基于知识追踪生成个性化习题，提升学习效果
KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing
- 用知识追踪模型分析学生掌握情况，选最需练习的知识点
- 在XES3G5M和MOOCRadar数据集上，生成习题有效性显著提升
- 适合教育AI、智能辅导系统开发者参考
教育习题生成（EQG）旨在生成定制化习题以促进学生学习。理想的EQG系统应根据学生知识状态进行个性化，生成最具学习价值的题目。然而，现有方法难以实现细粒度个性化。本文探索将知识追踪（KT）引入EQG，通过历史表现建模学生知识状态并预测未来表现。提出KT4EQG框架，利用KT模型选择对个体学生最合适的知识点进行练习，并训练基于大语言模型的生成器，产出与所选概念高度一致的题目。在XES3G5M和MOOCRadar数据集上的实验表明，相较于缺乏或仅有有限个性化的方法，KT4EQG持续生成更有效的习题。
原文摘要 · Abstract (English)
Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning. An effective EQG system should ideally personalize questions for each student by modeling the student's knowledge state and generating questions that provide the greatest learning benefit. However, few existing EQG approaches are able to achieve such fine-grained personalization. In this paper, we explore how EQG can benefit from knowledge tracing (KT), which models students' knowledge states based on historical performance and predicts future performance. We propose KT4EQG, a personalized EQG framework that generates effective questions for individual students under the guidance of a KT model. Specifically, KT4EQG seeks to maximize a student's potential improvement in overall knowledge mastery by leveraging the KT model to select the most suitable knowledge concept for the student to practice. An LLM-based question generator is then trained to produce a question faithfully grounded in the selected concept. Experimental results on XES3G5M and MOOCRadar show that KT4EQG consistently generates more effective questions than methods with limited or no personalization.
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