A Data-Driven Analysis of Electromagnetism Simulation Platforms: Thematic Coverage, Accessibility, and Structural Patterns

Abstract

Digital simulations have become a central resource in electromagnetics education due to their capacity to support the visualization and exploration of abstract phenomena. Despite their widespread use, systematic analyses examining their thematic coverage, technical characteristics, and accessibility remain limited. This study aims to provide a comprehensive overview of the current landscape of electromagnetism simulation platforms, identifying patterns in content coverage, technological design, and accessibility features. A mixed-methods, data-driven approach was applied to a dataset of 43 simulation platforms. The analysis combined educational data mining, natural language processing (NLP), correlation analysis, and clustering techniques to evaluate variables related to animation type, programming environments, language availability, licensing models, accessibility support, and emerging technological features. NLP-based thematic analysis was used to compare simulation content with standard electromagnetism curricula. The results reveal a strong emphasis on phenomenological and application-oriented topics, with limited representation of core theoretical constructs. Correlation analysis identified associations between language availability, licensing models, and technical complexity, while clustering techniques revealed distinct thematic groupings and patterns of content concentration across platforms. The findings highlight both the pedagogical potential and the structural gaps present in current electromagnetism simulation resources. This study contributes a replicable analytical framework for evaluating digital educational content and offers evidence to support more informed selection and development of simulation tools in physics education.