@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://aclanthology.org/2025.bea-1.53/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T A Bayesian Approach to Inferring Prerequisite Structures and Topic Difficulty in Language Learning
%A Vu, Anh-Duc
%A Hou, Jue
%A Katinskaia, Anisia
%A Sheu, Ching-Fan
%A Yangarber, Roman
%Y Kochmar, Ekaterina
%Y Alhafni, Bashar
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-270-1
%F vu-etal-2025-bayesian
%X 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.
%R 10.18653/v1/2025.bea-1.53
%U https://aclanthology.org/2025.bea-1.53/
%U https://doi.org/10.18653/v1/2025.bea-1.53
%P 737-751
Markdown (Informal)
[A Bayesian Approach to Inferring Prerequisite Structures and Topic Difficulty in Language Learning](https://aclanthology.org/2025.bea-1.53/) (Vu et al., BEA 2025)
ACL