Research Article
Factors Influencing Learners’ Self –Regulated Learning Skills in a Massive Open Online Course (MOOC) Environment
Abstract
The importance of self-regulation in a MOOC has been extensively discussed in research studies that provide evidence about the significant relationship between self-regulated learning and success in an e-learning environment. Learners with high self-regulated learning are more independent in regulating their learning and have a greater probability of success in their online courses. This study identifies factors that influence self-regulated learning and determines relationships between these factors and self-regulated learning. A conceptual model is proposed for combining success factors for self-regulated learning in a MOOC environment. A research instrument based on the model was designed and administered to six hundred and twenty-two MOOC students enrolled in five universities. Relationships between relevant factors and self-regulated learning were examined using a Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, and the statistical findings revealed that three factors - service quality, attitude, and course quality - influence self-regulated learning in a MOOC.
References
- Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2012). A model to measure e-learning systems success. Measuring Organizational Information Systems Success: New Technologies and Practices, Business Science Reference, Hershey, PA, 293-317. Alsabawy, A. Y., Cater-Steel, A., and Soar, J. (2011). Measuring e-learning system success (Research in progress). In Proceedings of the 15th Pacific Asia Conference on Information Systems (PACIS 2011, July) (pp. 1-15). Queensland University of Technology. Authors (2018). Mapping the Factors Influencing Success of Massive Open Online Courses (MOOC) in Higher Education. Eurasia Journal of Mathematics, Science and Technology Education, 14(7), 2995- 3012. Auvinen, T. (2015). Educational Technologies for Supporting Self-Regulated Learning in Online Learning Environments. Barnard-Brak, L., Paton, V. O., & Lan, W. Y. (2010). Profiles in self-regulated learning in the online learning environment. The International Review of Research in Open and Distributed Learning, 11(1), 61-80. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. http://dx.doi.org/10.1016/j.iheduc.2015.04.007 Chin, W. W., Marcolin, B., & Newsted, P. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronicmail emotion/adoption study. Information Systems Research, 14(2), 189–217. Cho, M. H., & Kim, B. J. (2013). Students’ self-regulation for interaction with others in online learning environments. The Internet and Higher Education, 17, 69-75. Cho, M., & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34(3), 290–301. http:// dx.doi.org/10.1080/01587919.2013.835770 Cohen, J. (1988). Statistical power analysis for the Behavioral Sciences. Mahwah, NJ: Erlbaum. Daniel, J., & Uvalic-Trumbic, S. (2013). Turbulent times in tertiary education: Lessons for Bangladesh. Paper presented at the International Conference on Tertiary Education: Realities and Challenges, Daffodil University, Bangladesh. 14 Davis, D. J., Chen, G., Jivet, I., Hauff, C., & Houben, G. (2016). Encouraging metacognition and selfregulation in MOOCs through increased learner feedback. CEUR Workshop Proceedings, 1596, 17– 22. Retrieved from http://ceur-ws.org/Vol-1596/paper3.pdf de Waard, I., Gallagher, M. S., Zelezny-Green, R., Czerniewicz, L., Downes, S., Kukulska-Hulme, A., & Willems, J. (2014). Challenges for conceptualising EU MOOC for vulnerable learner groups. Proceedings of the European MOOC Stakeholder Summit 2014, 33-42. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. Freeze, R. D., Alshare, K. A., Lane, P. L., & Wen, H. J. (2010). IS Success Model in ELearning context based on students’ perceptions. Journal of Information Systems Education, 21(2), 173-184. Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: an organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. Hair, J. F., Black, W. C., Babin, B., Anderson, R. E., & Ronald, L. T. (2006). Multivariate data analysis (5th ed.). Englewood Cliffs, NJ: Prentice Hall. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: SAGE. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. Hair, J.F., Anderson, R.E. , Tatham, R.L. , & Black, W.C. . (1998). Multivariate data analysis (5th ed.). New Jersey: Prentice Hall. Hair, J.F., Black, W.C. , Babin, B., & Anderson, R.E. . (2010). Multivariate data analysis (7th ed.). New Jersey: Prentice Hall. Hammoud, L. (2010). Factors affecting students’ attitude and performance when using a web-enhanced learning environment (Doctoral dissertation, Brunel University, School of Information Systems, Computing and Mathematics PhD Theses). Retrieved from http://bura.brunel.ac.uk/bitstream/2438/4622/1/ FulltextThesis.pdf Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959-10966. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. doi:10.1007/s11747-014-0403-8. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited. Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45-58. doi: 10.1016/j. edurev.2014.05.001 Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education, 91, 83-91. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195–204. Kizilcec, R. F., & Halawa, S. (2015, March). Attrition and achievement gaps in online learning. Paper presented at Learning@Scale 2015, Vancouver. http://dx.doi.org/10.1145/2724660.2724680 Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2016, April). Recommending self-regulated learning strategies does not improve performance in a MOOC. Paper presented at Learning @ Scale 2016, Edinburgh. http://dx.doi.org/10.1145/2876034.2893378 15 Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18–33. http://dx.doi.org/10.1016/j.compedu.2016.10.001 Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press. Kop, R. (2011). The challenges to connectivist learning on open online networks: Learning experiences during a massive open online course. The International Review of Research in Open and Distributed Learning, 12(3), 19-38. Kramarski, B., & Gutman, M. (2006). How can self‐regulated learning be supported in mathematical E‐ learning environments?. Journal of Computer Assisted Learning, 22(1), 24-33. Lee, J.K., & Lee, W.K. (2008). The relationship of e-Learner’s self-regulatory efficacy and perception of e-Learning environmental quality. Computers in Human Behaviour, 24(1), 32-47. Lee, Y., Choi, J., & Kim, T. (2012). Discriminating factors between completers of and dropouts from online learning courses. British Journal of Educational Technology, 44(2), 328–337. http://dx.doi. org/10.1111/j.1467-8535.2012.01306.x Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. Lin, Y.-L., Lin, H.-W., & Hung, T.-T. (2015). Value hierarchy for massive open online courses. Computers in Human Behaviour, 53, 408-418. Littlejohn, A., & Milligan, C. (2015). Designing MOOCs for professional learners: Tools and patterns to encourage self-regulated learning. eLearning Papers, 42. Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and selfregulated learning in MOOCs. The Internet and Higher Education, 29, 40-48. Magen-Nagar, N., & Cohen, L. (2016). Learning strategies as a mediator for motivation and a sense of achievement among students who study in MOOCs. Education and Information Technologies, 1–20. http://dx.doi.org/10.1007/s10639-016-9492-y Mazoue, J. G. (2014). The MOOC model: Challenging traditional education. Educause Review. Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. MERLOT Journal of Online Learning and Teaching, 9(2), 149-159. Nawrot, I., & Doucet, A. (2014, April). Building engagement for MOOC students: Introducing support for time management on online learning platforms. Paper presented at the 23rd International World Wide Web Conference, Seoul, South Korea. http://dx.doi.org/10.1145/2567948.2580054 Nordin, N., Norman, H., & Embi, M. A. (2015). Technology acceptance of massive open online courses in Malaysia. Malaysian Journal of Distance Education, 17(2), 1-16. Onah, D. F. O., & Sinclair, J. E. (2017). Assessing self-regulation of learning dimensions in a stand-alone MOOC platform. International Journal of Engineering Pedagogy (iJEP), 7(2), 4-21. Owens, J. D., & Price, L. (2010). Is e-learning replacing the traditional lecture? Education and Training Journal, 52(2), pp. 128-139. Ozkan, S., Koseler, R., & Baykal, N. (2009). Evaluating learning management systems: Adoption of hexagonal e-Learning assessment model in higher education. Transforming Government: People, Process and Policy, 3(2), 111-130. Parr, C. (2013). MOOC completion rates ‘below 7%,’. Times higher education, 9. Retrieved from