Article Sidebar
Submitted
May 12, 2018
Accepted
February 12, 2019
Published
January 30, 2019
Download
Statistic
Read Counter :
730
Download : 474
Downloads
Download data is not yet available.
Main Article Content
Abstract
The usage of Learning Management System (LMS) conducted in Blended Learning style is believed to increase students' academic performance and their self-directed learning. Nonetheless, the success of students' behavioural intention to use these e-learning platforms still remains unclear due to factors like self-directed learning, computer self-efficacy, satisfaction and perceived enjoyment and ESL. This preliminary study aims to incorporate factors that impact students' self-directed learning of English language in achieving behavioural intention to use LMS with an extended model namely, SDLTAM, generalised for the Malaysian educational institutions. The original Technology Acceptance Model 1 by Davis (1989) was used as a theoretical framework of this study. However, the last variable Actual Use was excluded in this study. A sample of 338 respondents from both private and public universities in Malaysia took part in the 48-items survey. The data were analysed through Structural Equation Modelling through SPSS AMOS 24. The SEM AMOS revealed that the factors were found to moderately fit into the proposed model. This could be misspecification within the model and some items within a factor were more correlated to each other than others.
Article Details
How to Cite
Ong, C. Y. F. S. (2019). Malaysian Undergraduates’ Behavioural Intention to Use LMS: An Extended Self-Directed Learning Technology Acceptance Model (SDLTAM). Journal of ELT Research: The Academic Journal of Studies in English Language Teaching and Learning, 4(1), 8–25. https://doi.org/10.22236/JER_Vol4Issue1pp8-25
References
-
Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888.
Aaker, D. A. (1995). Kumar, V. & Day, G. Marketing Research. New York: John Wiley & Sons, Inc.
Al-Adwan, A., & Smedley, J. (2013). Exploring students' acceptance of e-learning using Technology Acceptance Model in Jordanian universities Amer Al-Adwan Applied Science University, Jordan. International Journal of Education and Development using Information and Communication Technology, 9(2), 4-18.
Al-Aulamie, A. (2013). Enhanced technology acceptance model to explain and predict learners' behavioural intentions in learning management systems.
Annuar, N., & Shaari, R. (2014). The Antecedents toward Self-directed Learning among Distance Learner in Malaysian Public Universities. Proceeding of the Global Summit on Education, 195-205.
Baleghi-Zadeh, S., Ayub, A. F. M., Mahmud, R., & Daud, S. M. (2017). The influence of system interactivity and technical support on learning management system utilization. Knowledge Management & E-Learning, 9(1), 50.
Cigdem, H., & Ozturk, M. (2016). Factors affecting students' behavioral intention to use LMS at a Turkish post-secondary vocational school. The International Review of Research in Open and Distributed Learning, 17(3).
Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: An extension of the technology acceptance model. Group Decision and Negotiation, 11(2), 87-100.
Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119-143.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
Grover, K. S., Miller, M. T., Swearingen, B., & Wood, N. (2014). An examination of the self-directed learning practices of ESL adult language learners. Journal of Adult Education, 43(2), 12.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall.
Hajiyev, J., & Chang, C.-T. (2017). Gen Y Membersí¢ í‚ í‚ Mobile Banking Adoption Intention and Actual Use in Azerbaijan and Turkey: The Technology Acceptance Model and Social Cognitive Theory Approach. The Journal of Internet Banking and Commerce, 1-33.
Hsia, J.-W. (2007). An enhanced technology acceptance model for e-learning systems in high-tech companies. Paper presented at the Proceedings of the 7th WSEAS international conference on Distance learning and web engineering.
Hsu, C.-K., Hwang, G.-J., & Chang, C.-K. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327-336.
Hu, L. T., Bentler, P. M., & Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues, and applications. Evaluating model fit, 76-99.
