Adaptive Learning System with Educational Data Mining and Learning Analytics

Abstract

As per the 21st century educational trends, there is increase in the size of the cohort of students in the present classrooms. Further, present COVID pandemic situation, switching the modes of learning from offline to online, and inclusion of technology in the existing learning environments, the fields of educational data mining, learning analytics and adaptive learning systems have gained much attention worldwide. These help in optimizing the educational environments in various ways like providing personalized learning support, improving student learning outcomes, instructor performance and curriculum, identifying career learning pathways etc. This research study aims to develop an adaptive learning system with educational data mining and learning analytics, for the blended learning environments. To develop the proposed system, the distinguishment between the knowledge level of the students is supported. Since the study is performed for blended learning environments, the viewpoints, characteristics, behavioural analysis and identification of probable parameters for different category students (high versus low performers) is necessary. This research study helps to build an understanding of low performer and at-risk (failure or dropout) students behaviour by interviewing low performer, at-risk students of fifth semester undergraduate engineering students. It focuses on the underlying reasons behind their low performance, at-risk behaviour for blended learning environments. This study explores their learning behaviour and perceptions, reasons for being low performers and objectives behind studying the courses. The research findings reveal that majority students belonging to the at-risk, low-performer category are found to be well-aware about their present academic state.