Using Deep Learning to Localize Errors in Student Code Submissions

Shion Fujimori, Mohamed Harmanani, Owais Siddiqui, Lisa Zhang

Abstract: We explore RNN and CodeBERT deep learning models that highlight errors in student submissions to Python coding problems. We find that a standard automatic metric like AUC does not correspond well to human evaluation, and that the scale of the benefits of transfer learning and pre-training are only seen when using human evaluation.