Article: Construction and modular design of teacher education course knowledge graph based on association rule mining Journal: International Journal of Computational Systems Engineering (IJCSYSE) 2026 Vol.10 No.9 pp.1 - 15 Abstract: To address knowledge fragmentation and information silos in constructing knowledge graphs (KGs) for teacher education courses, and to overcome limitations of existing approaches - such as shallow text-based relationship mining, expert-dependent modular design, and weak interpretability between competency objectives and curriculum resources - this study proposes an integrated KG construction and modular design method combining association rule mining (ARM) with an enhanced MetaPath2Vec++ model. A ternary relationship matrix linking curricula, knowledge points, and competencies is first established under teacher professional standards. Strong association rules are then mined via an improved FP-Growth algorithm to generate a weighted association matrix that reinforces semantic links. Finally, a multi-objective optimisation framework integrating modularity and information connectivity dynamically adjusts modules based on competency constraints. Experiments demonstrate improved knowledge density, clearer module boundaries, and stronger cross-module semantic connectivity, enabling adaptive support for teachers' professional development. Inderscience Publishers - linking academia, business and industry through research

Title: Construction and modular design of teacher education course knowledge graph based on association rule mining

Authors: Hui He

Addresses: School of Education, Huanggang Normal University, Huanggang City, Hubei Province, 438000, China

Abstract: To address knowledge fragmentation and information silos in constructing knowledge graphs (KGs) for teacher education courses, and to overcome limitations of existing approaches - such as shallow text-based relationship mining, expert-dependent modular design, and weak interpretability between competency objectives and curriculum resources - this study proposes an integrated KG construction and modular design method combining association rule mining (ARM) with an enhanced MetaPath2Vec++ model. A ternary relationship matrix linking curricula, knowledge points, and competencies is first established under teacher professional standards. Strong association rules are then mined via an improved FP-Growth algorithm to generate a weighted association matrix that reinforces semantic links. Finally, a multi-objective optimisation framework integrating modularity and information connectivity dynamically adjusts modules based on competency constraints. Experiments demonstrate improved knowledge density, clearer module boundaries, and stronger cross-module semantic connectivity, enabling adaptive support for teachers' professional development.

Keywords: association rule mining; ARM; knowledge graph; modular design; teacher education curriculum; dynamic structure.

DOI: 10.1504/IJCSYSE.2026.154065

International Journal of Computational Systems Engineering, 2026 Vol.10 No.9, pp.1 - 15

Received: 13 Aug 2025
Accepted: 22 Jan 2026
Published online: 10 Jun 2026 *

Title: Construction and modular design of teacher education course knowledge graph based on association rule mining

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