Learning Styles Prediction Using Social Network Analysis and Data Mining Algorithms

Abstract

One of the most interesting directions of digital educational technology is adaptive e-learning systems. Learning styles are used in adaptive e-learning systems to provide helpful suggestions and regulations for learners to improve their learning performance and optimize the educational process. Recently, the research trend is to detect learning styles without disturbing the users. In contrast to the old method, which included students filling out a questionnaire, many ways to automatically detecting learning styles have been presented. These methods are based on analyzing behavior data collected from students’ interactions with the system using various data mining tools. Simultaneously, recent research has embraced the use of social network analysis in improving online teaching and learning, with the goal of analyzing user profiles, as well as their interactions and behaviors, to better understand the learner and his needs in order to provide him with appropriate learning content. The aim of our research was to determine if the automatic detection of learning styles can be done using the learner social network analysis and data mining algorithms, our research was implemented with Sakai learning management systems, which allow us to examine the performance of our approach.