发表机构
Teachers College, Columbia University（哥伦比亚大学教师学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将最优动态治疗方案（ODTR）方法应用于高中数学课程个性化推荐，结合集成机器学习与靶向最大似然估计，通过可行性约束生成可行方案，为教育领域ODTR设计提供教程与应用参考。
AI 中文摘要
最优动态治疗方案（ODTR）正越来越多地用于生成个性化推荐序列，以最大化最终关注结果。尽管ODTR在生物统计学和精准医学中应用广泛，但其在教育领域的应用仍有限，尤其针对纵向决策场景。本研究将单阶段和多阶段设计的ODTR方法转化为高中数学课程的个性化推荐，利用2009年高中纵向研究（HSLS:09）的数据提供逐步指南和演示。我们采用集成机器学习的靶向最大似然估计来估计ODTR，以最大化学生的数学成绩和大学入学率，同时基于领域知识和倾向得分阈值施加可行性约束，确保推荐方案可实际执行。结果显示，无约束方案虽产生更高的估计值，但推荐方案不可行；而可行方案则提供了符合实证和专家意见支持的现实路径。最后，我们讨论了在教育领域设计ODTR的实际考量因素。
英文摘要
Optimal dynamic treatment regimes (ODTRs) are increasingly used to generate sequences of personalized recommendations that maximize a final outcome of interest. Although ODTRs are widely used in biostatistics and precision medicine, their applications in education remain limited, especially for longitudinal decision-making. This study translates ODTR methods for single- and multi-stage designs into individualized high school math course recommendations, providing step-by-step guidelines and demonstrations with data from the High School Longitudinal Study of 2009 (HSLS:09). We use Targeted Maximum Likelihood Estimation with ensemble machine learning to estimate ODTRs that maximize students' math achievement and college enrollment, imposing feasibility constraints based on domain knowledge and propensity-score thresholds to ensure recommendations are practically implementable. Results show that unconstrained regimes yield higher estimated values but infeasible recommendations, while feasible regimes produce realistic pathways with stronger empirical and expert-informed support. Finally, we discuss practical considerations for designing ODTRs in education.
CommentsAccepted for publication in Zeitschrift fur Psychologie (in press)
