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Download full textKoedinger, Kenneth R.; McLaughlin, Elizabeth A.; Stamper, John C. – International Educational Data Mining Society, 2012
Student modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational…
Descriptors: Educational Technology, Intelligent Tutoring Systems, Educational Improvement, Mathematics
Goldin, Ilya M.; Koedinger, Kenneth R.; Aleven, Vincent – International Educational Data Mining Society, 2012
Although ITSs are supposed to adapt to differences among learners, so far, little attention has been paid to how they might adapt to differences in how students learn from help. When students study with an Intelligent Tutoring System, they may receive multiple types of help, but may not comprehend and make use of this help in the same way. To…
Descriptors: Performance Factors, Intelligent Tutoring Systems, Individual Differences, Prediction
Stamper, John C.; Lomas, Derek; Ching, Dixie; Ritter, Steve; Koedinger, Kenneth R.; Steinhart, Jonathan – International Educational Data Mining Society, 2012
Traditional experimental paradigms have focused on executing experiments in a lab setting and eventually moving successful findings to larger experiments in the field. However, data from field experiments can also be used to inform new lab experiments. Now, with the advent of large student populations using internet-based learning software, online…
Descriptors: Internet, Feedback (Response), Computer Software, Data Collection
