A Hybrid DistilBERT-GRU Based Regression System for Automated Student Answer Scoring

Grading student responses manually is a time-consuming process, particularly in resource-constrained schools. Current automated solutions employ keyword matching or classification, which does not account for semantic meaning, coherence, and partial correctness. The regression approach proposed here overcomes the need for strict categories and more accurately represents the gradual differences in answer quality. The problem of scoring student responses is framed as a regression problem, predicting scores ranging from 0 to 100. The proposed hybrid model integrates a pretrained DistilBERT encoder for semantic encoding with a one-way GRU to represent the coherence of the sequence and produce score predictions. Experiments were carried out with both frozen and fine-tuned encoder configurations to assess the performance under computational constraints. The model performed well on short and long-answer datasets across various subjects, and it made consistent predictions. Freezing the encoder decreased training time but impacted generalization performance. A major drawback is that the model does not handle implicit reasoning and rubric-based scoring criteria well.