AIED 2023: A SHAP-inspired method for computing interaction contribution in deep knowledge tracing
AIED is a conference I had always wanted to participate in, along with CHI. Most researchers hope to publish in Nature, but to me, the dream is AIED and CHI. The awesome Enrique who spent a summer with Cross Lab in Tokyo (and much longer with the lab online because of the pandemic) finally made it happen with his work that we submitted as a late-breaking result.
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Details
Title: A SHAP-inspired method for computing interaction contribution in deep knowledge tracing
Authors: Enrique Valero-Leal, May Kristine Jonson Carlon, Jeffrey S. Cross
Date: July 3 to 7, 2023
Abstract
Deep knowledge tracing (DKT) consists of predicting the probability of correctly answering a test or quiz question using the history of a particular learner's previous question-answer interactions. The probability of a correct answer is computed using a complex recurrent neural network. In this work, an approach similar to Shapley Additive exPlanations (SHAP) to better understand DKT was used. The number of skills a learner must master to lead to improved learning outcomes in an explainable manner was first reduced. Then, the impact of subsequences rather than every single interaction is studied, as simpler results are expected to be easier to understand. Results help to highlight both subsequences in which the student acquired knowledge and in which its progress stagnated.
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