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Full-Text Articles in Social and Behavioral Sciences
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Attention-Based Knowledge Tracing With Heterogeneous Information Network Embedding, Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, Geng Sun
Faculty of Engineering and Information Sciences - Papers: Part B
Knowledge tracing is a key area of research contributing to personalized education. In recent times, deep knowledge tracing has achieved great success. However, the sparsity of students’ practice data still limits the performance and application of knowledge tracing. An additional complication is that the contribution of the answer record to the current knowledge state is different at each time step. To solve these problems, we propose Attention-based Knowledge Tracing with Heterogeneous Information Network Embedding (AKTHE). First, we describe questions and their attributes with a heterogeneous information network and generate meaningful node embeddings. Second, we capture the relevance of historical data …
Developing An Ontology For Representing The Domain Knowledge Specific To Non-Pharmacological Treatment For Agitation In Dementia, Zhenyu Zhang, Ping Yu, H.C. Chang, S K. Lau, Cui Tao, Ning Wang, Mengyang Yin, Chao Deng
Developing An Ontology For Representing The Domain Knowledge Specific To Non-Pharmacological Treatment For Agitation In Dementia, Zhenyu Zhang, Ping Yu, H.C. Chang, S K. Lau, Cui Tao, Ning Wang, Mengyang Yin, Chao Deng
Faculty of Engineering and Information Sciences - Papers: Part B
Introduction: A large volume of clinical care data has been generated for managing agitation in dementia. However, the valuable information in these data has not been used effectively to generate insights for improving the quality of care. Application of artificial intelligence technologies offers us enormous opportunities to reuse these data. For health data science to achieve this, this study focuses on using ontology to coding clinical knowledge for non-pharmacological treatment of agitation in a machine-readable format. Methods: The resultant ontology—Dementia-Related Agitation Non-Pharmacological Treatment Ontology (DRANPTO)—was developed using a method adopted from the NeOn methodology. Results: DRANPTO consisted of 569 concepts …