Hutasuhut, I. J., Adruce, S. A. Z., & Bujang, S. (2018). An Understanding of Work-Related Self-Directed Learning. Sarawak Library Journal: Publication of Pustaka Negeri Sarawak, 1(1), 10-10.
Ifinedo, P., Pyke, J., & Anwar, A. (2018). Business undergraduates' perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support. Telematics and Informatics, 35(1), 93-102.
Idros, S. N. S., Mohamed, A. R., Esa, N., Samsudin, M. A., & Daud, K. A. M. (2010). Enhancing self-directed learning skills through e-SOLMS for Malaysian learners. Procedia-Social and Behavioral Sciences, 2(2), 698-706.
Khairudin, N., & Hamid, M. N. A. (2015). The Development of a LMS Decision Making Model: Evaluating the Importance of Non-Financial Measures in LMS Decision Making at Universities.
Kline, R. B. (2005). Methodology in the social sciences.
Lai, C. (2015). Modeling teachers' influence on learners' self-directed use of technology for language learning outside the classroom. Computers & education, 82, 74-83.
Liaw, S.S., (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & education, 51(2), 864-873.
Mantoro, T., Utami, P., Dewanti, R., & Yudhi, W. S. (2017). The Use of Learning Management System (LMS) for College Students to Become an Active Learner: Constructivism View. Advanced Science Letters, 23(2), 796-800.
Md Yunus, M. (2007). Malaysian ESL teachers' use of ICT in their classrooms: expectations and realities. ReCALL, 19(01), 79-95.
Mohammadi, H. (2015). Investigating users' perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
Ofori, K. S., Larbi-Siaw, O., Fianu, E., Gladjah, R. E., & Boateng, E. O. Y. (2016). Factors influencing the continuance use of mobile social media: The effect of privacy concerns. Journal of Cyber Security and Mobility, 4(3), 105-124.
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296.
Punnoose, A. (2012). Determinants of intention to use eLearning based on the technology acceptance model. Journal of Information Technology Education: Research, 11(1), 301-337.
Ramayah, T., & Lee, J. W. C. (2012). System Characteristics, Satisfaction and E-Learning Usage: A Structural Equation Model (SEM). Turkish Online Journal of Educational Technology-TOJET, 11(2), 196-206.
Rigopoulou, I. D., Chaniotakis, I. E., & Kehagias, J. D. (2017). An extended technology acceptance model for predicting smartphone adoption among young consumers in Greece. International Journal of Mobile Communications, 15(4), 372-387.
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Saadé, R. G., Tan, W., & Nebebe, F. (2008). Impact of motivation on intentions in online learning: Canada vs China. Setting Knowledge Free: The Journal of Issues in Informing Science and Information Technology, 5, 137-149.
Samaradiwakara, G., & Gunawardena, C. (2014). Comparison of existing technology acceptance theories and models to suggest a well improved theory/model. International Technical Sciences Journal, 21.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
Sekaran, U., & Bougie, R. (2010). Research methods for business: A skill building approach. Wiley: London.
Tan, P. J. B. (2015). English e-learning in the virtual classroom and the factors that influence ESL (English as a Second Language): Taiwanese citizens' acceptance and use of the Modular Object-Oriented Dynamic Learning Environment. Social Science Information, 0539018414566670.
Tselios, N., Altanopoulou, P., & Katsanos, C. (2011, September). Effectiveness of a framed wiki-based learning activity in the context of HCI education. In Informatics (PCI), 2011 15th Panhellenic Conference on (pp. 368-372). IEEE.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Zaharias, P., & Pappas, C. (2016). Quality Management of Learning Management Systems: A User Experience Perspective. Current Issues in Emerging eLearning, 3(1), 5.
References
Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888.
Aaker, D. A. (1995). Kumar, V. & Day, G. Marketing Research. New York: John Wiley & Sons, Inc.
Al-Adwan, A., & Smedley, J. (2013). Exploring students' acceptance of e-learning using Technology Acceptance Model in Jordanian universities Amer Al-Adwan Applied Science University, Jordan. International Journal of Education and Development using Information and Communication Technology, 9(2), 4-18.
