@inproceedings{kolagar-etal-2025-investigating,
    title = "Investigating Methods for Mapping Learning Objectives to Bloom{'}s Revised Taxonomy in Course Descriptions for Higher Education",
    author = "Kolagar, Zahra  and
      Zalkow, Frank  and
      Zarcone, Alessandra",
    editor = {Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Ana{\"i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng},
    booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.bea-1.32/",
    pages = "415--445",
    ISBN = "979-8-89176-270-1",
    abstract = "Aligning Learning Objectives (LOs) in course descriptions with educational frameworks such as Bloom{'}s revised taxonomy is an important step in maintaining educational quality, yet it remains a challenging and often manual task. With the growing availability of large language models (LLMs), a natural question arises: can these models meaningfully automate LO classification, or are non-LLM methods still sufficient? In this work, we systematically compare LLM- and non-LLM-based methods for mapping LOs to Bloom{'}s taxonomy levels, using expert annotations as the gold standard. LLM-based methods consistently outperform non-LLM methods and offer more balanced distributions across taxonomy levels. Moreover, contrary to common concerns, we do not observe significant biases (e.g. verbosity or positional) or notable sensitivity to prompt structure in LLM outputs. Our results suggest that a more consistent and precise formulation of LOs, along with improved methods, could support both automated and expert-driven efforts to better align LOs with taxonomy levels."
}