Effects of Online Self-Regulated Learning on Learning Ineffectiveness in the Context of COVID-19
Within the COVID-19 pandemic and the new normal period, online learning has become one of the main options for learning. Previous studies on self-regulated learning have shown that it was a better predictor of online learning effectiveness. However, this discussion has not been extended to the situation of the COVID-19 pandemic. To address this gap, this study aims to explore the relationship between the three stages of self-regulated learning (SRL) and learning ineffectiveness (LI). Data of 370 high school students were collected during the period of COVID-19. Structural equation modeling was used to perform confirmatory factor analysis on the data. Findings show that the preparatory stage was positively related to the stages of performance and appraisal, and the performance stage was positively related to the appraisal stage; on the other hand, the stages of performance and appraisal were negatively related to learning ineffectiveness. In addition, the preparatory stage had no direct relation to learning ineffectiveness, but the preparatory stage was correlated with learning ineffectiveness, mediated by the stages of performance and appraisal. These results suggest that better performance in the three stages of self-regulated learning decrease learners’ perceived online learning ineffectiveness. This understanding can have implications for global education.
Adam, N. L., Alzahri, F. B., Soh, S. C., Bakar, N. A., & Kamal, N. (2017). Self-regulated learning and online learning: A systematic review. In H. Badioze Zaman, P. Robinson, A. F. Smeaton, T. K. Shih, S. Velastin, T. Terutoshi, A. Jaafar, & N. Mohamad Ali (Eds.), Advances in visual informatics: 5th International Visual Informatics Conference, IVIC 2017, Bangi, Malaysia, November 28–30, 2017, Proceedings (pp. 143–154). https://doi.org/10.1007/978-3-319-70010-6_14
Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review-science direct. Clinical Psychology Review, 30(2), 217–237. https://doi.org/10.1016/j.cpr.2009.11.004
Alghamdi, A., Karpinski, A. C., Lepp, A., & Barkley, J. (2020). Online and face-to-face classroom multitasking and academic performance: Moderated mediation with self-efficacy for self-regulated learning and gender. Computers in Human Behavior, 102, 214–222. https://doi.org/10.1016/j.chb.2019.08.018
Alsancak Sirakaya, D., & Ozdemir, S. (2018). The effect of a flipped classroom model on academic achievement, self-directed learning readiness, motivation and retention. Malaysian Online Journal of Educational Technology, 6(1), 76–91. https://files.eric.ed.gov/fulltext/EJ1165484.pdf
Bahasoan, A. N., Ayuandiani, W., Mukhram, M., & Rahmat, A. (2020). Effectiveness of online learning in pandemic COVID-19. International Journal of Science, Technology & Management, 1(2), 100–106. https://doi.org/10.46729/ijstm.v1i2.30
Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. The Internet & Higher Education, 12(1), 1–6. https://doi.org/10.1016/j.iheduc.2008.10.005
Bezzina, F. H. (2010). Investigating gender differences in mathematics performance and in self-regulated learning: An empirical study from Malta. Equality, Diversity and Inclusion, 29(7), 669–693. https://doi.org/10.1108/02610151011074407
Boom, G., Paas, F., Merrieenboer, J. J. G. V., & Gog, T. V. (2004). Reflection prompts and tutor feedback in a Web-based learning environment: Effects on students’ self-regulated learning competence. Computers in Human Behavior, 20(4), 551–567. https://doi.org/10.1016/j.chb.2003.10.001
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://doi.org/10.1016/j.iheduc.2015.04.007
Cai, R., Wang, Q., Xu, J. & Zhou, L. (2020). Effectiveness of students’ self-regulated learning during the COVID-19 pandemic. Social Science Electronic Publishing, 34(1), 175–182. https://ssrn.com/abstract=3622569
Carrol, N., & Burke, M. (2010). Learning effectiveness using different teaching modalities. Journal of Business & Economics Research, 3(12), 65–72. http://doi.org/10.19030/ajbe.v3i12.966
Charo, R., Maite, A. S., & Guillermo, M. (2020). Self-regulation of learning and MOOC retention. Computers in Human Behavior, 111, Article 106423. https://doi.org/10.1016/j.chb.2020.106423
Chen, P., & Bonner, S. (2020). A framework for classroom assessment, learning, and self-regulation. Assessment in Education Principles Policy and Practice, 27(4), 373–393. https://doi.org/10.1080/0969594X.2019.1619515
Cleary, T. J., Callan, G. L., & Zimmerman, B. J. (2012). Assessing self-regulation as a cyclical, context-specific phenomenon: An overview and analysis of SRL microanalysis protocols. Education Research International, 2012, 1–19. https://doi.org/10.1155/2012/428639
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. http://www.utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf
Colthorpe, K., Ogiji, J., Ainscough, L., Zimbardi, K., & Anderson, S. (2019). Effect of metacognitive prompts on undergraduate pharmacy students’ self-regulated learning behavior. American Journal of Pharmaceutical Education, 83, Article 6646. https://doi.org/10.5688/ajpe6646
Cosnefroy, L., Fe Nouillet, F., Corinne Mazé, & Bonnefoy, B. (2018). On the relationship between the forethought phase of self-regulated learning and self-regulation failure. Issues in Educational Research, 28(2), 329–348. http://www.iier.org.au/iier28/cosnefroy.pdf
DeVellis, R. F., (2012). Scale development: Theory and applications (3rd ed.). Sage Publications.
