@inproceedings{boonsarngsuk-etal-2025-evaluating,
    title = "Evaluating Sampling Strategies for Similarity-Based Short Answer Scoring: a Case Study in {T}hailand",
    author = "Boonsarngsuk, Pachara  and
      Arpanantikul, Pacharapon  and
      Hiranwipas, Supakorn  and
      Watcharakajorn, Wipu  and
      Chuangsuwanich, Ekapol",
    editor = "Wijaya, Derry  and
      Aji, Alham Fikri  and
      Vania, Clara  and
      Winata, Genta Indra  and
      Purwarianti, Ayu",
    booktitle = "Proceedings of the Second Workshop in South East Asian Language Processing",
    month = jan,
    year = "2025",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.sealp-1.3/",
    pages = "27--41",
    abstract = "Automatic short answer scoring is a task whose aim is to help grade written works by learners of some subject matter. In niche subject domains with small examples, existing methods primarily utilized similarity-based scoring, relying on predefined reference answers to grade each student{'}s answer based on the similarity to the reference. However, these reference answers are often generated from a randomly selected set of graded student answer, which may fail to represent the full range of scoring variations. We propose a semi-automatic scoring framework that enhances the selective sampling strategy for defining the reference answers through a K-center-based and a K-means-based sampling method. Our results demonstrate that our framework outperforms previous similarity-based scoring methods on a dataset with Thai and English. Moreover, it achieves competitive performance compared to human reference performance and LLMs."
}