How Much and for Whom?: A Multi-Wave Study of the Impact of Self-Regulated Learning Scaffolds on MOOC Student Academic Performance
Pages 350 - 354
Abstract
With an effort to ameliorate the high dropout rates and the low performance in the massive open online courses (MOOCs), this paper presents multi-wave, experimental studies with a longitudinal intervention of self-regulated learning scaffolds on 24 MOOCs for 2650 learners. The self-regulated learning user interface (SRLUI) is designed based on Zimmerman's SRL cyclical model with a learning dashboard, interactive user interface, nudging effect. SRLUI is designed with two goals: 1. manifesting learner self-regulated learning behavior 2. enhancing learning outcomes. In this study, our primary research question is, "What is the marginal effect of SRLUI on learning as evidenced by final course grade?" To answer the research question, we employed a multilevel Bayesian beta regression modeling approach, first to each intervention and then across all three interventions in the aggregate. Our findings were mixed. We found some evidence of enhanced learning for passing and non-passing students across some of the individual interventions. On the whole, we determined that there was no statistical evidence of positive impacts on learning for students who pass a given course, though there is evidence of a small positive impact on learning for students who did not ultimately pass a given course. We discuss potential reasons for this differential impact and its implication for future course design and research.
References
[1]
Pheatt, L. E. (2017). The pursuit of profit or prestige: What the diffusion of MOOCs can tell us about disruptive innovation in US higher education. Doctoral dissertation, Columbia University.
[2]
Reich, J. (2020). Failure to disrupt: Why technology alone can't transform education. Harvard University Press. Review, 12, 45--58.
[3]
Shah, D. (2020, December 8). By the numbers: Moocs in 2020 - class central. The Report by Class Central. Retrieved February 21, 2022, from https://www.classcentral.com/report/mooc-stats-2020/
[4]
McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice.
[5]
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. Proceedings of 2014 IEEE 14th International Conference on Advanced Learning Technologies (pp. 44--48). IEEE.
[6]
Gamage, D., Fernando, S., & Perera, I. (2015, August). Quality of MOOCs: A review of literature on effectiveness and quality aspects. In 2015 8th International Conference on Ubi-Media Computing (UMEDIA) (pp. 224--229). IEEE.
[7]
Hsu. (2021). An Experimental Study of Self-regulated Learning Strategies Application in Moocs (Publication No. 28545839) [Doctoral dissertation, Teachers College, Columbia University]. ProQuest Dissertations.
[8]
Eckstein, J. (2019). How Coursera Makes Money: Certifications. Degree Programs, and Retraining for Businesses Investopedia URL https://www. investopedia. com/articles/investing/042815/how-coursera-works-makes-money. asp.
[9]
Pintrich, P. R. (2000a). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451--502). San Diego, CA: Academic.
[10]
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277--304). Hillsdale, NJ: Erlbaum.
[11]
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary educational psychology, 25(1), 82--91.
[12]
Zimmerman, B. J., & Schunk, D. H. (Eds.). (2001). Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.
[13]
Davis, D., Triglianos, V., Hauff, C., & Houben, G. J. (2018). SRLx: A personalized learner interface for MOOCs. In European Conference on Technology Enhanced Learning (pp. 122--135). Springer, Cham.
[14]
Alonso-Mencía, M. E., Alario-Hoyos, C., & Delgado Kloos, C. (2019). Chrome Plug-in to Support SRL in MOOCs. In M. Calise, C. Delgado Kloos, J. Reich, J. A. Ruiperez-Valiente, & M. Wirsing (Eds.), Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge (Vol. 11475, pp. 3--12). Springer International Publishing. https://doi.org/10.1007/978--3-030--19875--6_1
[15]
Pérez-Álvarez, R., Maldonado-Mahauad, J., & Pérez-Sanagustín, M. (2018). Design of a Tool to Support Self-Regulated Learning Strategies in MOOCs. Journal of Universal Computer Science, 24(8), 1090--1109.
[16]
Pérez?Sanagustín, M., Sapunar?Opazo, D., Pérez?Álvarez, R., Hilliger, I., Bey, A., Maldonado?Mahauad, J., & Baier, J. (2020). A MOOC?based flipped experience: Scaffolding SRL strategies improves learners' time management and engagement. Computer Applications in Engineering Education.
[17]
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis. CRC Press.
[18]
McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.
Index Terms
- How Much and for Whom?: A Multi-Wave Study of the Impact of Self-Regulated Learning Scaffolds on MOOC Student Academic Performance
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Published In
June 2022
491 pages
ISBN:9781450391580
DOI:10.1145/3491140
- General Chair:
- René F. Kizilcec,
- Program Chairs:
- Katie Davis,
- Xavier Ochoa
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Published: 01 June 2022
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