In recent years, instructional practices in Operations Research (OR), Management Science (MS), and Analytics have increasingly shifted toward digital environments, where large and diverse groups of learners make it difficult to provide practice that adapts to individual needs. This paper introduces a method that generates personalized sequences of exercises by selecting, at each step, the exercise most likely to advance a learner's understanding of a targeted skill. The method uses information about the learner and their past performance to guide these choices, and learning progress is measured as the change in estimated skill level before and after each exercise. Using data from an online mathematics tutoring platform, we find that the approach recommends exercises associated with greater skill improvement and adapts effectively to differences across learners. From an instructional perspective, the framework enables personalized practice at scale, highlights exercises with consistently strong learning value, and helps instructors identify learners who may benefit from additional support.

翻译：近年来，运筹学、管理科学与分析学的教学实践日益向数字化环境转移，庞大而多样化的学习者群体使得提供适应个体需求的练习变得困难。本文提出一种方法，通过在每个学习步骤中选择最有可能提升学习者目标技能理解度的练习，生成个性化练习序列。该方法利用学习者信息及其历史表现来指导选择，学习进度通过每次练习前后估计技能水平的变化来衡量。基于在线数学辅导平台的数据，我们发现该方法推荐的练习能带来更大的技能提升，并能有效适应学习者的个体差异。从教学视角看，该框架实现了规模化个性化练习，突显了具有持续高学习价值的练习，并帮助教师识别可能需要额外支持的学习者。