Abstracts
Abstract
High dropout rates have been an unsolved issue in massive open online courses (MOOCs). As perceived effectiveness predicts learner retention in MOOCs, instructional design factors that affect it have been increasingly examined. However, self-regulated learning, self-efficacy, and task value have been underestimated from the perspective of instructors even though they are important instructional design considerations for MOOCs. This study investigated the influence of self-regulated learning strategies, self-efficacy, and task value on perceived effectiveness of successful MOOC learners. Three hundred fifty-three learners who successfully completed the Mountain 101 MOOC participated in this study by completing a survey through e-mail. The results of stepwise multiple regression analysis showed that perceived effectiveness was significantly predicted by both self-regulated learning strategies and task value. In addition, the results of another stepwise multiple regression analysis showed that meta-cognitive activities after learning, environmental structuring, and time management significantly predicted perceived effectiveness.
Keywords:
- self-regulated learning,
- self-efficacy,
- task value,
- MOOCs,
- perceived effectiveness
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Bibliography
- Alario-Hoyos, C., Estévez-Ayres, I., Pérez-Sanagustín, M., Kloos, C. D., & Fernández-Panadero, C. (2017). Understanding learners’ motivation and learning strategies in MOOCs. The International Review of Research in Open and Distributed Learning, 18(3), 119-137. https://doi.org/10.19173/irrodl.v18i3.2996
- Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006
- Amoozegar, A., Daud, S. M., Mahmud, R., & Jalil, H. A. (2017). Exploring learner to institutional factors and learner characteristics as a success factor in distance learning. International Journal of Innovation and Research in Educational Sciences, 4(6), 647-656. Retrieved from http://www.ijires.org/administrator/components/com_jresearch/files/publications/IJIRES_1122_FINAL.pdf
- An, Y. J., & Cao, L. (2014). Examining the effects of metacognitive scaffolding on students’ design problem solving and metacognitive skills in an online environment. Journal of Online Learning and Teaching, 10(4), 552-568. Retrieved from https://pdfs.semanticscholar.org/48fe/fa6c8f2a676513ff3819f87481e9e62a419b.pdf
- Artino, A. R. (2007). Online military training: Using a social cognitive view of motivation and self-regulation to understand students’ satisfaction, perceived learning, and choice. Quarterly Review of Distance Education, 8(3), 191-202.
- Artino, A. R. (2008). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online learning. Journal of Computer Assisted Learning, 24(3), 260-270. https://doi.org/10.1111/j.1365-2729.2007.00258.x
- Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37, 122-147.
- Bandura, A. (1994). Self-efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (pp. 71-81). New York, NY: Academic Press.
- Bolliger, D. U., & Halupa, C. (2012). Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(1), 81-98. https://doi.org/10.1080/01587919.2012.667961
- Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica: Journal of the Econometric Society, 47(5), 1287-1294. https://doi.org/10.2307/1911963
- 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. https://doi.org/10.1016/j.iheduc.2015.04.007
- Chiu, C. M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Info rmation & Management, 45(3), 194-201. https://doi.org/10.1016/j.im.2008.02.003
- Cho, M. H., & Cho, Y. (2017). Self-regulation in three types of online interaction: A scale development. Distance Education, 38(1), 70-83. https://doi.org/10.1080/01587919.2017.1299563
- Davis, D. J., Chen, G., Jivet, I., Hauff, C., & Houben, G. (2016, April). Encouraging metacognition and self-regulation in MOOCs through increased learner feedback. In CEUR Workshop Proceedings: Vol. 1596 (pp. 17-22). Retrieved from http://ceur-ws.org/Vol-1596/paper3.pdf
- Dick, W. & Carey, L. M. (1978). The systematic design of instruction. New York, NY: HarperCollins.
- Dirkes, M. A. (1985). Metacognition: Students in charge of their thinking. Roeper Review, 8(2), 96-100. https://doi.org/10.1080/02783198509552944
- Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). London, England: SAGE.
- García Espinosa, B. J., Tenorio Sepúlveda, G. C., & Ramírez Montoya, M. S. (2015). Self-motivation challenges for student involvement in the open educational movement with MOOC. Revista De Universidad Y Sociedad Del Conocimiento, 12(1), 91-103. https://doi.org/10.7238/rusc.v12i1.2185
- Hodges, C. (2016, April). The development of learner self-efficacy in MOOCs. In Proceedings of Global Learn-Global Conference on Learning and Technology (pp. 517-522). Limerick, Ireland. Retrieved from https://www.learntechlib.org/p/172763
- Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157-168. https://doi.org/10.1016/j.compedu.2016.03.016
- Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education, 91, 83-91. https://doi.org/10.1016/j.compedu.2015.10.019
- Jansen, R. S., Van Leeuwen, A., Janssen, J., & Kester, L. (2018). Validation of the Revised Self-regulated Online Learning Questionnaire. In V. Pammer-Schindler, M. Pérez-Sanagustín, H. Drachsler, R.Elferink, & M. Scheffel (Eds.), Proceedings of the European Conference on Technology Enhanced Learning (pp. 116-121). Cham, Switzerland: Springer.
