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Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation
- Source :
- Information Sciences. 523:266-278
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
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). However, 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.
- Subjects :
- Information Systems and Management
Computer science
02 engineering and technology
Personalized learning
Machine learning
computer.software_genre
Theoretical Computer Science
Task (project management)
Personalization
Knowledge modeling
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Bayesian Knowledge Tracing
Personal knowledge base
Representation (mathematics)
business.industry
05 social sciences
050301 education
Computer Science Applications
Control and Systems Engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
0503 education
computer
Software
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 523
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........cccd3e8a71a2741ef4888add195d7a90