Knowledge tracing refers to the problem of estimating each student's knowledge component/skill mastery level from their past responses to questions in educational applications. One direct benefit knowledge tracing methods provide is the ability to predict each student's performance on the future questions. However, one key limitation of most existing knowledge tracing methods is that they treat student responses to questions as binary-valued, i.e., whether the responses are correct or

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... Response correctness analysis/prediction is easy to navigate but loses important information, especially for open-ended questions: the exact student responses can potentially provide much more information about their knowledge states than only response correctness. In this paper, we present our first exploration into open-ended knowledge tracing (OKT), i.e., the analysis and prediction of students' open-ended responses to questions in the knowledge tracing setup. We detail OKT's application to the domain of computer science education with programming exercises. We conduct a series of quantitative and qualitative experiments to test the boundaries of OKT on the real-world dataset for the Second CSEDM Data Challenge.