A fuzzy similarity based approach for intelligent web based e-learning

TL;DR

This paper presents an intelligent web-based e-learning system that employs fuzzy logic, clustering, and ontology alignment to assess student knowledge and improve decision-making, outperforming existing methods.

Contribution

It introduces a novel combination of fuzzy logic, K-means clustering, and a new domain ontology alignment technique for enhanced e-learning decision support.

Findings

The proposed ontology alignment method outperforms existing techniques in precision and recall.

Fuzzy logic and clustering effectively categorize student performance levels.

The system provides more accurate and intelligent e-learning decisions.

Abstract

In this paper, an intelligent system for web based e-Learning is proposed which analyzes students knowledge capacity by applying clustering technique. This system uses fuzzy logic and k-means clustering algorithm to arrange the documents according to the level of their performance. Moreover, a new domain ontology alignment technique is proposed that uses contextual information of the knowledge sources from the e-Learning domain for effective decision making. The proposed ontology alignment method has been empirically tested in an e-Learning environment and the experimental results show that the proposed method performs better than the existing methods in terms of precision and recall. The salient contributions of this paper are the use of Jaccard Similarity, fuzzy approach for ontology alignment and K-Means clustering algorithm for decision making using decision rules for providing intelligent e-Learning.