Ernesto, P. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, Article 422. https://doi.org/10.3389/fpsyg.2017.00422
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Hadwin, A. F., Järvelä, S., and Miller, M. (2018). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 83–106). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315697048-6/self-regulation-co-regulation-shared-regulation-collaborative-learning-environments-allyson-hadwin-sanna-j%C3%A4rvel%C3%A4-mariel-miller
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. https://www.cengage.co.uk/books/9781473756540/
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. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Hong, J. C., Lee, Y. F., & Ye, J. H. (2021). Procrastination predicts online self-regulated learning and online learning ineffectiveness during the coronavirus lockdown. Personality and Individual Differences, 174(7), Article 110673. https://doi.org/10.1016/j.paid.2021.110673
Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2019). Supporting learners’ self-regulated learning in massive open online courses. Computers & Education, 146, Article 103771. https://doi.org/10.1016/j.compedu.2019.103771
Kline, R. B. (2011). Principles and practice of structural equation modeling. The Guilford Press. https://www.guilford.com/books/Principles-and-Practice-of-Structural-Equation-Modeling/Rex-Kline/9781462523344
Lee, D., Watson, S. L., & Watson, W. R. (2020). The relationships between self-efficacy, task value, and self-regulated learning strategies in massive open online courses. International Review of Research in Open and Distance Learning, 21(1), 23–39. https://doi.org/10.19173/irrodl.v20i5.4389
Lehmann, T., Haehnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323. https://doi.org/10.1016/j.chb.2013.07.051
Liu, X., He, W., Zhao, L., & Hong, J. C. (2021). Gender differences in self-regulated online learning during the COVID-19 lockdown. Frontiers in Psychology, 12, Article 752131. https://doi.org/10.3389/fpsyg.2021.752131
Maison, S., & Syamsurizal, T. (2019). Learning environment, students’ beliefs, and self-regulation in learning physics: Structural equation modeling. Journal of Baltic Science Education, 18(3), 389–403. https://doi.org/10.33225/jbse/19.18.389
Mei, D. (2020, January). Research on multi-integrated online and offline teaching model in the post-epidemic era: Taking numerical analysis course as an example. In Proceedings of the 2020 5th International Conference on Modern Management and Education Technology (MMET 2020) (pp. 356–359). Atlantis Press. https://doi.org/10.2991/assehr.k.201023.072
Moghadari-Koosha, M., Moghadasi-Amiri, M., Cheraghi, F., Mozafari, H., & Zandieh, M. (2020). Self-efficacy, self-regulated learning, and motivation as factors influencing academic achievement among paramedical students: A correlation study. Journal of Allied Health, 49(3), e145–e152. https://pubmed.ncbi.nlm.nih.gov/32877483/
Mustajab, M., Baharun, H. & Fawa’Iedah, Z. (2020). Adapting to teaching and learning during COVID-19: A case of Islamic school’s initiative of self-regulated learning. Nadwa, 14(2), 241–264. https://doi.org/10.21580/nw.2020.14.2.6515
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010.10.002
Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation: Theory, research, and applications (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185–227. https://doi.org/10.1080/00273170701341316
Rabin, E., Henderikx, M., Kalman, Y. M., & Kalz, M. (2020). What are the barriers to learners’ satisfaction in MOOCs and what predicts them? The role of age, intention, self-regulation, self-efficacy and motivation. Australasian Journal of Educational Technology, 36(3), 119–131. https://doi.org/10.14742/ajet.5919
Ridgley, L. M., Rubenstein, D., & Callan, G. L. (2020). Gifted underachievement within a self-regulated learning framework: Proposing a task-dependent model to guide early identification and intervention. Psychology in the Schools, 57(3), 1365–1384. https://doi.org/10.1002/pits.22408
Ruhland, S. K., & Brewer, J. A. (2001). Implementing an assessment plan to document student learning in a two-year technical college. Journal of Vocational Education Research, 26, 141–171. https://doi.org/10.5328/JVER26.2.141
Samruayruen, B., Enriquez, J., Natakuatoong, O., & Samruayruen, K. (2013). Self-regulated learning: A key of a successful learner in online learning environments in Thailand. Journal of Educational Computing Research, 48(1), 45–69. https://doi.org/10.2190/EC.48.1.c
Sanchez, G.. (2013). PLS path modeling with R. Trowchez Editions. https://www.gastonsanchez.com/PLS_Path_Modeling_with_R.pdf
Su, Y. S., Ding, T. J., & Chen, M. Y. (2021). Deep learning methods in internet of medical things for valvular heart disease screening system. IEEE Internet of Things Journal, 8(23), 16921-16932. https://doi.org/ 10.1109/JIOT.2021.3053420
Su, Y. S., & Wu, S. Y. (2021). Applying data mining techniques to explore users behaviors and viewing video patterns in converged it environments. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02712-6
Thompson, B. (2000). Ten commandments of structural equation modeling. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 261–283). American Psychological Association.
Tuti, T., Paton, C., & Winters, N. (2021). The counterintuitive self-regulated learning behaviours of healthcare providers from low-income settings. Computers & Education, 166(1), Article 104136. https://doi.org/10.1016/j.compedu.2021.104136
Tzeng, S., & Nieh, H. (2015, September). How self-concept, self-efficacy and self-evaluation relate to relate to achievement outcomes: New technology-based learning models for science and technology universities students. In Proceedings of 2015 International Conference on Interactive Collaborative Learning (ICL), 20–24 September 2015, Firenze, Italy (pp. 863–870). https://doi.org/10.1109/ICL.2015.7318141
van Herk, H., Poortinga, Y. H., & Verhallen, T. M. M. (2004). Response styles in rating scales: Evidence of method bias in data from six EU countries. Journal of Cross-Cultural Psychology, 35, 346–360. https://doi.org/10.1177/0022022104264126
Wolters, C. A., & Brady, A. C. (2020). College students’ time management: A self-regulated learning perspective. Educational Psychology Review. Advance online publication. https://doi.org/10.1007/s10648-020-09519-z
Won, S., Hensley, L. C., & Wolters, C. A. (2021). Brief research report: Sense of belonging and academic help-seeking as self-regulated learning. The Journal of Experimental Education, 89(1), 1–13. https://doi.org/10.1080/00220973.2019.1703095
Zeidner, M., & Stoeger, H. (2019). Self‐regulated learning (SRL): A guide for the perplexed. High Ability Studies, 30(1–2), 9–51. https://doi.org/10.1080/13598139.2019.1589369
Zhang, T., Taub, M., & Chen, Z. (2021, April). Measuring the impact of COVID-19 induced campus closure on student self-regulated learning in physics online learning modules. In LAK21: 11th International Learning Analytics and Knowledge Conference (pp. 110–120). Association for Computing Machinery. https://doi.org/10.1145/3448139.3448150
Zhang, W., Wang, Y., Yang, L., & Wang, C. (2020). Suspending classes without stopping learning: China’s education emergency management policy in the COVID-19 outbreak. Journal of Risk and Financial Management, 13(3), Article 55. https://doi.org/10.3390/jrfm13030055
Zhao, L., He, W., & Su, Y. S. (2021). Innovative pedagogy and design-based research on flipped learning in higher education. Frontiers in Psychology, 12, Article 230. https://doi.org/10.3389/fpsyg.2021.577002.
Zhao, L., Liu, X., & Su, Y. S. (2021). The differentiate effect of self-efficacy, motivation, and satisfaction on pre-service teacher students’ learning achievement in a flipped classroom: A case of a modern educational technology course. Sustainability, 13, Article 2888. https://doi.org/10.3390/su13052888
Zhu, Y. Q., Chen, L. Y., Chen, H. G., Chern, C. C. (2011). How does Internet information seeking help academic performance? The moderating and mediating roles of academic self-efficacy. Computers & Education, 57(4), 2476–2484. https://doi.org/10.1016/j.compedu.2011.07.006
Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: A self-regulated learning perspective. Educational Technology Research and Development, 68, 1485–1519. https://doi.org/10.1007/s11423-020-09753-w
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2
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). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7
Zimmerman, B. J. (2015). Self-regulated learning: Theories, measures, and outcomes. In J. D. Wright (Ed.), International encyclopedia of the social & behavioral sciences (2nd ed, pp. 541–546). https://doi.org/10.1016/B978-0-08-097086-8.26060-1
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International Licence. The copyright of all content published in IRRODL is retained by the authors.
This copyright agreement and use license ensures, among other things, that an article will be as widely distributed as possible and that the article can be included in any scientific and/or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.