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Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D. – Grantee Submission, 2013
When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if…
Descriptors: Educational Research, Data Collection, Data Analysis, Generalizability Theory
Gobert, Janice D.; Baker, Ryan; Pedro, Michael Sao – Society for Research on Educational Effectiveness, 2011
The authors present work towards automatically assessing data collection behaviors as middle school students engage in inquiry within a physics microworld. In this study, the authors used machine learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using…
Descriptors: Physics, Middle School Students, Multiple Choice Tests, Data Collection
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Gobert, Janice D.; Sao Pedro, Michael; Raziuddin, Juelaila; Baker, Ryan S. – Journal of the Learning Sciences, 2013
We present a method for assessing science inquiry performance, specifically for the inquiry skill of designing and conducting experiments, using educational data mining on students' log data from online microworlds in the Inq-ITS system (Inquiry Intelligent Tutoring System; www.inq-its.org). In our approach, we use a 2-step process: First we use…
Descriptors: Intelligent Tutoring Systems, Science Education, Inquiry, Science Process Skills