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Title: Deep Hierarchical Knowledge Tracing
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.
Authors:
; ;
Award ID(s):
1724889
Publication Date:
NSF-PAR ID:
10157350
Journal Name:
Proceedings of the 12th International Conference on Educational Data Mining
Page Range or eLocation-ID:
667-670
Sponsoring Org:
National Science Foundation
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