提出概念缺陷预测新任务，帮系统发现学生具体哪里不会。
ConceptKT: A Benchmark for Concept-Level Deficiency Prediction in Knowledge Tracing
- 用概念级标注数据，识别学生未来可能出错的知识点。
- 基于概念匹配和语义相似度选历史答题记录，效果更优。
- 适合做智能辅导、自适应学习系统的开发者参考。
知识追踪（Knowledge Tracing, KT）是支持个性化学习的关键技术，但现有系统多聚焦于预测答题对错，无法诊断导致错误的深层概念误解。为弥补这一不足，本文提出概念级缺陷预测任务，旨在识别学生在后续题目中可能遇到困难的具体知识点。为此，我们构建了ConceptKT数据集，其中包含每道题所需的概念标签以及错误回答背后的缺失概念标注。研究探索了上下文学习在KT中的应用，评估了多种大语言模型（LLMs）与大推理模型（LRMs）的诊断能力，并对比了不同历史记录筛选策略的效果。实验表明，依据概念一致性与语义相似度选择历史答题记录，可同时提升答题正确率预测与概念缺陷识别的性能。
原文摘要 · Abstract (English)
Knowledge Tracing (KT) is a critical technique for modeling student knowledge to support personalized learning. However, most KT systems focus on binary correctness prediction and cannot diagnose the underlying conceptual misunderstandings that lead to errors. Such fine-grained diagnostic feedback is essential for designing targeted instruction and effective remediation. In this work, we introduce the task of concept-level deficiency prediction, which extends traditional KT by identifying the specific concepts a student is likely to struggle with on future problems. We present ConceptKT, a dataset annotated with labels that capture both the concepts required to solve each question and the missing concepts underlying incorrect responses. We investigate in-context learning approaches to KT and evaluate the diagnostic capabilities of various Large Language Models (LLMs) and Large Reasoning Models (LRMs). Different strategies for selecting informative historical records are explored. Experimental results demonstrate that selecting response histories based on conceptual alignment and semantic similarity leads to improved performance on both correctness prediction and concept-level deficiency identification.
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