@inproceedings{chitez-etal-2025-assessing,
    title = "Assessing Critical Thinking Components in {R}omanian Secondary School Textbooks: A Data Mining Approach to the {ROTEX} Corpus",
    author = "Chitez, Madalina  and
      Dinu, Liviu  and
      Micluta-Campeanu, Marius  and
      Bucur, Ana-Maria  and
      Rogobete, Roxana",
    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-1.56/",
    pages = "780--793",
    ISBN = "979-8-89176-270-1",
    abstract = "This paper presents a data-driven analysis of Romanian secondary school textbooks through the lens of Bloom{'}s Taxonomy, focusing on the promotion of critical thinking in instructional design. Using the ROTEX corpus, we extract and annotate almost 2 million words of Romanian Language and Literature textbooks (grades 5-8) with Bloom-aligned labels for verbs associated with pedagogical tasks. Our annotation pipeline combines automatic verb extraction, human filtering based on syntactic form and task relevance, and manual assignment of Bloom labels supported by in-text concordance checks. The resulting dataset enables fine-grained analysis of task complexity both across and within textbooks and grade levels. Our findings reveal a general lack of structured cognitive progression across most textbook series. We also propose a multi-dimensional framework combining cognitive-level and linguistic evaluation to assess instructional design quality. This work contributes annotated resources and reproducible methods for NLP-based educational content analysis in low-resource languages."
}