2022/06/07 by Yang Shi, Shi, Yang, Min Chi +5 · 2 citations
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Innovative Teaching and Learning Methods #Intelligent Tutoring Systems and Adaptive Learning #Online Learning and Analytics #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2206.03545
openalex publication_date 2022/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In this work, we propose Code-based Deep Knowledge Tracing (Code-DKT), a model that uses an attention mechanism to automatically extract and select domain-specific code features to extend DKT. We compared the effectiveness of Code-DKT against Bayesian and Deep Knowledge Tracing (BKT and DKT) on a dataset from a class of 50 students attempting to solve 5 introductory programming assignments. Our results show that Code-DKT consistently outperforms DKT by 3.07-4.00% AUC across the 5 assignments, a comparable improvement to other state-of-the-art domain-general KT models over DKT. Finally, we analyze problem-specific performance through a set of case studies for one assignment to demonstrate when and how code features improve Code-DKT's predictions.