In this paper, we study knowledge tracing in the domain of programming
education and make two important contributions. First, we harvest and publish
so far the most comprehensive dataset, namely BePKT, which covers various
online behaviors in an OJ system, including programming text problems,
knowledge annotations, user-submitted code and system-logged events. Second, we
propose a new model PDKT to exploit the enriched context for accurate student
behavior prediction. More specifically, we construct a bipartite graph for
programming problem embedding, and design an improved pre-training model
PLCodeBERT for code embedding, as well as a double-sequence RNN model with
exponential decay attention for effective feature fusion. Experimental results
on the new dataset BePKT show that our proposed model establishes
state-of-the-art performance in programming knowledge tracing. In addition, we
verify that our code embedding strategy based on PLCodeBERT is complementary to
existing knowledge tracing models to further enhance their accuracy. As a side
product, PLCodeBERT also results in better performance in other
programming-related tasks such as code clone detection.

本文在编程教育领域研究知识追踪，提出了一个新的模型 PDKT 以及一个最全面的数据集 BePKT，实验结果显示该模型在编程知识追踪方面表现突出，且码嵌入策略基于 PLCodeBERT 对于提高准确性有补充作用。