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Student Model Adjustment Through Random-Restart Hill Climbing

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dc.contributor.authorDoost, Ahmad Salimde_DE
dc.contributor.authorMelis, Ericade_DE
dc.contributor.editorHartmann, Melaniede_DE
dc.contributor.editorHerder, Eelcode_DE
dc.contributor.editorKrause, Danielde_DE
dc.contributor.editorNauerz, Andreasde_DE
dc.date.accessioned2017-11-15T15:01:01Z
dc.date.available2017-11-15T15:01:01Z
dc.date.issued2010
dc.description.abstractACTIVEMATH is a web-based intelligent tutoring system (ITS) for studying mathematics. Its course generator, which assembles content to personalized books, strongly depends on the underlying student model. Therefore, a student model is important to make an ITS adaptive. The more accurate it is, the better could be the adaptation. Here we present which parameters can be optimized and how they can be optimized in an efficient and affordable manner. This methodology can be generalized beyond ACTIVEMATH’s student model. We also present our results for the optimization based on two sets of log data. Our optimization method is based on random-restart hill climbing and it considerably improved the student model’s accuracy.
dc.identifier.urihttp://abis.l3s.uni-hannover.de/images/proceedings/abis2010/abis6.pdfde_DE
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/5093
dc.language.isoende_DE
dc.relation.ispartof18th Intl. Workshop on Personalization and Recommendation on the Web and Beyondde_DE
dc.titleStudent Model Adjustment Through Random-Restart Hill Climbingde_DE
dc.typeText/Conference Paper
gi.document.qualitydigidoc

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ABIS 2010 – 18th Intl. Workshop on Personalization and Recommendation on the Web and Beyond