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HeTROPY: Explainable learning diagnostics via heterogeneous maximum-entropy and multi-spatial knowledge representation
Huo,Yujia1; Wong,Derek F.1; Ni,Lionel M.2; Chao,Lidia S.1; Zhang,Jing3
2020-11-05
Source PublicationKnowledge-Based Systems
ISSN0950-7051
Volume207
Abstract

Autonomous learning diagnostics, where the students’ strengths and weaknesses are disclosed from their observed performance data, is a challenging task in e-learning systems. Current student knowledge models can alleviate some of the problems in learning (i.e. predicting student performance) but they neglect learning diagnostics, which is based on causal reasoning. To this end, we propose a novel heterogeneous attention interpreter with a maximum entropy regularizer on top of a student knowledge model to achieve explainable learning diagnostics. Our model segregates the impact of the homogeneous knowledge points, while promoting the heterogeneous relatives by maximizing their chance to contribute to the prediction. We also propose a multi-spatial knowledge representation that is readily generalizable to other data-driven educational tasks. Extensive experiments on real-world datasets reveal that the proposed method is able to enhance the model's explanatory power, hence increases the trustworthiness towards learning diagnostics. It also brings notable improvement in accuracy in the student performance prediction task. The findings in this paper are adoptable to various types of e-learning systems to assist teachers to gain insights into student learning states and diagnose learning problems.

KeywordCausal Reasoning Knowledge Representation Learning Diagnostics Relation Prediction
DOI10.1016/j.knosys.2020.106389
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000574944400002
Scopus ID2-s2.0-85089748555
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Citation statistics
Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF PORTUGUESE
Corresponding AuthorHuo,Yujia
Affiliation1.NLPCT Lab,Department of Computer and Information Science,University of Macau,China
2.Department of Computer Science and Engineering,Hong Kong University of Science and Technology,Hong Kong
3.Department of Portuguese,University of Macau,China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Huo,Yujia,Wong,Derek F.,Ni,Lionel M.,et al. HeTROPY: Explainable learning diagnostics via heterogeneous maximum-entropy and multi-spatial knowledge representation[J]. Knowledge-Based Systems,2020,207.
APA Huo,Yujia,Wong,Derek F.,Ni,Lionel M.,Chao,Lidia S.,&Zhang,Jing.(2020).HeTROPY: Explainable learning diagnostics via heterogeneous maximum-entropy and multi-spatial knowledge representation.Knowledge-Based Systems,207.
MLA Huo,Yujia,et al."HeTROPY: Explainable learning diagnostics via heterogeneous maximum-entropy and multi-spatial knowledge representation".Knowledge-Based Systems 207(2020).
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