用深度学习追踪学生知识，帮黑人高校理工科学生提前预警学业风险。
Deep Knowledge Tracing for Personalized Adaptive Learning at Historically Black Colleges and Universities
- 基于多模型深度知识追踪，分析黑人高校理工科学生学习数据。
- 萨克特和KQN模型在准确率与AUC上表现最优，预测效果显著。
- 适合教育研究者、教学管理者用于学生学业干预与提升毕业率。
个性化自适应学习（PAL）通过密切跟踪学生个体进展，为其定制学习路径。知识追踪是实现有效PAL的核心技术，可建模学生知识演变以预测未来表现。近年来，深度学习推动了深度知识追踪（DKT）的发展。然而，针对历史黑人高校（HBCUs）理工科教育的DKT研究仍较匮乏。本研究构建了一个综合性数据集，探究在HBCUs理工科教育中实施PAL的深度知识追踪方法，采用多种当前最优（SOTA）DKT模型进行性能评估。数据集涵盖普莱里维尤农工大学（PVAMU）八所学院共17,181名本科生的352,148条学习记录。所用模型包括DKT、DKT+、DKVMN、SAKT和KQN。实验结果表明，DKT模型能有效预测学生学术表现，其中SAKT与KQN在准确率和AUC指标上优于其他模型。研究结果对教师和学术顾问具有重要启示，有助于在学期结束前识别学业风险学生，实现主动干预，有望提升学生留校率与毕业率。
原文摘要 · Abstract (English)
Personalized adaptive learning (PAL) stands out by closely monitoring individual students' progress and tailoring their learning paths to their unique knowledge and needs. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Recent advancements in deep learning have significantly enhanced knowledge tracing through Deep Knowledge Tracing (DKT). However, there is limited research on DKT for Science, Technology, Engineering, and Math (STEM) education at Historically Black Colleges and Universities (HBCUs). This study builds a comprehensive dataset to investigate DKT for implementing PAL in STEM education at HBCUs, utilizing multiple state-of-the-art (SOTA) DKT models to examine knowledge tracing performance. The dataset includes 352,148 learning records for 17,181 undergraduate students across eight colleges at Prairie View A&M University (PVAMU). The SOTA DKT models employed include DKT, DKT+, DKVMN, SAKT, and KQN. Experimental results demonstrate the effectiveness of DKT models in accurately predicting students' academic outcomes. Specifically, the SAKT and KQN models outperform others in terms of accuracy and AUC. These findings have significant implications for faculty members and academic advisors, providing valuable insights for identifying students at risk of academic underperformance before the end of the semester. Furthermore, this allows for proactive interventions to support students' academic progress, potentially enhancing student retention and graduation rates.
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