基于学生数据生成智能提示，提升自适应辅导效果
Data-Driven Hints in Intelligent Tutoring Systems
- 从历史学习数据中自动提取下一步提示、路径点和策略目标
- 能判断最佳时机提供帮助，避免过早或过晚干预
- 融合大语言模型，拓展问题解决行为的动态适应能力
本章探讨了智能辅导系统（ITS）中数据驱动提示生成的发展。提示工厂（Hint Factory）与交互网络（Interaction Networks）使系统能够从学生历史数据中生成下一步提示、路径点和战略子目标。数据驱动方法还实现了提示提供的最佳时机判断。本文进一步探索基于行为问题解决数据的动态适应机制，并研究大语言模型（LLMs）的集成潜力。
原文摘要 · Abstract (English)
This chapter explores the evolution of data-driven hint generation for intelligent tutoring systems (ITS). The Hint Factory and Interaction Networks have enabled the generation of next-step hints, waypoints, and strategic subgoals from historical student data. Data-driven techniques have also enabled systems to find the right time to provide hints. We explore further potential data-driven adaptations for problem solving based on behavioral problem solving data and the integration of Large Language Models (LLMs).
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