Knowledge tracing refers to a family of methods that estimate each student's knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate an \emph{overall} knowledge level of a student per knowledge component/skill since they analyze only the (usually binary-valued) correctness of student responses. Therefore, it is hard to use them to diagnose specific student errors. In this paper, we extend existing knowledge tracing methods beyond correctness prediction to the task of predicting the exact option students select in multiple choice questions. We quantitatively evaluate the performance of our option tracing methods on two large-scale student response datasets. We also qualitatively evaluate their ability in identifying common student errors in the form of clusters of incorrect options across different questions that correspond to the same error.
@article{arxiv.2104.09043,
title = {Option Tracing: Beyond Correctness Analysis in Knowledge Tracing},
author = {Aritra Ghosh and Jay Raspat and Andrew Lan},
journal= {arXiv preprint arXiv:2104.09043},
year = {2021}
}