Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing
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by
Hanshuang Tong, Yun Zhou, Zhen Wang, Ben Teng
2021
Abstract
Knowledge tracing (KT) which aims at predicting learner's knowledge mastery
plays an important role in the computer-aided educational system. In recent
years, many deep learning models have been applied to tackle the KT task, which
has shown promising results. However, limitations still exist. Most existing
methods simplify the exercising records as knowledge sequence, which fails to
explore rich information existed in exercise texts. Besides, the latent
hierarchical graph nature of exercises and knowledge remains unexplored. Thus,
in this paper, we propose a hierarchical graph knowledge tracing model
framework (HGKT) which can leverage the advantages of hierarchical exercise
graph and of sequence model to enhance the ability of knowledge tracing.
Besides, we introduce the concept of problem schema to better represent a group
of similar exercises and propose a hierarchical graph neural network to learn
representations of problem schemas. Moreover, in the sequence model, we employ
two attention mechanisms to highlight important historical states of students.
In the testing stage, we present a K\&S diagnosis matrix that could trace the
transition of mastery of knowledge and problem schema, which can be more easily
applied to different applications. Extensive experiments show the effectiveness
and interpretability of our proposed models.
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2006.16915v4
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