Mapping research themes and future directions in learning style detection research: A bibliometric and content analysis

• Main Authors: Adi Wijaya, Adi Wijaya, Noor Akhmad Setiawan, Noor Akhmad Setiawan, Shapiai, Mohd. Ibrahim
• Format: Article
• Language: English
• Published: Academic Conferences and Publishing International Limited 2023
• Subjects: T Technology (General)
• Online Access: http://eprints.utm.my/108304/1/MohdIbrahimShapiai2023_MappingResearchThemesandFutureDirections.pdf; http://eprints.utm.my/108304/; http://dx.doi.org/10.34190/ejel.21.4.3097
• Institution: Universiti Teknologi Malaysia

This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric and content analysis will be conducted to map out the existing research and identify emerging topics and directions for future research. The study analyzes 1074 bibliographic sources from Scopus and visualizes the results of the bibliometric analysis through co-occurrence and thematic map analysis using VOSviewer and BibliometriX software. Content analysis is then conducted based on the results of the co-occurrence analysis. The findings reveal a significant increase in publications and citations in the field, with popular research topics including classification, adaptive learning, and MOOCs, and the most frequently used learning style models being Felder-Silverman, VARK, and Kolb. Emerging research topics include the use of EEG signals, online learning, and feature extraction. Future research may focus on classification, intelligent tutoring systems, MOOCs, online learning, adaptive learning, and deep learning. This study provides valuable insights into the current and future research trends in learning style detection, which can support the development of adaptive e-learning systems, intelligent tutoring systems, and MOOCs. By identifying popular research topics and emerging areas of study, this research can guide the design and implementation of effective online learning environments. Additionally, the study advances the field of e-learning knowledge by providing a comprehensive overview of the most frequently used learning style models and potential research areas. It sheds light on the ongoing development of learning style detection research and the potential for future advancements in the field, ultimately contributing to the growth and improvement of e-learning practices.