@inproceedings{dutulescu-etal-2026-edumuse,
    title = "{E}du{MUSE}: A Multimodal Educational Dataset with Automatically Extracted Instructional Context",
    author = "Dutulescu, Andreea  and
      Ruseti, Stefan  and
      Dascalu, Mihai  and
      McNamara, Danielle",
    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-1.25/",
    pages = "358--367",
    ISBN = "979-8-89176-409-5",
    abstract = "Research in AI applied to education increasingly relies on large-scale, high-quality datasets to support the development and evaluation of learning analytics and intelligent educational systems. Open educational resources provide a promising foundation, yet few datasets integrate structured instructional content with assessment materials in a multimodal form. In this study, we introduce a large-scale multimodal educational dataset (EduMUSE - Educational Multimodal Understanding {\&} Solution Dataset) constructed from OpenStax undergraduate textbooks across multiple domains. The dataset integrates hierarchically structured instructional text, figures, exercises, and, when available, official solutions. For exercises with solutions, we introduce an automatic method that associates each exercise with a focused instructional subsection rather than entire textbook chapters, estimating subsection relevance via solution likelihood under candidate contexts using a vision{--}language model. We analyze the impact of contextualization on the behavior of vision{--}language models across different contexts. Results indicate that subsection-level instructional context has a measurable impact on model performance, with variation across model scales and task formulations. The dataset and code are released as open source at \url{https://github.com/upb-nlp/BEA-EduMUSE/} to support reproducible research in multimodal educational modeling and to facilitate generating similar datasets using our approach."
}