UPDATED: 1 FEB, 2021

Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing

Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim, Yeongmin Cha, Dongmin Shin, Chan Bae, Jaewe Heo

Knowledge Tracing(KT) comes in various forms, from data-driven algorithms to deep learning-based models. This study integrates a contemporary deep learning model called Transformers and applies it to student response data.


Knowledge Tracing models attempt to accurately determine a student’s current knowledge state, such as whether a student may respond correctly to a question
Separated Self-AttentIve Neural Knowledge Tracing(SAINT) utilizes an encoder-decoder structure that enables response sequences to stack attention layers multiple times, capturing the complex relations between educational exercises and student responses. Empirical evaluations show that SAINT attained state-of-the-art performance in our EdNet dataset.


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