Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study

TL;DR

This study compares fuzzy clustering models, specifically fuzzy c-means and kernelized fuzzy c-means, to evaluate and predict e-learners' behavior, achieving a 78% accuracy in matching real-world profiles and demonstrating KFCM's superiority.

Contribution

It introduces a methodology using fuzzy clustering techniques to categorize e-learners' behavior and compares their effectiveness in predicting learner profiles.

Findings

KFCM outperforms FCM in predicting learner behavior.

Fuzzy clustering achieves 78% accuracy in matching real profiles.

Fuzzy clustering better reflects learner behavior than crisp methods.

Abstract

This paper introduces an evaluation methodologies for the e-learners' behaviour that will be a feedback to the decision makers in e-learning system. Learner's profile plays a crucial role in the evaluation process to improve the e-learning process performance. The work focuses on the clustering of the e-learners based on their behaviour into specific categories that represent the learner's profiles. The learners' classes named as regular, workers, casual, bad, and absent. The work may answer the question of how to return bad students to be regular ones. The work presented the use of different fuzzy clustering techniques as fuzzy c-means and kernelized fuzzy c-means to find the learners' categories and predict their profiles. The paper presents the main phases as data description, preparation, features selection, and the experiments design using different fuzzy clustering models. Analysis of the obtained results and comparison with the real world behavior of those learners proved that there is a match with percentage of 78%. Fuzzy clustering reflects the learners' behavior more than crisp clustering. Comparison between FCM and KFCM proved that the KFCM is much better than FCM in predicting the learners' behaviour.