用图模型分析学生学习行为，精准识别落后者。
Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems
- 构建课程结构图，自监督学习学生行为模式。
- 可全面追踪学习进度、强度与概念掌握差异。
- 适合教育研究者和智能辅导系统开发者使用。
智能辅导系统在课程化在线学习中的广泛应用，虽有助于教学，却可能加剧学生成绩差距。为应对这一问题，对学生进行行为画像至关重要，以跟踪进展、识别学习困难者并缓解差异。此类画像需衡量学生在内容覆盖、学习强度及知识点掌握等方面的多维度表现。本文提出CTGraph，一种基于图的自监督表示学习方法，用于刻画学习者行为与表现。实验表明，该方法能全面呈现学生的学习历程，涵盖行为与表现的多方面特征及其与课程结构一致的学习路径差异。此外，该方法可有效识别学习困难学生，并对不同群体进行对比分析，定位其学习瓶颈出现的时间与环节。这为教育者提供丰富洞察，助力开展精准干预。
原文摘要 · Abstract (English)
The surge in the adoption of Intelligent Tutoring Systems (ITSs) in education, while being integral to curriculum-based learning, can inadvertently exacerbate performance gaps. To address this problem, student profiling becomes crucial for tracking progress, identifying struggling students, and alleviating disparities among students. Such profiling requires measuring student behaviors and performance across different aspects, such as content coverage, learning intensity, and proficiency in different concepts within a learning topic. In this study, we introduce CTGraph, a graph-level representation learning approach to profile learner behaviors and performance in a self-supervised manner. Our experiments demonstrate that CTGraph can provide a holistic view of student learning journeys, accounting for different aspects of student behaviors and performance, as well as variations in their learning paths as aligned to the curriculum structure. We also show that our approach can identify struggling students and provide comparative analysis of diverse groups to pinpoint when and where students are struggling. As such, our approach opens more opportunities to empower educators with rich insights into student learning journeys and paves the way for more targeted interventions.
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