Knowledge Tracing Over Time: A Longitudinal Analysis
Knowledge Tracing Over Time: A Longitudinal Analysis
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发表时间：
2023
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通讯作者：
Morgan P. Lee;Ethan A. Croteau;Ashish Gurung;Anthony F. Botelho;Neil T. Heffernan
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作者：
Morgan P. Lee;Ethan A. Croteau;Ashish Gurung;Anthony F. Botelho;Neil T. Heffernan
使用贝叶斯知识追踪（BKT）模型预测学生的学习和掌握，特别是在数学方面，是学习分析中一种成熟且经过验证的方法。在这项工作中，我们报告了我们的分析，检查跨学年归因于“检测器腐烂”的BKT模型的普遍性。我们比较知识培训（KT）模型的泛化能力，通过比较模型在学年内和跨学年预测学生知识的性能。模型的训练数据来自两个流行的开源课程，可通过开放教育资源。我们观察到，这些模型通常在预测学生在一个学年内的学习方面表现出色，而某些学年比其他学年更具有普遍性。我们认为，知识追踪模型在整个学年的表现方面相对稳定，但仍然容易受到系统性变化和潜在学习者行为的影响。正如本文中的证据所示，我们认为利用KT模型的学习平台需要注意某些用户人口统计数据的系统性变化或急剧变化。
The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to ”detector rot.” We compare the generalizability of Knowledge Training (KT) models by comparing model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curricula available through Open Educational Resources. We observed that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing models are relatively stable in terms of performance across academic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evidence in this paper, we posit that learning platforms leveraging KT models need to be mindful of systemic changes or drastic changes in certain user demographics.