http:// www.timeshighereducation.co.uk/news/moocs-completion-ratesbelow- 7/2003710 Presley, A., & Presley, T. (2009). Factors influencing student acceptance and use of academic portals. Journal of Computing in Higher Education, 21(3), pp.167-182. Retrieved from www.springerlink.com/ index/e575145287667515.pdf 16 Rai, L., & Chunrao, D. (2016). Influencing factors of success and failure in MOOC and general analysis of learner behavior. International Journal of Information and Education Technology, 6(4), 262. Rhema, A., & Miliszewska, I. (2014). Analysis of student attitudes towards e-learning: The case of engineering students in Libya. Issues in Informing Science and Information Technology, 11, 169-190. Ringle, C. M., Wende, S., & Becker, J-M. (2015). SmartPLS 3. Hamburg, Germany: SmartPLS. Retrieved from http://www.smartpls.com Samarasinghe, S. M. (2012). e-Learning systems success in an organisational context: a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management Information Systems at Massey University, Palmerston North, New Zealand (Doctoral dissertation, Massey University). Retrieved from http://hdl.handle.net/10179/4726 Sun, P., Tasi, R. J., Finger, G., & Chen, Y. (2008). What drives a successful e- learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. Tella, A. (2011). Reliability and factor analysis of a blackboard course management system success: A scale development and validation in an educational context. Journal of Information Technology Education: Research, 10, 55-80. Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. Terras, M. M., & Ramsay, J. (2015). Massive open online courses (MOOCs): Insights and challenges from a psychological perspective. British Journal of Educational Technology, 46(3), 472–487. http://dx.doi. org/10.1111/bjet.12274 Wang, H. C., & Chiu, Y. F. (2011). Assessing e-learning 2.0 system success. Computers & Education, 57(2), 1790-1800. You, J.W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Compute Educ. 77, 125-133. doi:10.1016/j. compedu.2014.04.018 Yousef, A. M. F., Chatti, M. A., Schroeder, U., & Wosnitza, M. (2014). What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. In Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on (pp. 44-48). IEEE. Zhao, H. (2016). Factors influencing self-regulation in e-learning 2.0: Confirmatory factor model. Canadian Journal of Learning and Technology, 42(2), n2. Zimmerman, B. J. (2015). Self-regulated learning: theories, measures, and outcomes. Zimmerman, B.J. & Schunk, D.H. (2001). Self-regulated Learning and Academic Achievement: Theoretical perspectives. Mahwah, N.J.: Lawrence Erlbaum.
Year 2019,
Volume: 20 Issue: 3, 1 - 16, 01.07.2019
Abstract
References
- Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2012). A model to measure e-learning systems success. Measuring Organizational Information Systems Success: New Technologies and Practices, Business Science Reference, Hershey, PA, 293-317. Alsabawy, A. Y., Cater-Steel, A., and Soar, J. (2011). Measuring e-learning system success (Research in progress). In Proceedings of the 15th Pacific Asia Conference on Information Systems (PACIS 2011, July) (pp. 1-15). Queensland University of Technology. Authors (2018). Mapping the Factors Influencing Success of Massive Open Online Courses (MOOC) in Higher Education. Eurasia Journal of Mathematics, Science and Technology Education, 14(7), 2995- 3012. Auvinen, T. (2015). Educational Technologies for Supporting Self-Regulated Learning in Online Learning Environments. Barnard-Brak, L., Paton, V. O., & Lan, W. Y. (2010). Profiles in self-regulated learning in the online learning environment. The International Review of Research in Open and Distributed Learning, 11(1), 61-80. Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. http://dx.doi.org/10.1016/j.iheduc.2015.04.007 Chin, W. W., Marcolin, B., & Newsted, P. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronicmail emotion/adoption study. Information Systems Research, 14(2), 189–217. Cho, M. H., & Kim, B. J. (2013). Students’ self-regulation for interaction with others in online learning environments. The Internet and Higher Education, 17, 69-75. Cho, M., & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34(3), 290–301. http:// dx.doi.org/10.1080/01587919.2013.835770 Cohen, J. (1988). Statistical power analysis for the Behavioral Sciences. Mahwah, NJ: Erlbaum. Daniel, J., & Uvalic-Trumbic, S. (2013). Turbulent times in tertiary education: Lessons for Bangladesh. Paper presented at the International Conference on Tertiary Education: Realities and Challenges, Daffodil University, Bangladesh. 