Random Forests for Evaluating Pedagogy and Informing Personalized Learning



Published Dec 25, 2016
Kelly Spoon Joshua Beemer John C. Whitmer Juanjuan Fan James P. Frazee Jeanne Stronach Andrew J. Bohonak Richard A. Levine


Random forests are presented as an analytics foundation for educational data mining tasks. The focus is on course- and program-level analytics including evaluating pedagogical approaches and interventions and identifying and characterizing at-risk students. As part of this development, the concept of individualized treatment effects (ITE) is introduced as a method to provide personalized feedback to students. The ITE quantifies the effectiveness of intervention and/or instructional regimes for a particular student based on institutional student information and performance data. The proposed random forest framework and methods are illustrated in the context of a study of the efficacy of a supplemental, weekly, one-unit problem-solving session in a large enrollment, bottleneck introductory statistics course. The analytics tools are used to identify factors for student success, characterize the benefits of a supplemental instruction section, and suggest intervention initiatives for at-risk groups in the course. In particular, we develop an objective criterion to determine which students should be encouraged, at the beginning of the semester, to join a supplemental instruction section.

How to Cite

Spoon, K., Beemer, J., Whitmer, J. C., Fan, J., Frazee, J. P., Stronach, J., Bohonak, A. J., & Levine, R. A. (2016). Random Forests for Evaluating Pedagogy and Informing Personalized Learning. Journal of Educational Data Mining, 8(2), 20–50. https://doi.org/10.5281/zenodo.3554595
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random forest, individualized treatment effect, problem solving, statistics education

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