Al-Aulamie, A. (2013). Enhanced technology acceptance model to explain and predict learners' behavioural intentions in learning management systems.
Annuar, N., & Shaari, R. (2014). The Antecedents toward Self-directed Learning among Distance Learner in Malaysian Public Universities. Proceeding of the Global Summit on Education, 195-205.
Baleghi-Zadeh, S., Ayub, A. F. M., Mahmud, R., & Daud, S. M. (2017). The influence of system interactivity and technical support on learning management system utilization. Knowledge Management & E-Learning, 9(1), 50.
Cigdem, H., & Ozturk, M. (2016). Factors affecting students' behavioral intention to use LMS at a Turkish post-secondary vocational school. The International Review of Research in Open and Distributed Learning, 17(3).
Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: An extension of the technology acceptance model. Group Decision and Negotiation, 11(2), 87-100.
Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119-143.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
Grover, K. S., Miller, M. T., Swearingen, B., & Wood, N. (2014). An examination of the self-directed learning practices of ESL adult language learners. Journal of Adult Education, 43(2), 12.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall.
Hajiyev, J., & Chang, C.-T. (2017). Gen Y Membersí¢ í‚ í‚ Mobile Banking Adoption Intention and Actual Use in Azerbaijan and Turkey: The Technology Acceptance Model and Social Cognitive Theory Approach. The Journal of Internet Banking and Commerce, 1-33.
Hsia, J.-W. (2007). An enhanced technology acceptance model for e-learning systems in high-tech companies. Paper presented at the Proceedings of the 7th WSEAS international conference on Distance learning and web engineering.
Hsu, C.-K., Hwang, G.-J., & Chang, C.-K. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327-336.
Hu, L. T., Bentler, P. M., & Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues, and applications. Evaluating model fit, 76-99.
Hutasuhut, I. J., Adruce, S. A. Z., & Bujang, S. (2018). An Understanding of Work-Related Self-Directed Learning. Sarawak Library Journal: Publication of Pustaka Negeri Sarawak, 1(1), 10-10.
Ifinedo, P., Pyke, J., & Anwar, A. (2018). Business undergraduates' perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support. Telematics and Informatics, 35(1), 93-102.
Idros, S. N. S., Mohamed, A. R., Esa, N., Samsudin, M. A., & Daud, K. A. M. (2010). Enhancing self-directed learning skills through e-SOLMS for Malaysian learners. Procedia-Social and Behavioral Sciences, 2(2), 698-706.
Khairudin, N., & Hamid, M. N. A. (2015). The Development of a LMS Decision Making Model: Evaluating the Importance of Non-Financial Measures in LMS Decision Making at Universities.
Kline, R. B. (2005). Methodology in the social sciences.
Lai, C. (2015). Modeling teachers' influence on learners' self-directed use of technology for language learning outside the classroom. Computers & education, 82, 74-83.
Liaw, S.S., (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & education, 51(2), 864-873.
Mantoro, T., Utami, P., Dewanti, R., & Yudhi, W. S. (2017). The Use of Learning Management System (LMS) for College Students to Become an Active Learner: Constructivism View. Advanced Science Letters, 23(2), 796-800.
Md Yunus, M. (2007). Malaysian ESL teachers' use of ICT in their classrooms: expectations and realities. ReCALL, 19(01), 79-95.
Mohammadi, H. (2015). Investigating users' perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
Ofori, K. S., Larbi-Siaw, O., Fianu, E., Gladjah, R. E., & Boateng, E. O. Y. (2016). Factors influencing the continuance use of mobile social media: The effect of privacy concerns. Journal of Cyber Security and Mobility, 4(3), 105-124.
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296.
Punnoose, A. (2012). Determinants of intention to use eLearning based on the technology acceptance model. Journal of Information Technology Education: Research, 11(1), 301-337.