14 Davis, D. J., Chen, G., Jivet, I., Hauff, C., & Houben, G. (2016). Encouraging metacognition and selfregulation in MOOCs through increased learner feedback. CEUR Workshop Proceedings, 1596, 17– 22. Retrieved from http://ceur-ws.org/Vol-1596/paper3.pdf de Waard, I., Gallagher, M. S., Zelezny-Green, R., Czerniewicz, L., Downes, S., Kukulska-Hulme, A., & Willems, J. (2014). Challenges for conceptualising EU MOOC for vulnerable learner groups. Proceedings of the European MOOC Stakeholder Summit 2014, 33-42. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. Freeze, R. D., Alshare, K. A., Lane, P. L., & Wen, H. J. (2010). IS Success Model in ELearning context based on students’ perceptions. Journal of Information Systems Education, 21(2), 173-184. Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: an organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. Hair, J. F., Black, W. C., Babin, B., Anderson, R. E., & Ronald, L. T. (2006). Multivariate data analysis (5th ed.). Englewood Cliffs, NJ: Prentice Hall. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: SAGE. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. Hair, J.F., Anderson, R.E. , Tatham, R.L. , & Black, W.C. . (1998). Multivariate data analysis (5th ed.). New Jersey: Prentice Hall. Hair, J.F., Black, W.C. , Babin, B., & Anderson, R.E. . (2010). Multivariate data analysis (7th ed.). New Jersey: Prentice Hall. Hammoud, L. (2010). Factors affecting students’ attitude and performance when using a web-enhanced learning environment (Doctoral dissertation, Brunel University, School of Information Systems, Computing and Mathematics PhD Theses). Retrieved from http://bura.brunel.ac.uk/bitstream/2438/4622/1/ FulltextThesis.pdf Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959-10966. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135. doi:10.1007/s11747-014-0403-8. Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (pp. 277-319). Emerald Group Publishing Limited. Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45-58. doi: 10.1016/j. edurev.2014.05.001 Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education, 91, 83-91. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195–204. Kizilcec, R. F., & Halawa, S. (2015, March). Attrition and achievement gaps in online learning. Paper presented at Learning@Scale 2015, Vancouver. http://dx.doi.org/10.1145/2724660.2724680 Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2016, April). Recommending self-regulated learning strategies does not improve performance in a MOOC. Paper presented at Learning @ Scale 2016, Edinburgh. http://dx.doi.org/10.1145/2876034.2893378 15 Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18–33. http://dx.doi.org/10.1016/j.compedu.2016.10.001 Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press. Kop, R. (2011). The challenges to connectivist learning on open online networks: Learning experiences during a massive open online course. The International Review of Research in Open and Distributed Learning, 12(3), 19-38. Kramarski, B., & Gutman, M. (2006). How can self‐regulated learning be supported in mathematical E‐ learning environments?. Journal of Computer Assisted Learning, 22(1), 24-33. Lee, J.K., & Lee, W.K. (2008). The relationship of e-Learner’s self-regulatory efficacy and perception of e-Learning environmental quality. Computers in Human Behaviour, 24(1), 32-47. Lee, Y., Choi, J., & Kim, T. (2012). Discriminating factors between completers of and dropouts from online learning courses. British Journal of Educational Technology, 44(2), 328–337. http://dx.doi. org/10.1111/j.1467-8535.2012.01306.x Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. Lin, Y.-L., Lin, H.-W., & Hung, T.-T. (2015). Value hierarchy for massive open online courses. Computers in Human Behaviour, 53, 408-418. Littlejohn, A., & Milligan, C. (2015). Designing MOOCs for professional learners: Tools and patterns to encourage self-regulated learning. eLearning Papers, 42. Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and selfregulated learning in MOOCs. The Internet and Higher Education, 29, 40-48. Magen-Nagar, N., & Cohen, L. (2016). Learning strategies as a mediator for motivation and a sense of achievement among students who study in MOOCs. Education and Information Technologies, 1–20. http://dx.doi.org/10.1007/s10639-016-9492-y Mazoue, J. G. (2014). The MOOC model: Challenging traditional education. Educause Review. Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. MERLOT Journal of Online Learning and Teaching, 9(2), 149-159. Nawrot, I., & Doucet, A. (2014, April). Building engagement for MOOC students: Introducing support for time management on online learning platforms. Paper presented at the 23rd International World Wide Web Conference, Seoul, South Korea. http://dx.doi.org/10.1145/2567948.2580054 Nordin, N., Norman, H., & Embi, M. A. (2015). Technology acceptance of massive open online courses in Malaysia. Malaysian Journal of Distance Education, 17(2), 1-16. Onah, D. F. O., & Sinclair, J. E. (2017). Assessing self-regulation of learning dimensions in a stand-alone MOOC platform. International Journal of Engineering Pedagogy (iJEP), 7(2), 4-21. Owens, J. D., & Price, L. (2010). Is e-learning replacing the traditional lecture? Education and Training Journal, 52(2), pp. 128-139. Ozkan, S., Koseler, R., & Baykal, N. (2009). Evaluating learning management systems: Adoption of hexagonal e-Learning assessment model in higher education. Transforming Government: People, Process and Policy, 3(2), 111-130. Parr, C. (2013). MOOC completion rates ‘below 7%,’. Times higher education, 9. Retrieved from http:// www.timeshighereducation.co.uk/news/moocs-completion-ratesbelow- 7/2003710 Presley, A., & Presley, T. (2009). Factors influencing student acceptance and use of academic portals. Journal of Computing in Higher Education, 21(3), pp.167-182. Retrieved from www.springerlink.com/ index/e575145287667515.pdf 16 Rai, L., & Chunrao, D. (2016). Influencing factors of success and failure in MOOC and general analysis of learner behavior. International Journal of Information and Education Technology, 6(4), 262. Rhema, A., & Miliszewska, I. (2014). Analysis of student attitudes towards e-learning: The case of engineering students in Libya. Issues in Informing Science and Information Technology, 11, 169-190. Ringle, C. M., Wende, S., & Becker, J-M. (2015). SmartPLS 3. Hamburg, Germany: SmartPLS. Retrieved from http://www.smartpls.com Samarasinghe, S. M. (2012). e-Learning systems success in an organisational context: a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management Information Systems at Massey University, Palmerston North, New Zealand (Doctoral dissertation, Massey University). Retrieved from http://hdl.handle.net/10179/4726 Sun, P., Tasi, R. J., Finger, G., & Chen, Y. (2008). What drives a successful e- learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. Tella, A. (2011). Reliability and factor analysis of a blackboard course management system success: A scale development and validation in an educational context. Journal of Information Technology Education: Research, 10, 55-80. Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205. Terras, M. M., & Ramsay, J. (2015). Massive open online courses (MOOCs): Insights and challenges from a psychological perspective. British Journal of Educational Technology, 46(3), 472–487. http://dx.doi. org/10.1111/bjet.12274 Wang, H. C., & Chiu, Y. F. (2011). Assessing e-learning 2.0 system success. Computers & Education, 57(2), 1790-1800. You, J.W., & Kang, M. (2014). The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning. Compute Educ. 77, 125-133. doi:10.1016/j. compedu.2014.04.018 Yousef, A. M. F., Chatti, M. A., Schroeder, U., & Wosnitza, M. (2014). What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. In Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on (pp. 44-48). IEEE. Zhao, H. (2016). Factors influencing self-regulation in e-learning 2.0: Confirmatory factor model. Canadian Journal of Learning and Technology, 42(2), n2. Zimmerman, B. J. (2015). Self-regulated learning: theories, measures, and outcomes. Zimmerman, B.J. & Schunk, D.H. (2001). Self-regulated Learning and Academic Achievement: Theoretical perspectives. Mahwah, N.J.: Lawrence Erlbaum.
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Details
| Primary Language | English |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | July 1, 2019 |
| Submission Date | December 15, 2018 |
| Published in Issue | Year 2019 Volume: 20 Issue: 3 |