Cargando…

Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining

Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress an...

Descripción completa

Detalles Bibliográficos
Autores principales: Taub, Michelle, Banzon, Allison M., Zhang, Tom, Chen, Zhongzhou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968150/
https://www.ncbi.nlm.nih.gov/pubmed/35369254
http://dx.doi.org/10.3389/fpsyg.2022.813514
_version_ 1784678987647156224
author Taub, Michelle
Banzon, Allison M.
Zhang, Tom
Chen, Zhongzhou
author_facet Taub, Michelle
Banzon, Allison M.
Zhang, Tom
Chen, Zhongzhou
author_sort Taub, Michelle
collection PubMed
description Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress and content understanding, and reflecting on how they completed each assignment. Based on Winne & Hadwin’s COPES model, SRL is a series of events that temporally unfold during learning, impacted by changing internal and external factors, such as goal orientation and content difficulty. Thus, as goal orientation and content difficulty change throughout a course, so might students’ use of SRL processes. This paper studies how students’ SRL behavior and achievement goal orientation change over time in a large (N = 250) college introductory level physics course taught online. Students’ achievement goal orientation was measured by repeated administration of the achievement goals questionnaire-revised (AGQ-R). Students’ SRL behavior was measured by analyzing their clickstream event traces interacting with online learning modules via a combination of trace clustering and process mining. Event traces were first divided into groups similar in nature using agglomerative clustering, with similarity between traces determined based on a set of derived characteristics most reflective of students’ SRL processes. We then generated causal nets for each cluster of traces via process mining and interpreted the underlying behavior and strategy of each causal net according to the COPES SRL framework. We then measured the frequency at which students adopted each causal net and assessed whether the adoption of different causal nets was associated with responses to the AGQ-R. By repeating the analysis for three sets of online learning modules assigned at the beginning, middle, and end of the semester, we examined how the frequency of each causal net changed over time, and how the change correlated with changes to the AGQ-R responses. Results have implications for measuring the temporal nature of SRL during online learning, as well as the factors impacting the use of SRL processes in an online physics course. Results also provide guidance for developing online instructional materials that foster effective SRL for students with different motivational profiles.
format Online
Article
Text
id pubmed-8968150
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89681502022-04-01 Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining Taub, Michelle Banzon, Allison M. Zhang, Tom Chen, Zhongzhou Front Psychol Psychology Success in online and blended courses requires engaging in self-regulated learning (SRL), especially for challenging STEM disciplines, such as physics. This involves students planning how they will navigate course assignments and activities, setting goals for completion, monitoring their progress and content understanding, and reflecting on how they completed each assignment. Based on Winne & Hadwin’s COPES model, SRL is a series of events that temporally unfold during learning, impacted by changing internal and external factors, such as goal orientation and content difficulty. Thus, as goal orientation and content difficulty change throughout a course, so might students’ use of SRL processes. This paper studies how students’ SRL behavior and achievement goal orientation change over time in a large (N = 250) college introductory level physics course taught online. Students’ achievement goal orientation was measured by repeated administration of the achievement goals questionnaire-revised (AGQ-R). Students’ SRL behavior was measured by analyzing their clickstream event traces interacting with online learning modules via a combination of trace clustering and process mining. Event traces were first divided into groups similar in nature using agglomerative clustering, with similarity between traces determined based on a set of derived characteristics most reflective of students’ SRL processes. We then generated causal nets for each cluster of traces via process mining and interpreted the underlying behavior and strategy of each causal net according to the COPES SRL framework. We then measured the frequency at which students adopted each causal net and assessed whether the adoption of different causal nets was associated with responses to the AGQ-R. By repeating the analysis for three sets of online learning modules assigned at the beginning, middle, and end of the semester, we examined how the frequency of each causal net changed over time, and how the change correlated with changes to the AGQ-R responses. Results have implications for measuring the temporal nature of SRL during online learning, as well as the factors impacting the use of SRL processes in an online physics course. Results also provide guidance for developing online instructional materials that foster effective SRL for students with different motivational profiles. Frontiers Media S.A. 2022-03-17 /pmc/articles/PMC8968150/ /pubmed/35369254 http://dx.doi.org/10.3389/fpsyg.2022.813514 Text en Copyright © 2022 Taub, Banzon, Zhang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Taub, Michelle
Banzon, Allison M.
Zhang, Tom
Chen, Zhongzhou
Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining
title Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining
title_full Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining
title_fullStr Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining
title_full_unstemmed Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining
title_short Tracking Changes in Students’ Online Self-Regulated Learning Behaviors and Achievement Goals Using Trace Clustering and Process Mining
title_sort tracking changes in students’ online self-regulated learning behaviors and achievement goals using trace clustering and process mining
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968150/
https://www.ncbi.nlm.nih.gov/pubmed/35369254
http://dx.doi.org/10.3389/fpsyg.2022.813514
work_keys_str_mv AT taubmichelle trackingchangesinstudentsonlineselfregulatedlearningbehaviorsandachievementgoalsusingtraceclusteringandprocessmining
AT banzonallisonm trackingchangesinstudentsonlineselfregulatedlearningbehaviorsandachievementgoalsusingtraceclusteringandprocessmining
AT zhangtom trackingchangesinstudentsonlineselfregulatedlearningbehaviorsandachievementgoalsusingtraceclusteringandprocessmining
AT chenzhongzhou trackingchangesinstudentsonlineselfregulatedlearningbehaviorsandachievementgoalsusingtraceclusteringandprocessmining