Ramayah, T., & Lee, J. W. C. (2012). System Characteristics, Satisfaction and E-Learning Usage: A Structural Equation Model (SEM). Turkish Online Journal of Educational Technology-TOJET, 11(2), 196-206.
Rigopoulou, I. D., Chaniotakis, I. E., & Kehagias, J. D. (2017). An extended technology acceptance model for predicting smartphone adoption among young consumers in Greece. International Journal of Mobile Communications, 15(4), 372-387.
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Saadé, R. G., Tan, W., & Nebebe, F. (2008). Impact of motivation on intentions in online learning: Canada vs China. Setting Knowledge Free: The Journal of Issues in Informing Science and Information Technology, 5, 137-149.
Samaradiwakara, G., & Gunawardena, C. (2014). Comparison of existing technology acceptance theories and models to suggest a well improved theory/model. International Technical Sciences Journal, 21.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
Sekaran, U., & Bougie, R. (2010). Research methods for business: A skill building approach. Wiley: London.
Tan, P. J. B. (2015). English e-learning in the virtual classroom and the factors that influence ESL (English as a Second Language): Taiwanese citizens' acceptance and use of the Modular Object-Oriented Dynamic Learning Environment. Social Science Information, 0539018414566670.
Tselios, N., Altanopoulou, P., & Katsanos, C. (2011, September). Effectiveness of a framed wiki-based learning activity in the context of HCI education. In Informatics (PCI), 2011 15th Panhellenic Conference on (pp. 368-372). IEEE.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Zaharias, P., & Pappas, C. (2016). Quality Management of Learning Management Systems: A User Experience Perspective. Current Issues in Emerging eLearning, 3(1), 5.
Aaker, D. A. (1995). Kumar, V. & Day, G. Marketing Research. New York: John Wiley & Sons, Inc.
Al-Adwan, A., & Smedley, J. (2013). Exploring students' acceptance of e-learning using Technology Acceptance Model in Jordanian universities Amer Al-Adwan Applied Science University, Jordan. International Journal of Education and Development using Information and Communication Technology, 9(2), 4-18.
Al-Aulamie, A. (2013). Enhanced technology acceptance model to explain and predict learners' behavioural intentions in learning management systems.
Annuar, N., & Shaari, R. (2014). The Antecedents toward Self-directed Learning among Distance Learner in Malaysian Public Universities. Proceeding of the Global Summit on Education, 195-205.
Baleghi-Zadeh, S., Ayub, A. F. M., Mahmud, R., & Daud, S. M. (2017). The influence of system interactivity and technical support on learning management system utilization. Knowledge Management & E-Learning, 9(1), 50.
Cigdem, H., & Ozturk, M. (2016). Factors affecting students' behavioral intention to use LMS at a Turkish post-secondary vocational school. The International Review of Research in Open and Distributed Learning, 17(3).
Dasgupta, S., Granger, M., & McGarry, N. (2002). User acceptance of e-collaboration technology: An extension of the technology acceptance model. Group Decision and Negotiation, 11(2), 87-100.
Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119-143.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research, 382-388.
Grover, K. S., Miller, M. T., Swearingen, B., & Wood, N. (2014). An examination of the self-directed learning practices of ESL adult language learners. Journal of Adult Education, 43(2), 12.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall.
Hajiyev, J., & Chang, C.-T. (2017). Gen Y Membersí¢ í‚ í‚ Mobile Banking Adoption Intention and Actual Use in Azerbaijan and Turkey: The Technology Acceptance Model and Social Cognitive Theory Approach. The Journal of Internet Banking and Commerce, 1-33.
Hsia, J.-W. (2007). An enhanced technology acceptance model for e-learning systems in high-tech companies. Paper presented at the Proceedings of the 7th WSEAS international conference on Distance learning and web engineering.
Hsu, C.-K., Hwang, G.-J., & Chang, C.-K. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327-336.
