Towards Improving Open Student Answer Assessment using Pretrained Transformers
Abstract
The transfer learning pretraining-finetuning paradigm has revolutionized the natural language processing field yielding state-of the art results in several subfields such as text classification and question answering. However, little work has been done investigating pretrained language models for the open student answer assessment task. In this paper, we fine tune pretrained T5, BERT, RoBERTa, DistilBERT, ALBERT and XLNet models on the DTGrade dataset which contains freely generated (or open) student answers together with judgment of their correctness. The experimental results demonstrated the effectiveness of these models based on the transfer learning pretraining-finetuning paradigm for open student answer assessment. An improvement of 8%15% in accuracy was obtained over previous methods. Particularly, a T5 based method led to state-of-the-art results with an accuracy and F1 score of 0.88.
Publication Title
Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS
Recommended Citation
Khayi, N., Rus, V., & Tamang, L. (2021). Towards Improving Open Student Answer Assessment using Pretrained Transformers. Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS, 34 https://doi.org/10.32473/flairs.v34i1.128483
