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  • 标题:Random Forests for Evaluating Pedagogy and Informing Personalized Learning
  • 本地全文:下载
  • 作者:Kelly Spoon ; Joshua Beemer ; John C. Whitmer
  • 期刊名称:Journal of Educational Data Mining
  • 电子版ISSN:2157-2100
  • 出版年度:2016
  • 卷号:8
  • 期号:2
  • 页码:20-50
  • 出版社:International EDM Society
  • 摘要: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 on 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 students benefitting from the supplemental instruction section, develop an objective criterion to, at the beginning of the semester, identify and advise these students into that section, and suggest intervention initiatives for at-risk groups in the course.
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