TY - GEN
T1 - Deep hierarchical knowledge tracing
AU - Wang, Tianqi
AU - Ma, Fenglong
AU - Gao, Jing
N1 - Funding Information:
This work is sponsored by NSF IIS-1553411. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Knowledge tracing is an essential and challenging task in intelligent tutoring systems, whose goal is to estimate students' knowledge state based on their responses to questions. Although many models for knowledge tracing task are developed, most of them depend on either concepts or items as input and ignore the hierarchical structure of items, which provides valuable information for the prediction of student learning results. In this paper, we propose a novel deep hierarchical knowledge tracing (DHKT) model exploiting the hierarchical structure of items. In the proposed DHKT model, the hierarchical relations between concepts and items are modeled by the hinge loss on the inner product between the learned concept embeddings and item embeddings. Then the learned embeddings are fed into a neural network to model the learning process of students, which is used to make predictions. The prediction loss and the hinge loss are minimized simultaneously during training process.
AB - Knowledge tracing is an essential and challenging task in intelligent tutoring systems, whose goal is to estimate students' knowledge state based on their responses to questions. Although many models for knowledge tracing task are developed, most of them depend on either concepts or items as input and ignore the hierarchical structure of items, which provides valuable information for the prediction of student learning results. In this paper, we propose a novel deep hierarchical knowledge tracing (DHKT) model exploiting the hierarchical structure of items. In the proposed DHKT model, the hierarchical relations between concepts and items are modeled by the hinge loss on the inner product between the learned concept embeddings and item embeddings. Then the learned embeddings are fed into a neural network to model the learning process of students, which is used to make predictions. The prediction loss and the hinge loss are minimized simultaneously during training process.
UR - https://www.scopus.com/pages/publications/85085987937
UR - https://www.scopus.com/pages/publications/85085987937#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85085987937
T3 - EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
SP - 671
EP - 674
BT - EDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
A2 - Lynch, Collin F.
A2 - Merceron, Agathe
A2 - Desmarais, Michel
A2 - Nkambou, Roger
PB - International Educational Data Mining Society
T2 - 12th International Conference on Educational Data Mining, EDM 2019
Y2 - 2 July 2019 through 5 July 2019
ER -