research-article

Public Access

Measuring the Impact of COVID-19 Induced Campus Closure on Student Self-Regulated Learning in Physics Online Learning Modules

Authors:

Zhongzhou ChenAuthors Info & Claims

LAK21: LAK21: 11th International Learning Analytics and Knowledge Conference

Pages 110 - 120

https://doi.org/10.1145/3448139.3448150

Published: 12 April 2021 Publication History

All formats PDF

Abstract

This paper examines the impact of COVID-19 induced campus closure on university students’ self-regulated learning behavior by analyzing click-stream data collected from student interactions with 70 online learning modules in a university physics course. To do so, we compared the trend of six types of actions related to the three phases of self-regulated learning before and after campus closure and between two semesters. We found that campus closure changed students’ planning and goal setting strategies for completing the assignments, but didn’t have a detectable impact on the outcome or the time of completion, nor did it change students’ self-reflection behavior. The results suggest that most students still manage to complete assignments on time during the pandemic, while the design of online learning modules might have provided the flexibility and support for them to do so.

References

[1]

Vincent Aleven, Jonathan Sewall, Octav Popescu, Michael Ringenberg, Martin van Velsen, and Sandra Demi. 2016. Embedding Intelligent Tutoring Systems in MOOCs and e-Learning Platforms. In Intelligent Tutoring Systems, Alessandro Micarelli, John Stamper, and Kitty Panourgia(Eds.). Springer International Publishing, Cham, 409–415.

[2]

Maria Bannert, Inge Molenaar, Roger Azevedo, Sanna Järvelä, and Dragan Gašević. 2017. Relevance of Learning Analytics to Measure and Support Students’ Learning in Adaptive Educational Technologies. In Proceedings of the Seventh International Learning Analytics &; Knowledge Conference (Vancouver, British Columbia, Canada) (LAK ’17). Association for Computing Machinery, New York, NY, USA, 568–569. https://doi.org/10.1145/3027385.3029463

Digital Library

[3]

B Bloom. 1968. Learning for Mastery. Instruction and Curriculum. Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1.Evaluation comment 1(1968), 12.

[4]

Center for Distributed Learning. [n.d.]. Obojobo.

[5]

Zhongzhou Chen, Kyle M. Whitcomb, and Chandralekha Singh. 2018. Measuring the effectiveness of online problem-solving tutorials by multi-level knowledge transfer. In Physics Education Research Conference 2018(PER Conference). Washington, DC.

[6]

Zhongzhou Chen, Mengyu Xu, Geoffrey Garrido, and Matthew W. Guthrie. 2020. Relationship between students’ online learning behavior and course performance: What contextual information matters?Physical Review Physics Education Research 16, 1 (jun 2020), 010138. https://doi.org/10.1103/PhysRevPhysEducRes.16.010138

[7]

Analía Cicchinelli, Eduardo Veas, A. Pardo, V. Pammer-Schindler, Angela Fessl, C. Barreiros, and Stefanie N. Lindstädt. 2018. Finding traces of self-regulated learning in activity streams. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (2018).

Digital Library

[8]

Emma Dorn, Bryan Hancock, Jimmy Sarakatsannis, and Ellen Viruleg. 2020. COVID-19 and student learning in the United States: The hurt could last a lifetime. (2020), 14 pages.

[9]

Rob Eisinga, Tom Heskes, Ben Pelzer, and Manfred Te Grotenhuis. 2017. Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers. BMC Bioinformatics 18, 1 (2017), 1–18. https://doi.org/10.1186/s12859-017-1486-2

[10]

Andrew J Elliot and Kou Murayama. 2008. On the measurement of achievement goals: Critique, illustration, and application.Journal of Educational Psychology 100, 3 (2008), 613–628. https://doi.org/10.1037/0022-0663.100.3.613

[11]

Zachary Felker and Zhongzhou Chen. 2020. The impact of extra credit incentives on students’ work habits when completing online homework assignments. In 2020 Physics Education Research Conference Proceedings. American Association of Physics Teachers (AAPT), Virtual Conference, 143–148. https://doi.org/10.1119/perc.2020.pr.felker

[12]

T. Gonzalez, M. A. de la Rubia, K. P. Hincz, M. Comas-Lopez, Laia Subirats, Santi Fort, and G. M. Sacha. 2020. Influence of COVID-19 confinement on students’ performance in higher education. PloS one 15, 10 (2020), e0239490. https://doi.org/10.1371/journal.pone.0239490

[13]

Matthew W. Guthrie, Tom Zhang, and Zhongzhou Chen. 2020. A tale of two guessing strategies: interpreting the time students spend solving problems through online log data. In Physics Education Research Conference Proceedings. American Association of Physics Teachers (AAPT), Virtual Conference, 185–190. https://doi.org/10.1119/perc.2020.pr.guthrie

[14]

Brianne Gutmann, Gary E. Gladding, Morten Lundsgaard, and Timothy Stelzer. [n.d.]. Mastery-style homework exercises in introductory physics courses: Implementation matters. Physical Review Physics Education Research([n. d.]).

