Evaluating the effectiveness of two methods to improve students' problem solving performance after studying an online tutorial

Chen, Zhongzhou; Whitcomb, Kyle M.; Guthrie, Matthew W.; Singh, Chandralekha

doi:10.1119/perc.2019.pr.chen

Cited by 2 publications

(1 citation statement)

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“…This design is motivated by both the “mastery-learning” format that allows students who are already familiar with the content to proceed quickly to the next assignment, and by the concept of “preparation for future learning” intending to improve students’ learning from the IC by exposing them to the questions first. It also provides better interpretability of student log data ( Chen et al, 2018b ) and allows for measurement knowledge transfer between consecutive modules ( Whitcomb et al, 2018 , 2021 ; Chen et al, 2019 ).…”

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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Section: Introductionmentioning

confidence: 99%