Deep knowledge tracing with transformers

S Pu, M Yudelson, L Ou, Y Huang - … , AIED 2020, Ifrane, Morocco, July 6 …, 2020 - Springer
Artificial Intelligence in Education: 21st International Conference, AIED 2020 …, 2020Springer
In this work, we propose a Transformer-based model to trace students' knowledge
acquisition. We modified the Transformer structure to utilize 1) the association between
questions and skills and 2) the elapsed time between question steps. The use of question-
skill associations allows the model to learn specific representation for frequently
encountered questions while representing rare questions with their underline skill
representations. The inclusion of elapsed time opens the opportunity to address forgetting …
Abstract
In this work, we propose a Transformer-based model to trace students’ knowledge acquisition. We modified the Transformer structure to utilize 1) the association between questions and skills and 2) the elapsed time between question steps. The use of question-skill associations allows the model to learn specific representation for frequently encountered questions while representing rare questions with their underline skill representations. The inclusion of elapsed time opens the opportunity to address forgetting. Our approach outperforms the state-of-the-art methods in the literature by roughly 10% in AUC with frequently used public datasets.
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