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Knowledge tracing (KT) models are a popular approach for predicting students'future performance at practice problems using their prior attempts. Though manyinnovations have been made in KT, most models including the state-of-the-artDeep KT (DKT) mainly leverage each student's response either as correct orincorrect, ignoring its content. In this work, we propose Code-based DeepKnowledge Tracing (Code-DKT), a model that uses an attention mechanism toautomatically extract and select domain-specific code features to extend DKT.We compared the effectiveness of Code-DKT against Bayesian and Deep KnowledgeTracing (BKT and DKT) on a dataset from a class of 50 students attempting tosolve 5 introductory programming assignments. Our results show that Code-DKTconsistently outperforms DKT by 3.07-4.00% AUC across the 5 assignments, acomparable improvement to other state-of-the-art domain-general KT models overDKT. Finally, we analyze problem-specific performance through a set of casestudies for one assignment to demonstrate when and how code features improveCode-DKT's predictions.

Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks