用模拟学习者评估自适应阅读材料，提升计算机科学学习效果
Evaluating Adaptive Personalization of Educational Readings with Simulated Learners
- 基于知识组件构建学习目标本体，标注教材片段
- 模拟学习者通过记忆模型生成答案，BKT驱动个性化调整
- 在计算机科学中显著提升效果，其他学科效果有限
我们提出一个基于理论的模拟学习者框架，用于评估教育阅读材料的自适应个性化。系统从开放教材中构建学习目标与知识组件本体，通过浏览器端的本体图谱进行整理，将教材段落标注为本体实体，并生成匹配的阅读-测评对。模拟学习者通过受建构-整合启发的记忆模型学习，融合DIME风格的读者因素、KREC风格的误解修正机制以及开源New Dale-Chall可读性信号。答案基于学习者显式记忆状态的得分选择，而BKT模型驱动内容适应。在三个选中的学科本体上，每组50名模拟学习者条件下，自适应阅读在计算机科学中显著提升表现，在无机化学中产生较小但不明确的正向收益，在普通生物学中则无明显影响或略有负面影响。
原文摘要 · Abstract (English)
We present a framework for evaluating adaptive personalization of educational reading materials with theory-grounded simulated learners. The system builds a learning-objective and knowledge-component ontology from open textbooks, curates it in a browser-based Ontology Atlas, labels textbook chunks with ontology entities, and generates aligned reading-assessment pairs. Simulated readers learn from passages through a Construction-Integration-inspired memory model with DIME-style reader factors, KREC-style misconception revision, and an open New Dale-Chall readability signal. Answers are produced by score-based option selection over the learner's explicit memory state, while BKT drives adaptation. Across three sampled subject ontologies and matched cohorts of 50 simulated learners per condition, adaptive reading significantly improved outcomes in computer science, yielded smaller positive but inconclusive gains in inorganic chemistry, and was neutral to slightly negative in general biology.
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