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Learner Modeling for Integration Skills

Huang, Yun and Guerra-Hollstein, Julio and Barria-Pineda, Jordan and Brusilovsky, Peter (2017) Learner Modeling for Integration Skills. In: UMAP 2018, July 09 - 12, 2017, Bratislava, Slovakia.

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Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills. However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill. This risks early assertion of mastery or ineffective remediation failing to address skill integration. We introduce a novel integration-level approach to model learners' knowledge and provide fine-grained diagnosis: a Bayesian network based on a new kind of knowledge graph with progressive integration skills. We assess the value of such a model from multifaceted aspects: performance prediction, parameter plausibility, expected instructional effectiveness, and real-world recommendation helpfulness. Our experiments based on a Java programming tutor show that proposed model significantly improves two popular multiple-skill knowledge tracing models on all these four aspects.


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Item Type: Conference or Workshop Item (Paper)
Status: Published
CreatorsEmailPitt UsernameORCID
Huang, Yun
Guerra-Hollstein, Julio
Barria-Pineda, Jordan
Brusilovsky, Peterpeterb0000-0002-1902-1464
Date: 9 July 2017
Date Type: Publication
Page Range: pp. 85-93
Event Title: UMAP 2018
Event Dates: July 09 - 12, 2017
Event Type: Conference
DOI or Unique Handle: 10.1145/3079628.3079677
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Title of Book: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17
Official URL:
Date Deposited: 12 Dec 2018 19:02
Last Modified: 12 Dec 2018 19:02


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