Knowledge Modeling via Contextualized Representations for LSTM-based Personalized Exercise Recommendation

UM > Faculty of Science and Technology > DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE

Status	已發表Published
	Knowledge Modeling via Contextualized Representations for LSTM-based Personalized Exercise Recommendation
	Huo, Y.; Wong, F.; Chao, S.; Ni, M.; Zhang, J.
	2020-06-01
Source Publication	Information Sciences
ISSN	0020-0255
Pages	266-278
Abstract	Intelligent education systems have enabled personalized learning (PL). In PL, students are presented with educational contents that are consistent with their personal knowledge states (KS), and the critical task is accurately estimating these states through data. Knowledge tracing (KT) infers KS (latent) through historical student interactions (observed) with the knowledge components (KCs). A wide variety of KT techniques have been developed, from Bayesian Knowledge Tracing (BKT) to Deep Knowledge Tracing (DKT). How- ever, in most of these methods, the KCs are represented as stand-alone entities, and the effect of representing KCs using contexts such as learning-related factors has been under-investigated. Also, KT needs to generate personalized results to facilitate tasks such as exercise recommendation. In this paper, we propose two approaches that use a contextualized representation of KCs, one with a content-based approach and another with a Long Short Term Memory (LSTM) network plus a personalization mechanism. By performing extensive experiments on two real-world datasets, results show not only a tangible improvement in prediction accuracy in the KT task compared to existing methods, but also its effectiveness in improving the recommendation precision.
Keyword	Personalized learning Knowledge tracing LSTM Context representation Exercise recommendation
URL	View the original
Language	英語English
The Source to Article	PB_Publication
PUB ID	51231
Document Type	Journal article
Collection	DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Recommended Citation GB/T 7714	Huo, Y.,Wong, F.,Chao, S.,et al. Knowledge Modeling via Contextualized Representations for LSTM-based Personalized Exercise Recommendation[J]. Information Sciences, 2020, 266-278.
APA	Huo, Y.., Wong, F.., Chao, S.., Ni, M.., & Zhang, J. (2020). Knowledge Modeling via Contextualized Representations for LSTM-based Personalized Exercise Recommendation. Information Sciences, 266-278.
MLA	Huo, Y.,et al."Knowledge Modeling via Contextualized Representations for LSTM-based Personalized Exercise Recommendation".Information Sciences (2020):266-278.

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