| Download | - View accepted manuscript: A probabilistic model for knowledge component naming (PDF, 548 KiB)
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| Author | Search for: Goutte, Cyril1; Search for: Léger, Serge1; Search for: Durand, Guillaume1 |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | EDM2015, June 26-29 2015, Madrid, Spain |
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| Abstract | Recent 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. |
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| Publication date | 2015 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 21275890 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | c6f1a143-fa35-4790-900b-6b6fd5772b95 |
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| Record created | 2015-07-23 |
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| Record modified | 2020-06-04 |
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