Evaluating different training interventions to determine which produce the best learning outcomes is one of the main challenges faced by instructional designers. Typically, these designers use A/B experiments to evaluate each intervention; however, it is costly and time consuming to run such studies. To address this issue, we explore how computational models of learning might support designers in reasoning causally about alternative interventions within a fractions tutor. We present an approach for automatically tuning models to specific individuals and show that personalized models make better predictions of students' behavior than generic ones. Next, we conduct simulations to generate counterfactual predictions of performance and learning for two students (high and low performing) in different versions of the fractions tutor. Our approach makes predictions that align with previous human findings, as well as testable predictions that might be evaluated with future human experiments.

翻译：评估不同训练干预措施以确定哪些能产生最佳学习效果，是教学设计者面临的主要挑战之一。通常，设计者采用A/B实验来评估每种干预措施，但此类研究成本高昂且耗时。为解决这一问题，我们探索了学习计算模型如何支持设计者在分数辅导系统中对替代性干预措施进行因果推理。我们提出了一种将模型自动调优至特定个体的方法，并证明个性化模型比通用模型能更准确地预测学生行为。随后，我们通过模拟为两名学生（高表现者与低表现者）在不同版本的分数辅导系统中生成关于表现与学习的反事实预测。我们的方法所得预测结果与先前人类研究结论一致，同时提出了可供未来人类实验验证的可检验预测。