Factors Influencing Learners’ Self –Regulated Learning Skills in a Massive Open Online Course (MOOC) Environment
Year 2019,
, 1 - 16, 01.07.2019
Nour Awni Albelbısı
Farrah Dina Yusop
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,
, 1 - 16, 01.07.2019
Nour Awni Albelbısı
Farrah Dina Yusop
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.