- Jansen, R. S., Van Leeuwen, A., Janssen, J., Kester, L., & Kalz, M. (2017). Validation of the self-regulated online learning questionnaire. Journal of Computing in Higher Education, 29(1), 6-27. https://doi.org/10.1007/s12528-016-9125-x
- Johnson, B., & Christensen, L. (2017). Educational research: Quantitative, qualitative, and mixed approaches (6th ed.). Thousand Oaks, CA: Sage.
- Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computers & Education, 62, 149-158. https://doi.org/10.1016/j.compedu.2012.10.027
- Jung, E., Kim, D., Yoon, M., Park, S., & Oakley, B. (2019). The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education, 128, 377-388. https://doi.org/10.1016/j.compedu.2018.10.001
- Kang, M., & Im, T. (2013). Factors of learner-instructor interaction which predict perceived learning outcomes in online learning environment. Journal of Computer Assisted Learning, 29(3), 292-301. https://doi.org/10.1111/jcal.12005
- Kerr, S. (2011). Tips, tools, and techniques for teaching in the online high school classroom. TechTrends, 55(1), 28-31.
- 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, Scotland. https://doi.org/10.1145/2876034.2893378
- Lee, D., Watson, S. L., & Watson, W. R. (2019). Systematic literature review on self-regulated learning in massive open online courses. Australasian Journal of Educational Technology, 35(1), 28-41. https://doi.org/10.14742/ajet.3749
- 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. https://doi.org/10.1016/j.compedu.2007.09.005
- 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. https://doi.org/10.1016/j.compedu.2012.07.015
- 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, 22(3), 1-20. https://doi.org/10.1007/s10639-016-9492-y
- Milligan, C., & Littlejohn, A. (2016). How health professionals regulate their learning in massive open online courses. The Internet and Higher Education, 31, 113-121. https://doi.org/10.1016/j.iheduc.2016.07.005
- Miltiadou, M. (2001). Motivational constructs as predictors of success in the online classroom. Dissertation Abstracts International, 61(9-A), 3527.
- Morales Chan, M., Hernandez Rizzardini, R., Barchino Plata, R., & Amelio Medina, J. (2015). MOOC using cloud-based tools: A study of motivation and learning strategies in Latin America. International Journal of Engineering Education, 31(3), 901-911. Retrieved from http://www.ijee.ie/latestissues/Vol31-3/22_ijee3046ns.pdf
- 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. https://doi.org/10.1145/2567948.2580054
- Pappas, C. (2015, June). Getting the most out of your eLearning course: 10 study tips for online learners. eLearning Industry. Retrieved from https://elearningindustry.com/10-study-tips-for-online-learners-getting-the-most-out-of-your-elearning-course
- Park, T. J., Cha, H. J., & Lee, G. Y. (2016). A study on design guidelines of learning analytics to facilitate self-regulated learning in MOOCs. Educational Technology International, 17(1), 117-150. Retrieved from http://kset.or.kr/eti_ojs/index.php/instruction/article/viewFile/61/pdf_19
- Peltier, J. W., Drago, W., & Schibrowsky, J. A. (2003). Virtual communities and the assessment of online marketing education. Journal of Marketing Education, 25(3), 260-276. https://doi.org/10.1177/0273475303257762
- Peterson, C. (2003). Bringing ADDIE to life: Instructional design at its best. Journal of Educational Multimedia and Hypermedia, 12(3), 227-241. Retrieved from https://www.learntechlib.org/primary/p/2074/
- Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, MI: The University of Michigan. https://doi.org/10.1037/0022-0663.82.1.33
- Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801-813. https://doi.org/10.1177/0013164493053003024
- Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. The American Journal of Distance Education, 22(2), 72-89. https://doi.org/10.1080/08923640802039024
- Sujatha, R., & Kavitha, D. (2018). Learner retention in MOOC environment: Analyzing the role of motivation, self-efficacy and perceived effectiveness. International Journal of Education and Development Using Information and Communication Technology, 14(2), 62-74. Retrieved from http://ijedict.dec.uwi.edu//viewissue.php?id=51
- Tsai, Y. H., Lin, C. H., Hong, J. C., & Tai, K. H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18-29. https://doi.org/10.1016/j.compedu.2018.02.011
- Venkatesh, V., Croteau, A. M., & Rabah, J. (2014, January). Perceptions of effectiveness of instructional uses of technology in higher education in an era of Web 2.0. In 2014 47th Hawaii International Conference on System Sciences (pp. 110-119). IEEE. https://doi.org/10.1109/HICSS.2014.22
- Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses?. Contemporary Educational Psychology, 11(4), 307-313. https://doi.org/10.1016/0361-476X(86)90027-5
- Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. https://doi.org/10.1037/0022-0663.81.3.329
- Zimmerman, B. J. (1998). Academic studying and the development of personal skill: A self-regulatory perspective. Educational Psychologist, 33(2/3), 73-86.
- Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). San Diego, CA: Academic Press.