@inproceedings{moore-etal-2026-multi,
    title = "Multi-component student writing profiles for expert-aligned automated evaluation of {E}nglish learner essays.",
    author = "Moore, Russell  and
      Caines, Andrew  and
      Buttery, Paula",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.39/",
    pages = "562--573",
    ISBN = "979-8-89176-409-5",
    abstract = "Automated Writing Evaluation (AWE) platforms have become common, but a significant gap remains between automated assessment and expert human feedback. We address this gap by introducing a supervised learning method that uses a multi-component student writing profile (comprising estimated CEFR levels, grammatical error rates, and vocabulary distribution) to align AI scoring with expert human judgements. In the context of an online essay-writing platform for second language learners of English, our model achieves a 36{\%} reduction in RMSE for holistic essay scoring and an 84{\%} improvement in similarity to human-expert annotation of grammatical errors compared to automarker scores (26{\%} and 57{\%} improvement from the best-performing comparable earlier work, by Zaidi et al. (2019) . Furthermore, we demonstrate that the model can predict a student{'}s final submission profile (CEFR level and grammatical error rate) from earlier drafts and that predictions generalise to a subsequent task, offering new possibilities for automated curriculum planning. Finally, we introduce a visualisation tool that provides educators with clear expert-aligned longitudinal views of student development."
}