An Integrated Fuzzy Logic and K-Means Clustering Adaptive Learning System
E-learning is gaining fame in assisting both instructors and students while reducing spatial or temporal boundaries. Despite these clear benefits, such platforms need to be measured apropos the degree of students’ commitment, the delivery of course materials, and teaching approach. This information is of paramount importance for evaluating teaching quality and adjusting the teaching delivery style. To tackle these challenges, an automated adaptive learning system is proposed according to the selection concepts of a customized learning path using integrated Fuzzy Logic based classifier and K-means Clustering algorithms. In this paper, the architecture for an integrated Fuzzy Logic and K-Means clustering adaptive learning system which allows learning paths to be adapted to the learners according to the evaluation criteria teacher is presented.
Authors: Nafea Alanazi, Finlay Smith, Attracta Brennan
- Date of Conference: 26-28 April 2022
- DOI: 10.20533/IICE.2022.0015
- ISBN: 978-1-913572-46-4
- Conference Location: Virtual (Dún Laoghaire, Ireland)