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ERIC Number: ED560874
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 15
Variations in Learning Rate: Student Classification Based on Systematic Residual Error Patterns across Practice Opportunities
Liu, Ran; Koedinger, Kenneth R.
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a parameter that individualizes for overall student ability but not for student learning rate. Here, we show that adding a per-student learning rate parameter to AFM overall does not improve predictive accuracy. In contrast, classifying students into three "learning rate" groups using residual error patterns, and adding a per-group learning rate parameter to AFM, substantially and consistently improves predictive accuracy across 8 datasets spanning the domains of Geometry, Algebra, English grammar, and Statistics. In a subset of datasets for which there are pre- and post-test data, we observe a systematic relationship between learning rate group and pre-topost-test gains. This suggests there is both predictive power and external validity in modeling these distinct learning rate groups. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED); National Science Foundation (NSF)
Authoring Institution: International Educational Data Mining Society
Grant or Contract Numbers: R305B110003; SBE0836012