Automatic concept extraction for learning domain modeling: A weakly supervised approach using contextualized word embeddings
Year: 2025
Type: conference-paper
Abstract: 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.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412889194