From Sparse to Smart: Leveraging AI for Effective Online Judge Problem Classification in Programming Education

Online Judges (OJs) have gained substantial traction in programming education due to their ability to simultaneously present problem-solving challenges to students while offering instant feedback and correction. Such technologies are also essential to allow students in remote areas to access quality and equitable education. Nonetheless, OJ systems often lack sufficient amounts of annotated data (i.e., labelled data) about the topics of the problems that they aim to support, which makes choosing appropriate problems hard. Topic annotations hold significant value for instructors when selecting problems for assignments and for novice students seeking independent use of OJ systems. In this work, we propose and evaluate a pre-trained deep learning architecture and an active learning methodology to automatically annotate OJ problems in the context of introductory programming. Our results show that, when using a smaller amount of data, the methodology demonstrates performance comparable to those of the existing state-of-the-art methods for the identical task.