Hu, L. T., Bentler, P. M., & Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues, and applications. Evaluating model fit, 76-99.
Hutasuhut, I. J., Adruce, S. A. Z., & Bujang, S. (2018). An Understanding of Work-Related Self-Directed Learning. Sarawak Library Journal: Publication of Pustaka Negeri Sarawak, 1(1), 10-10.
Ifinedo, P., Pyke, J., & Anwar, A. (2018). Business undergraduates' perceived use outcomes of Moodle in a blended learning environment: The roles of usability factors and external support. Telematics and Informatics, 35(1), 93-102.
Idros, S. N. S., Mohamed, A. R., Esa, N., Samsudin, M. A., & Daud, K. A. M. (2010). Enhancing self-directed learning skills through e-SOLMS for Malaysian learners. Procedia-Social and Behavioral Sciences, 2(2), 698-706.
Khairudin, N., & Hamid, M. N. A. (2015). The Development of a LMS Decision Making Model: Evaluating the Importance of Non-Financial Measures in LMS Decision Making at Universities.
Kline, R. B. (2005). Methodology in the social sciences.
Lai, C. (2015). Modeling teachers' influence on learners' self-directed use of technology for language learning outside the classroom. Computers & education, 82, 74-83.
Liaw, S.S., (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & education, 51(2), 864-873.
Mantoro, T., Utami, P., Dewanti, R., & Yudhi, W. S. (2017). The Use of Learning Management System (LMS) for College Students to Become an Active Learner: Constructivism View. Advanced Science Letters, 23(2), 796-800.
Md Yunus, M. (2007). Malaysian ESL teachers' use of ICT in their classrooms: expectations and realities. ReCALL, 19(01), 79-95.
Mohammadi, H. (2015). Investigating users' perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.
Ofori, K. S., Larbi-Siaw, O., Fianu, E., Gladjah, R. E., & Boateng, E. O. Y. (2016). Factors influencing the continuance use of mobile social media: The effect of privacy concerns. Journal of Cyber Security and Mobility, 4(3), 105-124.
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296.
Punnoose, A. (2012). Determinants of intention to use eLearning based on the technology acceptance model. Journal of Information Technology Education: Research, 11(1), 301-337.
Ramayah, T., & Lee, J. W. C. (2012). System Characteristics, Satisfaction and E-Learning Usage: A Structural Equation Model (SEM). Turkish Online Journal of Educational Technology-TOJET, 11(2), 196-206.
Rigopoulou, I. D., Chaniotakis, I. E., & Kehagias, J. D. (2017). An extended technology acceptance model for predicting smartphone adoption among young consumers in Greece. International Journal of Mobile Communications, 15(4), 372-387.
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of human-computer studies, 64(8), 683-696.
Saadé, R. G., Tan, W., & Nebebe, F. (2008). Impact of motivation on intentions in online learning: Canada vs China. Setting Knowledge Free: The Journal of Issues in Informing Science and Information Technology, 5, 137-149.
Samaradiwakara, G., & Gunawardena, C. (2014). Comparison of existing technology acceptance theories and models to suggest a well improved theory/model. International Technical Sciences Journal, 21.
Sánchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632-1640.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
Sekaran, U., & Bougie, R. (2010). Research methods for business: A skill building approach. Wiley: London.
Tan, P. J. B. (2015). English e-learning in the virtual classroom and the factors that influence ESL (English as a Second Language): Taiwanese citizens' acceptance and use of the Modular Object-Oriented Dynamic Learning Environment. Social Science Information, 0539018414566670.
Tselios, N., Altanopoulou, P., & Katsanos, C. (2011, September). Effectiveness of a framed wiki-based learning activity in the context of HCI education. In Informatics (PCI), 2011 15th Panhellenic Conference on (pp. 368-372). IEEE.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Zaharias, P., & Pappas, C. (2016). Quality Management of Learning Management Systems: A User Experience Perspective. Current Issues in Emerging eLearning, 3(1), 5.