Few-shot essay grading: weighted prototypical networks for ordinal text classification.

Wijayasekara, Vihanga Ashinsana; Martin, Kyle; Wiratunga, Nirmalie; Massie, Stewart; Wijekoon, Anjana

Abstract

Automated Essay Scoring (AES) presents a key opportunity to improve student experience while reducing the administrative burden of academic staff. However existing methods for AES are reliant on large volumes of data and fail to consider the ordinal aspect of grading. As a result, when an institution introduces a new assessment, there may be no data available to train algorithms. In this paper, we demonstrate that metric learning architectures, specifically Prototypical Networks, offer robust performance on few-shot ordinal classification essay grading tasks. We introduce three novel weighted prototype calculation strategies designed to enhance class representation in ordinal few-shot text classification. These strategies improve how class knowledge is modeled from limited examples by refining the way prototypes are computed, incorporating weighted mechanisms for better differentiation. Results across four datasets show that our methods outperform existing baselines and the current state-of-the-art in ordinal few-shot text classification. Additionally, we compare our approach with three large language models (LLMs) using a prompt-based approach to few-shot learning and find that we achieve superior or comparable performance in all evaluated tasks.

Citation

WIJAYASEKARA, V.A., MARTIN, K., WIRATUNGA, N., MASSIE, S. and WIJEKOON, A. [2025]. Few-shot essay grading: weighted prototypical networks for ordinal text classification. To be presented at the 25th European conference on artificial intelligence 2025 (ECAI-2025), 25-30 October 2025, Bologna, Italy.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: European conference on artificial intelligence 2025 (ECAI-2025)
• Start Date: Oct 25, 2025
• End Date: Oct 30, 2025
• Acceptance Date: Jul 11, 2025
• Deposit Date: Jul 17, 2025
• Peer Reviewed: Peer Reviewed
• Keywords: Automated essay scoring (AES); Student experience; Academic staff; Large language models (LLMs)
• Public URL: https://rgu-repository.worktribe.com/output/2929027