通过聚类建模学生行为差异，预测未来学习资源偏好。
Personalized Student Knowledge Modeling for Future Learning Resource Prediction
- 用聚类构建个性化学生画像，捕捉不同学习模式。
- 在两个真实数据集上验证，能显著提升资源预测准确率。
- 适合教育推荐系统、自适应学习平台研究者使用。
尽管深度学习在教育领域取得进展，但学生知识追踪与行为建模仍面临个性化不足、对非测评类学习活动（如讲座）建模不充分，以及知识掌握与行为模式关联忽视等问题。实际限制如固定长度序列分割常导致关键上下文信息丢失。依赖测评结果也限制了建模范围，忽略了非测评交互。为此，我们提出状态感知的多任务方法 KMaP，用于同时个性化建模学生知识与行为。KMaP 采用基于聚类的学生画像生成机制，构建个性化表示，提升对未来学习资源偏好的预测能力。在两个真实数据集上的大量实验表明，不同学生聚类间存在显著行为差异，并验证了 KMaP 模型的有效性。
原文摘要 · Abstract (English)
Despite advances in deep learning for education, student knowledge tracing and behavior modeling face persistent challenges: limited personalization, inadequate modeling of diverse learning activities (especially non-assessed materials), and overlooking the interplay between knowledge acquisition and behavioral patterns. Practical limitations, such as fixed-size sequence segmentation, frequently lead to the loss of contextual information vital for personalized learning. Moreover, reliance on student performance on assessed materials limits the modeling scope, excluding non-assessed interactions like lectures. To overcome these shortcomings, we propose Knowledge Modeling and Material Prediction (KMaP), a stateful multi-task approach designed for personalized and simultaneous modeling of student knowledge and behavior. KMaP employs clustering-based student profiling to create personalized student representations, improving predictions of future learning resource preferences. Extensive experiments on two real-world datasets confirm significant behavioral differences across student clusters and validate the efficacy of the KMaP model.
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