The use of metacognitive strategies for undisrupted online learning: Preparing university students in the age of pandemic
Open accessAbstract
The primary objective of this study is to evaluate the relationship between the use of metacognitive strategies and learning performance in online learning among university students. The global lockdown due to the Covid-19 global pandemic outbreak has resulted in major interruptions in students' learning and education at all levels around the world. One of the consequences of university closures is that students suddenly find themselves having a lot more responsibility for their learning. Surprisingly, many students are not fully equipped with the relevant skills to excel in online learning despite being born into technology. Students are not aware of how to look inward to examine how they learn and to judge which methods are effective especially when faced with new forms of learning online because they lack metacognitive skills. Metacognition is crucial to the talent of learning. Although many researchers affirmed that metacognitive skills are crucial in any learning, a study on the impact of the use of metacognitive strategies on learning performance is still rudimentary. The study was carried out with 770 university IT students who have taken at least one online learning course. Data was collected using a self-administered instrument that was adapted from multiple sources. Three hypotheses were formulated and structural equation modeling was employed to conduct path modeling analysis. The findings from this study affirmed that students who use metacognitive strategies in online learning are indeed capable of evaluating their understanding of the course content and are capable of adding more effort in regulating their learning process. In view of the findings, this study will be useful for course instructors and students to establish practices on how to utilise metacognitive strategies to enhance students' learning performance as those lacking in metacognition may find themselves at a huge disadvantage.
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The COVID-19 pandemic has thrown students into learning situations in which they were forced to reflect internally on their own learning and rely on their metacognitive skills, as they were unable to converse with instructors as easily as pre-pandemic.
“The COVID- 19 pandemic has also thrown students into learning situations in which they were forced to reflect internally on their own learning and rely on their metacognitive skills, as they were unable to converse with instructors as easily as they had done pre- pandemic (Anthonysamy, 2021).”
From Promoting metacognition in an allied health anatomy course (2023) · cites this paper as Anthonysamy, 2021
The Introduction says pandemic-related university closures gave students more responsibility for learning, and the Literature review says online learning demands more self-regulation and offers less direct instructor interaction than face-to-face learning. But the paper says many students lack metacognitive skills and use them only to a limited extent; it does not establish that students were forced to reflect internally or actually relied on those skills. (LLM, conf=0.86)
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References 60 · Cited by 102
Research works printed but not linked: 41
- Abdul Wahab N Othman J Warris SN Blended learning in higher education: An overview e-Academia Journal UiTMT 2016 5 2 115 122
- Anthonysamy, L., Koo, A-C., Hew, S.-H. (2020). Self-regulated learning strategies and non-academic outcomes in higher education blended learning environments: A one-decade review. Education and Information Technologies, 1–28. 10.1007/s10639-020-10134-2. · DOI
- Azevedo R Aleven V Azevedo R Aleven V Metacognition and learning technologies: An overview of current interdisciplinary research International handbook of metacognition and learning technologies 2013 Springer 1e16
- Barak, M., Hussein-Farraj, R., & Dori, Y. J. (2016). On-campus or online: Examining self-regulation and cognitive transfer skills in different learning settings. International Journal of Educational Technology in Higher Education, 13(35). 10.1186/s41239-016-0035-9. · DOI
- Bernard RM Borokhovski E Schmid RF Tamim RM Abrami PC A meta-analysis of blended learning and technology use in higher education: From the general to the applied Journal of Computing in Higher Education 2014 26 1 87 122 10.1007/s12528-013-9077-3 · DOI · Paper page
- Cao, Y. (2012). A study of metacognitive strategies in web-based English autonomous learning. International Conference on Management and Education Innovation IPEDR, 37, 66–69. IACSIT Press, Singapore.
