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This paper applies data mining and machine learning to learners' interaction logs in a technology-enhanced learning system to flag low self-efficacy. It is exploratory, based on one experiment across two universities, and the abstract gives no quantitative results, so the claimed gains in prediction performance can't yet be judged. For ML readers, the main interest is the feature-engineering and modeling approach for behavioral data in education, along with the finding that self-efficacy is highly dynamic and may need frequent monitoring.
Using Machine Learning to Measure Self-Efficacy Belief Through Learners' Interaction Traces in Technology-Enhanced Learning
arXiv:2610.41900v1 Announce Type: new Abstract: Self-efficacy beliefs play a critical role in learners' performance, academic choices, and, more broadly, in their future career trajectories. As Technology-Enhanced Learning (TEL) environments have demonstrated strong potential for supporting both performance and motivation, investigating self-efficacy within such environments is of particular interest, especially for implementing remedial practices as early as possible to support learners' self-efficacy beliefs. Our research aims to develop a system capable of detecting low self-efficacy based on learners' interaction traces while they solve exercises in a TEL system. In this paper, we present initial results and future perspectives on a detection approach based on data mining and machine learning techniques to identify features indicative of low self-efficacy beliefs. Because the detection of learners' self-efficacy beliefs through their use of TEL systems remains largely unexplored, this work is exploratory in nature. To this end, we conducted an experiment across two universities to collect interaction traces and self-reported measures of self-efficacy. We then developed a machine learning pipeline comprising multiple classification models, enabling us to highlight the importance of closely monitoring of learners' self-efficacy beliefs for improved prediction performance, given their highly dynamic nature during learning.
Abstract:Self-efficacy beliefs play a critical role in learners' performance, academic choices, and, more broadly, in their future career trajectories. As Technology-Enhanced Learning (TEL) environments have demonstrated strong potential for supporting both performance and motivation, investigating self-efficacy within such environments is of particular interest, especially for implementing remedial practices as early as possible to support learners' self-efficacy beliefs. Our research aims to develop a system capable of detecting low self-efficacy based on learners' interaction traces while they solve exercises in a TEL system. In this paper, we present initial results and future perspectives on a detection approach based on data mining and machine learning techniques to identify features indicative of low self-efficacy beliefs. Because the detection of learners' self-efficacy beliefs through their use of TEL systems remains largely unexplored, this work is exploratory in nature. To this end, we conducted an experiment across two universities to collect interaction traces and self-reported measures of self-efficacy. We then developed a machine learning pipeline comprising multiple classification models, enabling us to highlight the importance of closely monitoring of learners' self-efficacy beliefs for improved prediction performance, given their highly dynamic nature during learning.
Artificial Intelligence (cs.AI)
arXiv:2610.41900 [cs.AI]
(or arXiv:2610.41900v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.41900 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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