Improving Knowledge Tracing via Pre-training Question Embeddings

Understand

Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response.

• Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately.
• In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings.
• To be specific, the side information includes question difficulty and three kinds of relations contained in a bipartite graph between questions and skills.