Automatic concept extraction for learning domain modeling: A weakly supervised approach using contextualized word embeddings

Heterogeneity in student populations poses a challenge in formal education, with adaptive textbooks offering a potential solution by tailoring content based on individual learner models.However, creating domain models for textbooks typically demands significant manual effort.Recent work by Chau et al. ( 2021) demonstrated automated concept extraction from digital textbooks, but relied on costly domainspecific manual annotations.This paper introduces a novel, scalable method that minimizes manual effort by combining contextualized word embeddings with weakly supervised machine learning.Our approach clusters word embeddings from textbooks and identifies domain-specific concepts using a machine learner trained on concept seeds automatically extracted from Wikipedia.We evaluate this method using 28 economics textbooks, comparing its performance against a tf-idf baseline, a supervised machine learning baseline, the RAKE keyword extraction method, and human domain experts.Results demonstrate that our weakly supervised method effectively balances accuracy with reduced annotation effort, offering a practical solution for automated concept extraction in adaptive learning environments.