- Cho MH Heron ML Self-regulated learning: The role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course Distance Education 2015 36 1 80 99 10.1080/01587919.2015.1019963 · DOI
- Deschacht N Goeman K The effect of blended learning on course persistence and performance of adult learners: A difference-in-differences analysis Computers & Education 2015 87 83 89 10.1016/j.compedu.2015.03.020 · DOI
- Dumford AD Miller AL Online learning in higher education: Exploring advantages and disadvantages for engagement Journal of Computing in Higher Education 2018 30 3 452 465 10.1007/s12528-018-9179-z · DOI
- Ellis AK Denton DW Bond JB An analysis of research on metacognitive teaching strategies Procedia - Social and Behavioral Sciences 2014 116 4015 4024 10.1016/j.sbspro.2014.01.883 · DOI · Paper page
- Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215–235. 10.1111/j.1540-4609.2006.00114. · DOI
- Goda Y Yamada M Kato H Matsuda T Saito Y Miyagawa H Procrastination and other learning behavioral types in e-learning and their relationship with learning outcomes Learning and Individual Differences 2015 37 72 80 10.1016/j.lindif.2014.11.001 · DOI
- Gold A Malhotra A Segars A Knowledge management: An organizational capabilities perspective Journal of Management Information Systems 2001 18 185 214 10.1080/07421222.2001.11045669 · DOI · Paper page
- Goradia T Bugarcic A A social cognitive view of self-regulated learning within online environment Advances in Integrative Medicine. 2017 4 1 5 6 10.1016/j.aimed.2017.05.001 · DOI
- Greene JA Bolick CM Jackson WP Caprino AM Oswald C McVea M Domain-specificity of self-regulated learning processing in science and history Contemporary Educational Psychology 2015 42 111 128 10.1016/j.cedpsych.2015.06.001 · DOI
- Haidar AH Al Naqabi AK Emiratii high school students' understandings of stoichiometry and the influence of metacognition on their understanding Research in Science & Technological Education 2008 26 2 215 237 10.1080/02635140802037393 · DOI
- Hair JF Risher JJ Sarstedt M Ringle CM When to use and how to report the results of PLS-SEM European Business Review 2019 31 1 2 24 10.1108/EBR-11-2018-0203 · DOI · Paper page
- Hashemyolia S Asmuni A Ayub AFM Daud SM Shah JA Motivation to use self-regulated learning strategies in learning management system amongst science and social science undergraduates Asian Social Science 2015 11 3 49 10.5539/ass.v11n3p49 · DOI
- Hu, M., & Li, H. (2017). Student engagement in online learning: A review. 2017 International Symposium on Educational Technology (ISET), 39–43. 10.1109/ISET.2017.17. · DOI
- Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distance Learning, 14(1), 16–39. 10.19173/irrodl.v14i1.1338. · DOI · Paper page
- Lawanto O Santoso HB Lawanto KN Goodridge W Self-regulated learning skills and online activities between higher and lower performers on a web-intensive undergraduate engineering course Journal of Educators Online 2014 11 3 1 32 10.1007/s13398-014-0173-7.2 · DOI
- Li J Ye H Tang Y Zhou Z Hu X What are the effects of self-regulation phases and strategies for Chinese students? A meta-analysis of two decades research of the association between self-regulation and academic performance Frontiers in Psychology 2018 9 Dec. 1 13 10.3389/fpsyg.2018.02434 30618912 PMC6305361 · DOI
- Lilian A Ah-Choo K Soon-Hin H Investigating self-regulated learning strategies for digital learning relevancy Malaysian Journal of Learning and Instruction 2021 18 1 29 64 10.32890/mjli2021.18.1.2 · DOI
- Mohebi S Parham M Sharifirad G Gharlipour Z Social support and self - care behavior study Journal of Education and Health Promotion 2018 7 154 1 6 10.4103/jehp.jehp 29693029 PMC5903155 · DOI
- Moreno-Marcos PM Muñoz-Merino PJ Maldonado-Mahauad J Pérez-Sanagustín M Alario-Hoyos C Delgado Kloos C Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs Computers & Education 2019 145 103728 10.1016/j.compedu.2019.103728 · DOI
- Pellas N The influence of computer self-efficacy, metacognitive self-regulation, and self-esteem on student engagement in online learning programs: Evidence from the virtual world of second life Computers in Human Behavior 2014 35 157 170 10.1016/j.chb.2014.02.048 · DOI