[15]

Qiujie Li, Rachel Baker, and Mark Warschauer. 2020. Using clickstream data to measure, understand, and support self-regulated learning in online courses. Internet and Higher Education 45 (apr 2020), 100727. https://doi.org/10.1016/j.iheduc.2020.100727

[16]

Jorge Maldonado-Mahauad, Mar Pérez-Sanagustín, René F. Kizilcec, Nicolás Morales, and Jorge Munoz-Gama. 2018. Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses. Computers in Human Behavior 80 (2018), 179–196. https://doi.org/10.1016/j.chb.2017.11.011

Digital Library

[17]

Thorsten Pohlert. 2020. PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended. https://CRAN.R-project.org/package=PMCMRplus R package version 1.6.1.

[18]

R Core Team. 2019. R: A Language and Environment for Statistical Computing.

[19]

Ido Roll and Philip H. Winne. 2015. Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics 2, 1 (Jul. 2015), 7–12. https://doi.org/10.18608/jla.2015.21.2

[20]

J. Saint, A. Whitelock-Wainwright, D. Gašević, and A. Pardo. 2020. Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data. IEEE Transactions on Learning Technologies 13, 4 (Oct 2020), 861–877. https://doi.org/10.1109/TLT.2020.3027496

[21]

Daniel L. Schwartz and John D. Bransford. 2005. Efficiency and Innovation in Transfer. In Transfer of Learning from a Modern Multidisciplinary Perspective (Current Perspectives on Cognition, Learning and Instruction), Jose P. Mestre(Ed.). IAP - Informaiton Age Publishing Inc., Charlotte, 1–51.

[22]

Michelle Taub, Roger Azevedo, Amanda E. Bradbury, Garrett C. Millar, and James Lester. 2018. Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. Learning and Instruction 54 (2018), 93–103. https://doi.org/10.1016/j.learninstruc.2017.08.005

[23]

U.S Department of Education and Office of Educational Technology. 2017. Reimagining the Role of Technology in Higher Education. (2017).

[24]

Hadley Wickham, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, Alex Hayes, Lionel Henry, Jim Hester, Max Kuhn, Thomas Lin Pedersen, Evan Miller, Stephan Milton Bache, Kirill Müller, Jeroen Ooms, David Robinson, Dana Paige Seidel, Vitalie Spinu, Kohske Takahashi, Davis Vaughan, Claus Wilke, Kara Woo, and Hiroaki Yutani. 2019. Welcome to the tidyverse. Journal of Open Source Software 4, 43 (2019), 1686. https://doi.org/10.21105/joss.01686

[25]

Bethany R. Wilcox and Michael Vignal. 2020. Understanding the student experience with emergency remote teaching. In 2020 Physics Education Research Conference Proceedings. American Association of Physics Teachers (AAPT), Virtual Conference, 581–586. https://doi.org/10.1119/perc.2020.pr.wilcox

[26]

Philip Winne. 2017. Learning Analytics for Self-Regulated Learning. In The Handbook of Learning Analytics (1 ed.), Charles Lang, George Siemens, Alyssa Friend Wise, and Dragan Gaševic (Eds.). Society for Learning Analytics Research (SoLAR), Alberta, Canada, 241–249. http://solaresearch.org/hla-17/hla17-chapter1

[27]

Philip H. Winne. 2020. Construct and consequential validity for learning analytics based on trace data. Computers in Human Behavior 112 (2020), 106457. https://doi.org/10.1016/j.chb.2020.106457

[28]

Barry J. Zimmerman. 2013. From Cognitive Modeling to Self-Regulation: A Social Cognitive Career Path. Educational Psychologist 48, 3 (2013), 135–147. https://doi.org/10.1080/00461520.2013.794676

[29]

Barry J. Zimmerman. 2015. Self-Regulated Learning: Theories, Measures, and Outcomes (second edi ed.). Vol. 21. Elsevier. 541–546 pages. https://doi.org/10.1016/B978-0-08-097086-8.26060-1

Cited By

Chen ZSingh C(2025)Opportunities and Challenges in Harnessing Digital Technology for Effective Teaching and LearningTrends in Higher Education10.3390/higheredu40100064:1(6)Online publication date: 27-Jan-2025
https://doi.org/10.3390/higheredu4010006
Boulahmel ADjelil FSmits G(2025)Investigating Self-Regulated Learning Measurement Based on Trace Data: A Systematic Literature ReviewTechnology, Knowledge and Learning10.1007/s10758-025-09816-yOnline publication date: 18-Jan-2025
https://doi.org/10.1007/s10758-025-09816-y
Kulal ARahiman H(2024)COVID-19 anxiety and uncertainty of classes: Devastating effect on students’ academic behavior and performanceF1000Research10.12688/f1000research.126095.312(179)Online publication date: 8-Jan-2024
https://doi.org/10.12688/f1000research.126095.3
Show More Cited By

