The field of machine learning and programming education is moving towards developing more robust and fairness-aware methods. Researchers are working to address the challenges of noisy and imbalanced data, which can significantly limit model performance and application. Recent developments have focused on improving fairness in doubly imbalanced datasets and correcting label noise while preserving demographic parity. Additionally, there is a growing interest in enhancing programming knowledge tracking models to identify and mitigate the impact of noise in programming activities. Notable papers include:

• Coda, a Code graph-based tuning adaptor that effectively performs programming knowledge tracking in the presence of noisy programming records.
• GFLC, a Graph-based Fairness-aware Label Correction method that corrects label noise while preserving demographic parity. These advancements have the potential to improve the effectiveness and trustworthiness of machine learning systems and programming education.