Evaluation of e-learning systems based on fuzzy clustering models and statistical tools

This paper introduces a hybridization approach of AI techniques and statistical tools to evaluate and adapt the e-learning systems including e-learners. Learner’s profile plays a crucial role in the evaluation process and the recommendations to improve the e-learning process. This work classifies the learners into specific categories based on the learner’s profiles; the learners’ classes named as regular, workers, casual, bad, and absent. The work extracted the statistical usage patterns that give a clear map describing the data and helping in constructing the e-learning system. The work tries to find the answers of the question how to return the bad students who are away back to be regular ones and find a method to evaluate the e-learners as well as to adapt the content and structure of the e-learning system. The work introduces the application of different fuzzy clustering techniques (FCM and KFCM) to find the learners profiles. Different phases of the work are presented. Analysis of the results and comparison: There is a match with a 78% with the real world behavior and the fuzzy clustering reflects the learners’ behavior perfectly. Comparison between FCM and KFCM proved that the KFCM is much better than FCM.