Learning Activities Diagnostic Model Based on Educational Data Mining of Online Social Media Behavior

Abstract

Due to the advancement of technology, the learning system has been rapidly changed from traditional learning through the materials of text and image only in the classroom-based learning to online-based learning using E-learning platform or MOOC (Massive Open Online Course). However, the existing online learning still have the limitation. For example, all learners learn from the same learning materials. Especially, the current online learning does not meet the needs and learning styles of learners in the 21st century that why the learners lack of motivation to study and fail to learn. Moreover, the social network such as Facebook, Twitter, YouTube have an influence to all people’s everyday life. There are a big data of social media, learning media, human behaviors and interactions are generated rapidly in everyday. In the educational data analysis, the educators try to differentiate the learners based on their basic knowledge, needs and behaviors from social network data for offering them the appropriate learning activities. Therefore, the objective of this research is to propose the learning activities diagnostics model based on the educational data mining (EDM) that apply several data classification algorithms to analyze the learning behaviors. The learners’ behaviors, reactions and interactions among them are collected from the simulated social network (Facebook) and the online learning ecosystem of the course of Introduction to Information Technology and Data Science, Mae Fah Luang University. This proposed learning activities diagnostic (LAD) model would lead us to indeed understand the nature of the learners and be able to provide the proper learning activities for both of the learners and instructors.