaching as “mixed mode “. Another term , used in Australia , is flexible learning .While “flexible learning “ may encompass on-line learning , it can also include face – to-face teaching delivered in the workplace , and other flexible delivery methods (Bates ,2001) . However , despite the growing technology in higher education several recent studies (Link and Marz,2oo6 ; Hayashi , Chen , Ryan and Wa ; 2006 ) have advocated that universities have been slow to bring computer use , e-learning , on line learning into the main stream and maximize the potential benefits in the classroom . They discovered that failing to acknowledge the importance of understanding e-learning was an important 2 issue . Many students may lack the necessary skills to use e-learning effectively and are therefore handicapped . Yet colleges and universities continue to invest large sums of money in automation and electronic communication facilities . For this reason , Martinze (2004) suggests that the study of student’s attitude towards e-learning can in many ways help managers better prepare in light of e-learning for the future . Asan and Koca (2006) reveal there is a relationship between students attitude towards e-learning and positive learning outcomes . Perez Cereijo (2006) proposes that students attitude towards elearning provides a beneficial construct to predict learning outcomes . , The theory of technology acceptance model was really designed to test user’s attitude to accept new technology . This theory proposed by davis (1983) explains a variety of human behaviors based on intentions are jointly determined by attitudes . (figure 1) . According to TAM , perceived usefulness and perceived ease of use shape an individuals attitudes for using the technology . Perceived usefulness (PU) Attitude toward USING(A) Behavioral intention (BI) Perceived ease of use (PEOU) (Figure 1 ) Actual use ( Based on Davis 1989 ) Davis (1989) defined perceived usefulness (PU) as the degree to which a person believes that 0 using a particular system would enhance believes that using a particular system would enhance his or her job performance . He defined perceived ease of use as the degree to which a person believes that that using a particular system would be free of effort . (Halawi and McCarthy . 2009 ). In addition , the original TAM has since been extended and is recognized today as TAM2 (figure 2 ). Davis (1993) mainly suggests that added external variables be utilized in future research using TAM (halawil , mccarthv ; 2007 ) . A substantial amount work has been done investigating a variety of external factors may be important determinants of the user’s attitude ( jones , 2005 ; sam, Othman, Nordin 2005 ; Rezaei , Mohammadi ; Asadi and kalantary , 2008 ) .Hence , the 0 technology acceptance model gives relevance to the current study which investigates the factors that affect student’s attitude to adopt e-learning . Specifically , What is the best group of factors that can be used in predicting students’ attitudes to adopt e-learning ? 3 Availability of Resources Age Gender Perceived usefulness Pressured To use Attitude toward External Factors using elearning Perceived ease of use Computer self -efficacy Technology Acceptance ModeL2 ( TAM) 2 (Based on Davis 1989) ( Cited in Halawi ,mccarthy ; 2007) ( Figure 2 ) METHOD Designing the instrument To answer the question of the study , a 79 –item scale in 5-point Likert format was developed to gain as much information as possible regarding the factors that affect students ‘attitude to adopt e-learning . Table 2 shows the reliability of the measurement scale . The results demonstrate that the questionnaire is a reliable measurement instrument . 4 Table 1 Cronbach’s alpha Scale Perceived usefulness of e-learning 0.87 Intention to adopt e-learning 0.75 Ease of e-learning use 0.70 Availability of resources 0.70 E-learning stressors 0.79 Attitude towards e-learning o.78 survey sample Stratified sampling technique employed in the present study . 400 post graduate students at the University of Tehran, yazd , isfahan , khoramabad , dezful , shooshtar , chamran, from different faculties formed the sample of the study (Table 2). Table 2 Sample details Arts Science Department Number Department Education 40 Computer science 40 Mass communication 40 Biotechnology 40 Geography 40 Statistic 40 Psychology 40 Physic 40 Political science 40 Chemistry 40 5 Number Personal characteristics of respondents Approximately 56%of students who participated in the study here between 19 to 25 years , about 31% were between 26 to 30 and only 14% more than 30 years . 53%of respondents were male and 47% were female . Analyses of Data In order to test the research questions data analysis were made throughout the SPSS version 17 . To answer question 1 . A multiple regression model was computed to create a regression equation to answer the research question : what is the best groups of factors that can predict students’ attitudes to adopt e-learning ? The model included six predictor variables(Perceived usefulness of e-learning , Intention to adopt e-learning , Ease of elearning use , availability of resources , E-learning stressors ) simultaneously to determine the joint effect (attitude towards e-learning ) of these variables . Results and analysis : A multiple regression equation was computed to distinguish whether student’s attitude towards e-learning can be predicted by factors ( Perceived usefulness of e-learning , Intention to adapt e-learning , Ease of e-learning use , availability of resources , Elearning stressors ) . Table 3 reveals that the R2 value for this dataset was.706. This indicated that %70/6 of the student’s attitude toward e-learning was explained by the independent variables of above mentioned . figure 2 confirms this results. The statistical significance of the predication equation was analyzed by looking at the ANOVA(Table 3 ) .The data showed significance at the P<.000 level (F=189.414) . Table 3 Analyses of variance : Regression Sum of squres df Mean F Sig R Square Adjusted Square 21884.318 5 4376.864 189.414 Regression 6 R square .000a .706 .702 Residual Total 9104.322 394 30988.640 399 23.107 a. Predictors: (Constant) , intention to use ,e-learning stressors , ease of e-learning use, availability of resources , perceived use of e-learning b. Dependent Variable: total attitude mark Table 2 also shows that the most useful subset of variables that can be used in predicting students’ attitude towards e-learning includes Usefulness (U) , Intention (I) Ease of Use (EOU) , E-learning stressors (ES), availability of recourses (A) . Accordingly , the best regression model that can be used in predicting student’s attitude towards e-learning is: Attitude = -11.402 +.730 I+ .088 U + .080. (ES) + -.079 (A) + .023 (EOU) Table 4 multiple regression analysis : Coefficients Un standardized Variables Standardized t P Coefficients (sig) Coefficients B Std. Error Beta (Constant) 11.40 2 Perceived usefulness of .053 2.625 -4.344 .000 .021 .088 2.565 .011 .570 .033 .730 17.195 .000 .031 .051 .023 .605 .545 of -.134 .047 -.079 -2.827 .005 e-learning Intention to adopt e-learning Ease of e-learning use Availability resources 7 E-learning stressors .096 .035 .080 2.752 .006 Discussion and conclusion : The results show that the best subset of predictors that can be used in modeling student’s attitude to adopt e-learning includes : intention to adopt e-learning, , perceived of Usefulness, e-learning stressors and availability of resources . factors that are used to model e-learning explain %70/6 of the variation of the dependent variable (attitude to adapt e-learning ). Hence , Program managers can focus on these factors that are expected to affect student’s attitude to adapt e-learning . On the other hand Further studies should be carried out to explore more variables that can be used to get better insight into the research questions . 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