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ERIC Number: EJ1125805
Record Type: Journal
Publication Date: 2016
Pages: 31
Abstractor: As Provided
ISBN: N/A
ISSN: EISSN-2157-2100
EISSN: N/A
Random Forests for Evaluating Pedagogy and Informing Personalized Learning
Spoon, Kelly; Beemer, Joshua; Whitmer, John C.; Fan, Juanjuan; Frazee, James P.; Stronach, Jeanne; Bohonak, Andrew J.; Levine, Richard A.
Journal of Educational Data Mining, v8 n2 p20-50 2016
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.
International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://jedm.educationaldatamining.org/index.php/JEDM
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: California (San Diego)
Grant or Contract Numbers: N/A