Recommendations

Promoting self-regulated learning in web-based learning environments

Self-regulated learning with the Internet or hypermedia requires not only cognitive learning strategies, but also specific and general meta-cognitive strategies. The purposes of the Study2000 project, carried out at the TU Dresden, were to develop and ...
Supporting learners' self-regulated learning in Massive Open Online Courses
Abstract
In MOOCs, learners are typically presented with great autonomy over their learning process. Therefore, learners should engage in self-regulated learning (SRL) in order to successfully study in a MOOC. Learners however often struggle to self-...
Highlights
- Learners struggle to regulate their learning in massive open online courses (MOOCs).
- A self-regulated learning (SRL) intervention was implemented in three MOOCs.
- Learners' SRL was measured with trace data variables.
- ...
Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning

The results complement research on self-regulated learning in online learning environments.Findings indicate the benefits of directed preflective prompts for novice learners.Personalisation and adaptation of prompting in online learning is crucial for ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

LAK21: LAK21: 11th International Learning Analytics and Knowledge Conference

April 2021

645 pages

ISBN:9781450389358

DOI:10.1145/3448139

Program Chairs:
Maren Scheffel
Ruhr University Bochum, Germany
,
Nia Dowell
University of California, Irvine, USA
,
Srecko Joksimovic
University of South Australia, Australia
,
George Siemens
University of Texas, Arlington, USA & University of South Australia, Australia

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 April 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

NSF (National Science Foundation)

Conference

LAK21

LAK21: 11th International Learning Analytics and Knowledge Conference

April 12 - 16, 2021

CA, Irvine, USA

Acceptance Rates

Overall Acceptance Rate 236 of 782 submissions, 30%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
1,000
Total Downloads

Downloads (Last 12 months)218
Downloads (Last 6 weeks)27

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen ZSingh C(2025)Opportunities and Challenges in Harnessing Digital Technology for Effective Teaching and LearningTrends in Higher Education10.3390/higheredu40100064:1(6)Online publication date: 27-Jan-2025
https://doi.org/10.3390/higheredu4010006
Boulahmel ADjelil FSmits G(2025)Investigating Self-Regulated Learning Measurement Based on Trace Data: A Systematic Literature ReviewTechnology, Knowledge and Learning10.1007/s10758-025-09816-yOnline publication date: 18-Jan-2025
https://doi.org/10.1007/s10758-025-09816-y
Kulal ARahiman H(2024)COVID-19 anxiety and uncertainty of classes: Devastating effect on students’ academic behavior and performanceF1000Research10.12688/f1000research.126095.312(179)Online publication date: 8-Jan-2024
https://doi.org/10.12688/f1000research.126095.3
Yang SLin CHuang ALu OHou COgata H(2023)AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance EnhancementInformation and Technology in Education and Learning10.12937/itel.3.1.Inv.p0023:1(Inv-p002-Inv-p002)Online publication date: 2023
https://doi.org/10.12937/itel.3.1.Inv.p002
Kulal ARahiman H(2023)COVID-19 anxiety and uncertainty of classes: Devastating effect on students’ academic behavior and performanceF1000Research10.12688/f1000research.126095.212(179)Online publication date: 6-Nov-2023
https://doi.org/10.12688/f1000research.126095.2
Kulal ARahiman H(2023)COVID-19 anxiety and uncertainty of classes: Devastating effect on students’ academic behavior and performanceF1000Research10.12688/f1000research.126095.112(179)Online publication date: 15-Feb-2023
https://doi.org/10.12688/f1000research.126095.1
Felker ZChen Z(2023)Reducing procrastination on introductory physics online homework for college students using a planning prompt interventionPhysical Review Physics Education Research10.1103/PhysRevPhysEducRes.19.01012319:1Online publication date: 30-Mar-2023
https://doi.org/10.1103/PhysRevPhysEducRes.19.010123
Summers RBurgess AHigson HMoores E(2023)How you teach and who you teach both matter: lessons from learning analytics dataStudies in Higher Education10.1080/03075079.2023.224542449:3(576-591)Online publication date: 11-Aug-2023
https://doi.org/10.1080/03075079.2023.2245424
Pelikan EGrützmacher LHager KHolzer JKorlat SMayerhofer MSchober BSpiel CLüftenegger M(2023)The Role of Basic Need Satisfaction for Motivation and Self-Regulated Learning During COVID-19Zeitschrift für Psychologie10.1027/2151-2604/a000531231:3(228-238)Online publication date: Jul-2023
https://doi.org/10.1027/2151-2604/a000531
Goeman KStevens MElen J(2023)Instructional Disobedience in Flipped Higher Education Classrooms: An ExplorationReimagining Education: Studies and Stories for Effective Learning in an Evolving Digital Environment10.1007/978-3-031-25102-3_22(267-279)Online publication date: 31-May-2023
https://doi.org/10.1007/978-3-031-25102-3_22
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten