How Is Students’ Online Learning Behavior Related to Their Course Outcomes in an Introductory Physics Course?

Garrido, Geoffrey; Guthrie, Matthew W.; Chen, Zhongzhou

doi:10.2139/ssrn.3522731

Cited by 2 publications

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“…1.2.1 Level I: Clustering of Individual Events. Prior research on OLMs has shown that students' use of learning strategies may not be reflected by the order of events, but rather by the quality of events [4,9]. More specifically, an abnormally short assessment attempt is likely the result of random guessing or answer copying and may be indicative of the student adopting a performance-avoidance goal.…”

Section: Analysis Of Olm Trace Data Via Multi-level Clusteringmentioning

confidence: 99%

A Multi-Level Trace Clustering Analysis Scheme for Measuring Students' Self-Regulated Learning Behavior in a Master-Based Online Learning Environment

Zhang¹,

Taub²,

Chen³

2021

Preprint

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The study introduces a new analysis scheme to analyze trace data and visualize students' self-regulated learning strategies in a mastery-based online learning modules platform. The pedagogical design of the platform resulted in fewer event types and less variability in student trace data. The current analysis scheme overcomes those challenges by conducting three levels of clustering analysis. On the event level, mixture-model fitting is employed to distinguish between abnormally short and normal assessment attempts and study events. On the module level, trace level clustering is performed with three different methods for generating distance metrics between traces, with the best performing output used in the next step. On the sequence level, trace level clustering is performed on top of module-level clusters to reveal students' change of learning strategy over time. We demonstrated that distance metrics generated based on learning theory produced better clustering results than pure data-driven or hybrid methods. The analysis showed that most students started the semester with productive learning strategies, but a significant fraction shifted to a multitude of less productive strategies in response to increasing content difficulty and stress. The observations could prompt instructors to rethink conventional course structure and implement interventions to improve self-regulation at optimal times.

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Section: Analysis Of Olm Trace Data Via Multi-level Clusteringmentioning

confidence: 99%

A Multi-Level Trace Clustering Analysis Scheme for Measuring Students' Self-Regulated Learning Behavior in a Master-Based Online Learning Environment

Zhang¹,

Taub²,

Chen³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Clustering of individual events: Prior research on OLMs ( Chen et al, 2020 ; Garrido et al, 2020 ) has shown that an abnormally short assessment attempt is likely the result of random guessing or answer copying, perhaps indicative of the student adopting a performance avoidance goal. Therefore, the main goal of event-level clustering was to distinguish between abnormally short guessing attempts and normal problem-solving attempts.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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How Is Students’ Online Learning Behavior Related to Their Course Outcomes in an Introductory Physics Course?

Cited by 2 publications

References 0 publications

A Multi-Level Trace Clustering Analysis Scheme for Measuring Students' Self-Regulated Learning Behavior in a Master-Based Online Learning Environment

A Multi-Level Trace Clustering Analysis Scheme for Measuring Students' Self-Regulated Learning Behavior in a Master-Based Online Learning Environment

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

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