- Pintrich PR The role of motivation in promoting and sustaining self-regulated learning International Journal of Educational Research 1999 31 6 459 470 10.1016/S0883-0355(99)00015-4 · DOI
- Puška, A., Puška, E., Dragić, L., & Maksimović, A. (2020). Students ’ satisfaction with E-learning platforms in Bosnia and Herzegovina. Technology, Knowledge, and Learning. 10.1007/s10758-020-09446-6. · DOI
- Roth A Ogrin S Schmitz B Assessing self-regulated learning in higher education: A systematic literature review of self-report instruments Educational Assessment, Evaluation, and Accountability 2016 28 225 250 10.1007/s11092-015-9229-2 · DOI · Paper page
- Rovai, A. P., Wighting, M. J., Baker, J. D., & Grooms, L. D. (2009). Development of an instrument to measure perceived cognitive, affective, and psychomotor learning in traditional and virtual classroom higher education settings. Internet and Higher Education, 12(1), 7–13. 10.1016/j.iheduc.2008.10.002. · DOI
- Spanjers I Könings KD Leppink J Verstegen D Jong N Czabanowska K Merriënboer J The promised land of blended learning: Quizzes as a moderator Educational Research Review 2015 15 59 74 10.1016/j.edurev.2015.05.001 · DOI
- Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful eLearning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. 10.1016/j.compedu.2006.11.007. · DOI · Paper page
- Tauber S Dunlosky J Dunlosky J Tauber SK A brief history of metamemory research and handbook overview The Oxford handbook of metamemory 2016 Oxford University Press 7 21
- Terras MM Ramsay J Massive open online courses (MOOCs): Insights and challenges from a psychological perspective British Journal of Educational Technology 2015 46 3 472 487 10.1111/bjet.12274 · DOI
- Hurk, M. Van Den. (2016). The relation strategies and individual study time, prepared participation and achievement in a problem-based curriculum, 155–169. 10.1177/1469787406064752. · DOI
- Vo HM Zhu C Diep NA The effect of blended learning on student performance at course-level in higher education: A meta-analysis Studies in Educational Evaluation 2017 53 17 28 10.1016/j.stueduc.2017.01.002 · DOI
- Wu, H., Tennyson, R. D., & Hsia, T. (2010). A study of student satisfaction in a blended e-learning system environment. Computers and Education, 55(1), 155–164. 10.1016/j.compedu.2009.12.012. · DOI
- Yang JC Quadir B Chen NS Miao Q Effects of online presence on learning performance in a blog-based online course Internet and Higher Education 2016 30 11 20 10.1016/j.iheduc.2016.04.002 · DOI
- Zimmerman BJ Self-regulated learning and academic achievement: An overview Educational Psychologist 1990 25 1 3 17 10.1207/s15326985ep2501_2 · DOI · Paper page
- Zimmerman BJ Martinez-Pons M Development of a structured interview for assessing student use of self-regulated learning strategies American Educational Research Journal 1986 23 4 614 628 10.3102/00028312023004614 · DOI · Paper page
- Zimmerman BJ Moylan AR Hacker DJ Dunlosky J Graesser AC Self-regulation: Where metacognition and motivation intersect Handbook of metacognition in education 2009 Routledge 299 316
- Authors
- Anthonysamy, L.
- Affiliations
- Multimedia University (MY)
- Corresponding author
- Anthonysamy, Lilian
- ORCID
- Anthonysamy, Lilian
- Venue
- Education and Information Technologies
- Published
- 2021-04-20
- Volume
- 26
- Issue
- 6
- Pages
- 6881-6899
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- PMC
- PMC8056832
- OpenAlex
- W3156032337
- Cited by
- 102
- References
- 60
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 26 · 2023: 27 · 2022: 23 · 2021: 2
- FWCI
- 28.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Education and Information Technologies (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
- Funders
- Multimedia University
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Social Sciences › Education › Online and Blended Learning
- Keywords
- metacognitive strategies, online learning, learning performance, university students, COVID-19 pandemic, lockdown, student self-regulation, metacognition, information technology students
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