A probabilistic model for knowledge component naming

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Proceedings titleProceedings of the 8th International Conference on Data Mining
ConferenceEDM2015, June 26-29 2015, Madrid, Spain
Pages608609; # of pages: 2
AbstractRecent years have seen significant advances in automatic identifcation of the Q-matrix necessary for cognitive diagnostic assessment. As data-driven approaches are introduced to identify latent knowledge components (KC) based on observed student performance, it becomes crucial to describe and interpret these latent KCs. We address the problem of naming knowledge components using keyword automatically extracted from item text. Our approach identifies the most discriminative keywords based on a simple probabilistic model. We show this is effective on a dataset from the PSLC datashop, outperforming baselines and retrieving unknown skill labels in nearly 50% of cases.
Publication date
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21275890
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Record identifierc6f1a143-fa35-4790-900b-6b6fd5772b95
Record created2015-07-23
Record modified2016-05-09
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