Decoding cross-cultural textbook networks: structural patterns, formation mechanisms, and predictive insights using a topology–cognition framework
Year: 2026
Type: article
Abstract: Conventional textbook analysis often faces fragmented frameworks, obscuring the relationship between content structure and cognitive development. To address this gap, we propose an integrative computational modeling framework that combines complex network analysis with cognitive taxonomy. Specifically, textbook knowledge networks (TKNs) are constructed by representing knowledge points as nodes, with links defined by their co-occurrence within paragraphs. Cognitive attributes derived from Bloom’s taxonomy are then incorporated, enabling a joint assessment of structural topology and cognitive demand. Analyzing seven Chinese and U.S. physics textbooks (encompassing 18,209 paragraphs) reveals shared topological features and distinct cultural paradigms in textbook design. Chinese textbooks exhibit dense, low-modularity networks that prioritize systematic coherence, whereas U.S. textbooks adopt modular, long-path structures that promote cross-theme reasoning. Both systems exhibit heterogeneous core–periphery architecture ( \(r<0\) ) that scaffolds progressive knowledge integration. Exponential random graph models (ERGM) and mixed-effects modeling uncover four universal principles of high-quality TKNs: self-organization, thematic homophily, centrality-preferential attachment, and cross-cultural stability in topology–cognition coupling. Crucially, network centrality robustly predicts cognitive strength ( \(\beta =0.408\) –0.765, \(p < 0.001\) ), demonstrating topology is closely associated with cognitive development. Furthermore, Chinese textbooks display a bidirectional reinforcement between structure and cognition, while U.S. textbooks demonstrate an implicit alignment with cognitive progression through a low-intervention approach. These findings not only elucidate the fundamental relationship between textbook design and cognitive development, but also introduce a topology–cognition coupling analysis that provides an empirical foundation for cross-cultural textbook optimization and concept-centered instruction, contributing to global STEM education reform.
Source: EPJ Data Science
Authors: Min Xia, Xiu-Mei Feng, Wei-Bing Deng, Ben-Wei Zhang
Institution: Central China Normal University
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $1,890
OpenAlex ID: W7161633244