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Random Forests for Evaluating Pedagogy and Informing Personalized Learning

Kelly Spoon, Joshua Beemer, John C. Whitmer, Juanjuan Fan, James P. Frazee, Jeanne Stronach, Andrew J. Bohonak, Richard A. Levine
2016 Zenodo  
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
more » ... tional 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.
doi:10.5281/zenodo.3554595 fatcat:vxoumck6tfayxktcranfowosxe