Adaptive Learning System (ALS) using Fuzzy Logic and K-means

E-learning is increasing in popularity due to its accessibility and reduction in spatial and/or temporal boundaries. Notwithstanding these benefits, e-learning platforms must consider the student's level of commitment, the delivery and assessment of course materials, along with the underlying pedagogical approach. It is of paramount importance to use this information in order to evaluate the quality of teaching and modify the way that the information is presented to the students. To tackle these challenges, an automated adaptive learning system is proposed according to the selection concepts of a customised learning path using integrated the Fuzzy Logic based classifier and the K-means clustering algorithm. This paper presents the architecture for an integrated Fuzzy Logic and K-means clustering adaptive learning system which allows learning paths to be adapted to the learner, according to the evaluation criteria set by the teacher.