@inproceedings{vu-etal-2025-bayesian,
    title = "A {B}ayesian Approach to Inferring Prerequisite Structures and Topic Difficulty in Language Learning",
    author = "Vu, Anh-Duc  and
      Hou, Jue  and
      Katinskaia, Anisia  and
      Sheu, Ching-Fan  and
      Yangarber, Roman",
    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/acl25-workshop-ingestion/2025.bea-1.53/",
    pages = "737--751",
    ISBN = "979-8-89176-270-1",
    abstract = "Understanding how linguistic topics are related to each another is essential for designing effective and adaptive second-language (L2) instruction. We present a data-driven framework to model topic dependencies and their difficulty within a L2 learning curriculum. First, we estimate topic difficulty and student ability using a three-parameter Item Response Theory (IRT) model. Second, we construct topic-level knowledge graphs{---}as directed acyclic graphs (DAGs){---}to capture the prerequisite relations among the topics, comparing a threshold-based method with the statistical Grow-Shrink Markov Blanket algorithm. Third, we evaluate the alignment between IRT-inferred topic difficulty and the structure of the graphs using edge-level and global ordering metrics. Finally, we compare the IRT-based estimates of learner ability with assessments of the learners provided by teachers to validate the model{'}s effectiveness in capturing learner proficiency. Our results show a promising agreement between the inferred graphs, IRT estimates, and human teachers' assessments, highlighting the framework{'}s potential to support personalized learning and adaptive curriculum design in intelligent tutoring systems."
}