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dc.contributor.authorPedro, Michael Sao
dc.contributor.authorJiang, Yang
dc.contributor.authorPaquette, Luc
dc.contributor.authorBaker, Ryan S.
dc.contributor.authorGobert, Janice
dc.identifier.citationPedro, M. S., Jiang, Y., Paquette, L., Baker, R. S., & Gobert, J. (2014). Identifying Transfer of Inquiry Skills across Physical Science Simulations using Educational Data Mining. In Joseph L. Polman, Eleni A. Kyza, D. Kevin O'Neill, Iris Tabak, William R. Penuel, A. Susan Jurow, Kevin O'Connor, Tiffany Lee, and Laura D'Amico (Eds.). Learning and Becoming in Practice: The International Conference of the Learning Sciences (ICLS) 2014. Volume 1. Colorado, CO: International Society of the Learning Sciences, pp. 222-229.en_US
dc.description.abstractStudents conducted inquiry using simulations within a rich learning environment for 4 science topics. By applying educational data mining to students' log data, assessment metrics were generated for two key inqury skills, testing stated hypotheses and designing controlled experiments. Three models were then developed to analyze the transfer of these inquiry skills between science topics. Model one, Classic Bayesian Knowledge Tracing, assumes that either complete transfer of skill occurs or no transfer occurs; model two (BKT- PST), an extension of BKT, assumes partial transfer and tests that assumption; and model three, a variant of BKT-PST, assumes no transfer and tests this assumption. An analysis of models one and two suggest that transfer of these inquiry skills across topics did occur. This work makes contributions to methodological approaches for measuring fine-grained skills using log files, as well as to the literature on the domain-specificity vs. domain-generality of inquiry skills.en_US
dc.publisherBoulder, CO: International Society of the Learning Sciencesen_US
dc.titleIdentifying Transfer of Inquiry Skills across Physical Science Simulations using Educational Data Miningen_US
dc.typeBook chapteren_US
Appears in Collections:ICLS2014

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