arXiv cs.AI· Maeva Somny (LIRIS), Linda Chouati, Marie Lefevre·· 1 天前AI 评分18
用机器学习通过学习者交互轨迹测量技术增强学习中的自我效能信念
Using Machine Learning to Measure Self-Efficacy Belief Through Learners' Interaction Traces in Technology-Enhanced Learning
AI 导读
研究提出基于数据挖掘与机器学习的方法，从学习者在技术增强学习（TEL）系统中解题时的交互轨迹检测低自我效能信念。团队在两所大学开展实验，收集交互轨迹与自我效能自评数据，构建包含多个分类模型的机器学习流水线。结果显示，由于自我效能信念在学习过程中高度动态，密切监测学习者自我效能对提升预测性能很重要。
正文 · 原文
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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.41900 [cs.AI] |
| (or arXiv:2610.41900v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.41900 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | CELDA, IADIS, Oct 2026, Lisbonne, Portugal |
Submission history
From: Maeva SOMNY [view email] [via CCSD proxy]
[v1]
Fri, 30 Oct 2026 15:38:30 UTC (2,400 KB)
来源：arXiv cs.AI · arxiv.org