用计算模型预测教学干预效果，帮设计者选最优方案。
Model Human Learners: Computational Models to Guide Instructional Design
- 构建人类学习者计算模型，统一模拟学习过程。
- 准确预测两个真人实验的干预结果，误差小。
- 无需真实数据生成学习曲线，适合教育设计参考。
教学设计面临众多决策选择，难以判断最优干预措施。为此，本文提出「模型化人类学习者」概念——一种统一的计算学习模型，可辅助设计者评估教学干预方案。这是该概念首次成功验证：模型能准确预测两项真人A/B实验的结果，分别测试问题排序与题目设计干预；同时，模型可在无真实数据情况下生成学习曲线，并揭示干预有效的理论机制。这些成果为未来融合认知与学习理论的通用型模型奠定基础，适用于多样化任务与教学设计场景。
原文摘要 · Abstract (English)
Instructional designers face an overwhelming array of design choices, making it challenging to identify the most effective interventions. To address this issue, I propose the concept of a Model Human Learner, a unified computational model of learning that can aid designers in evaluating candidate interventions. This paper presents the first successful demonstration of this concept, showing that a computational model can accurately predict the outcomes of two human A/B experiments -- one testing a problem sequencing intervention and the other testing an item design intervention. It also demonstrates that such a model can generate learning curves without requiring human data and provide theoretical insights into why an instructional intervention is effective. These findings lay the groundwork for future Model Human Learners that integrate cognitive and learning theories to support instructional design across diverse tasks and interventions.
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