Analysis in Web 3D Environments of Thematic Research Networks on Immersive Learning through Variation of Clustering Criteria

Abstract

This project aims to develop a Web-based 3D visualization tool that provides a global understanding of the field of Immersive Learning. Thematic networks are a well-established approach for addressing such challenges. Therefore, a systematic literature review was conducted to extract clustering methods and criteria in thematic networks. The methodology employed in this study is Design Science research, which involved iterative development and evaluation of the visualization tool. Expert interviews were conducted to identify requirements, and rigorous methods, including recording, analyzing, and transcribing interviews, were applied to ascertain the research's relevance. The tool utilizes a node-link visualization approach to represent immersive learning strategies, practices, and associated papers. Additionally, it offers a range of filtering functionali- ties, allowing users to filter by strategies, practices, authors, institutions, and more. Furthermore, the tool incorporates various clustering functionalities, such as community detection using the Louvain Algorithm, with variable clustering criteria such as theme and paper association, paper citation, co-citation, and others. Users can also control the network's structure by modifying clus- ter size, number, and community colors. The tool features thematic networks exploratory methods for navigating the environment. By combining thematic networks with clustering and filtering capabilities, this tool aims to provide a global understanding of the scientific field. Its unique integration of Web and 3D technologies, along with exploratory methods, distinguishes it from existing visualization tools. The tool's powerful clustering algorithms, offering diverse criteria for understanding concept relationships, have the potential to make a significant impact in the immersive learning community. It is designed to serve as an innovative artifact that enhances the analytical capabilities of researchers, educators, and students in the field.