The Application of Structural Equation Modeling Technique to Analyze Students Priorities in Using Course Management Systems

The objective of this paper is to report on the application of Structural Equation Model (SEM) to analyze factors that influence students‘ priorities when selecting a Course Management System (CMS). A Conformation Factor Analysis (CFA) was performed to test the reliability and validity of the measurement model. The study is motivated by the inconsistencies, duplication and loss of integrity of data caused by simultaneous usage of two CMS-WebCT and Electronic Campus (EC) e-learning tools in the faculty of Information and Communication Technology (ICT) at Tshwane University of Technology. A composite model of Diffusion of Innovations (DOI) theory and Technology Acceptance Model (TAM) was used to predict actual selection of CMS when mediated by prioritization. Results indicated that the complexity of WebCT negatively influences students‘ prioritization, whereas perceived ease of use and less complexity of EC drives them towards its selection. This paper provides an insight for antecedent factors essential for planning and implementing CMSs. The developed framework is expected to act as a guide for university administrators in making informed decision about investing in e-learning tools.

[1]  In Lee,et al.  Learners' acceptance of e-learning in South Korea: Theories and results , 2009, Comput. Educ..

[2]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[3]  Sung Youl Park,et al.  An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning , 2009, J. Educ. Technol. Soc..

[4]  Raafat George Saadé,et al.  Understanding Intention to Use Multimedia Information Systems for Learning , 2005 .

[5]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[6]  Robert Andersson,et al.  Examining user acceptance of computer technology: an empirical study of student teachers , 2005, J. Comput. Assist. Learn..

[7]  Lori Rosenkopf,et al.  INSTITUTIONAL AND COMPETITIVE BANDWAGONS: USING MATHEMATICAL MODELING AS A TOOL TO EXPLORE INNOVATION DIFFUSION , 1993 .

[8]  Brett J. L. Landry,et al.  Measuring Student Perceptions of Blackboard Using the Technology Acceptance Model , 2006 .

[9]  Mark T. Dishaw,et al.  Extending the Technology Acceptance Model , 1997 .

[10]  Richard,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace , 2022 .

[11]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[12]  Julie F. Pallant,et al.  SPSS Survival Manual , 2020 .

[13]  Jane M. Howell,et al.  Personal Computing: Toward a Conceptual Model of Utilization , 1991, MIS Q..

[14]  Akihito Kamata,et al.  Reliability for Multidimensional Scale 1 Estimating Reliability for Multidimensional Composite Scale Scores , 2003 .

[15]  Jeff Seaman,et al.  Sizing the opportunity: the quality and extent of online education in the united states , 2003 .

[16]  Peter A. Todd,et al.  Assessing IT usage: the role of prior experience , 1995 .

[17]  Khaled A. Alshare,et al.  Factors influencing student intention to adopt online classes: a cross-cultural study , 2005 .

[18]  Raafat George Saadé,et al.  Viability of the "Technology Acceptance Model" in Multimedia Learning Environments: A Comparative Study , 2007 .

[19]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[20]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[21]  Mahesh S. Raisinghani,et al.  Strategic Decision Making: A Framework For Multicriteria Decision Analysis Of Technology Investments And A Field Survey , 1997 .

[22]  Nelson Oly Ndubisi,et al.  Factors of Online Learning Adoption: A Comparative Juxtaposition of the Theory of Planned Behaviour and the Technology Acceptance Model. , 2006 .

[23]  Richard J. Whiddett,et al.  A Model of User Acceptance of E-learning Technologies: a Case Study of a Polytechnic in New Zealand , 2005, ISTA.

[24]  William K. Horton,et al.  E-learning Tools and Technologies: A consumer's guide for trainers, teachers, educators, and instructional designers , 2003 .

[25]  I. Ajzen.  The theory of planned behavior , 1991 .

[26]  Fethi Calisir,et al.  Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry , 2011, Comput. Hum. Behav..

[27]  En Mao,et al.  AN INVESTIGATION OF IT USAGE OVER TIME , 2002 .

[28]  M. Häfner,et al.  Sizing Opportunity , 2013 .

[29]  Karl G. Jöreskog,et al.  Lisrel 8: Structural Equation Modeling With the Simplis Command Language , 1993 .