# Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer Grading

arXiv: https://arxiv.org/abs/2009.01303 · HTML: https://mlsanity.com/paper/2009.01303/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers](https://mlsanity.com/paper/2204.03503/) (2022)
- [Who's the Best Detective? LLMs vs. MLs in Detecting Incoherent Fourth Grade Math Answers](https://mlsanity.com/paper/2304.11257/) (2023)
- [Discovering Elementary Discourse Units in Textual Data Using Canonical Correlation Analysis](https://mlsanity.com/paper/2406.12997/) (2024)
- [Grade Guard: A Smart System for Short Answer Automated Grading](https://mlsanity.com/paper/2504.01253/) (2025)
- [AI-Enabled grading with near-domain data for scaling feedback with human-level accuracy](https://mlsanity.com/paper/2512.04113/) (2025)

## Abstract (arXiv)

Automatic Short Answer Grading (ASAG) is the process of grading the student answers by computational approaches given a question and the desired answer. Previous works implemented the methods of concept mapping, facet mapping, and some used the conventional word embeddings for extracting semantic features. They extracted multiple features manually to train on the corresponding datasets. We use pretrained embeddings of the transfer learning models, ELMo, BERT, GPT, and GPT-2 to assess their efficiency on this task. We train with a single feature, cosine similarity, extracted from the embeddings of these models. We compare the RMSE scores and correlation measurements of the four models with previous works on Mohler dataset. Our work demonstrates that ELMo outperformed the other three models. We also, briefly describe the four transfer learning models and conclude with the possible causes of poor results of transfer learning models.