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Attention-Based Knowledge Tracing with Heterogeneous Information Network Embedding

Journal Article


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Abstract


  • 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 to the current state by using attention mechanism. Experimental results on four benchmark datasets verify the superiority of our method for knowledge tracing.

UOW Authors


  •   Zhang, Nan (external author)
  •   Du, Ye (external author)
  •   Deng, Ke (external author)
  •   Li, Li (external author)
  •   Shen, Jun
  •   Sun, Geng (external author)

Publication Date


  • 2020

Citation


  • Zhang, N., Du, Y., Deng, K., Li, L., Shen, J. & Sun, G. (2020). Attention-Based Knowledge Tracing with Heterogeneous Information Network Embedding. Lecture Notes in Computer Science, 12274 95-103.

Scopus Eid


  • 2-s2.0-85090096251

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5366&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4337

Number Of Pages


  • 8

Start Page


  • 95

End Page


  • 103

Volume


  • 12274

Place Of Publication


  • Germany

Abstract


  • 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 to the current state by using attention mechanism. Experimental results on four benchmark datasets verify the superiority of our method for knowledge tracing.

UOW Authors


  •   Zhang, Nan (external author)
  •   Du, Ye (external author)
  •   Deng, Ke (external author)
  •   Li, Li (external author)
  •   Shen, Jun
  •   Sun, Geng (external author)

Publication Date


  • 2020

Citation


  • Zhang, N., Du, Y., Deng, K., Li, L., Shen, J. & Sun, G. (2020). Attention-Based Knowledge Tracing with Heterogeneous Information Network Embedding. Lecture Notes in Computer Science, 12274 95-103.

Scopus Eid


  • 2-s2.0-85090096251

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5366&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4337

Number Of Pages


  • 8

Start Page


  • 95

End Page


  • 103

Volume


  • 12274

Place Of Publication


  • Germany