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.

本研究针对教学设计者在众多设计选择中难以识别最有效干预措施的问题，提出了“人类学习者模型”的概念，旨在为设计者评估候选干预措施提供支持。研究首次成功演示了这一概念，表明计算模型能够准确预测人类 A/B 实验的结果，并生成学习曲线，为理解干预效果提供理论洞见。这些发现为未来整合认知与学习理论的“人类学习者模型”奠定了基础，支持针对多种任务和干预的教学设计。 

建模人类学习者：指导教学设计的计算模型