Interactive Code Information Integrated Programming Knowledge Tracing

Abstract

Programming Knowledge Tracing (PKT) aims to estimate students' programming proficiency by analyzing their historical coding activities. It is important to leverage interactive information for PKT code representations, as code submissions are problem-specific interactive solutions evaluated by Online Judge (OJ) feedback. However, most existing PKT models only rely on pre-trained language models to capture static code information, overlooking feedback scores and problem-specific context. To address this issue, we propose an Interactive Information integrated Code Embedding for Programming Knowledge Tracing (IICE-PKT). This model learns a unified code representation by integrating comprehensive interactive information, including code text, OJ feedback scores, problem statement text, problem-skill correlations, and problem difficulties. IICE-PKT consists of three modules: Problem Representation module generates enhanced problem embeddings based on problem-skill correlations and problem difficulties; Code Representation module combines fine-tuned CodeBERT-based code text embeddings, GPT-generated problem text embeddings, and enhanced problem embeddings with supervised feedback scores to produce a unified code representation; Dual-Sequence Modeling module employs two independent GRUs to separately model the problem sequence and the code sequence. Extensive experiments demonstrate the superiority of IICE-PKT. Ablation studies confirm that integrating interactive information into code representations significantly enhances effectiveness.