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Enhancement of Ontology e-learning through Machine Learning Techniques

Yaratha Nikhil Vardhan Reddy

FIITJEE School

65-73

Vol: 14, Issue: 2, 2024

Receiving Date: 2024-03-25 Acceptance Date:

2024-06-19

Publication Date:

2024-06-23

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http://doi.org/10.37648/ijrst.v14i02.007

Abstract

E-learning is a method of providing students with access to information via computers, tablets, and smartphones. Essentially, e-learning takes place online, giving students access to their course materials whenever and whenever they choose. The objectives of the machine learning approach presented in this research are to assist knowledge improvement by providing personalised and dynamic learning experiences. It matters while considering knowledge that is easily accessible. The study of ontology focusses on the nature of authenticity and reality, as well as what is real or truth. The ontology technique is used to illustrate one's knowledge in particular fields. E-learning ontology can be used to describe vibrant knowledge and to represent domains in the educational domains. This study consolidates ontological advancement and offers a balanced approach to addressing an educational model. Determining the ontology's domain and scope, taking into account reprocessing the current ontology, and listing important concepts are the subsequent procedures that have been modified for ontology progression. The lack of control over data design in the educational domain through e-Learning performances is one of the primary problems. Building modelling facilitates communication between the e-learning system's behaviour and structure. When paired with domain knowledge, e-learning ontology can construct a semantic model of the data. The suggested disclosure technique for enhancing e-learning ontologies using machine learning has been genuinely tested in an e-learning setting to demonstrate database management systems. The student will choose his subjects under this proposed system, and he will then take the test for that subject. After seeing his marks, the system will ask the student if he needs any videos or PDFs for his feedback form. Based on his performance prediction, the system will then display the relevant videos or PDFs. This paper's major goal is to cluster students using the KNN, SVM, and K-Means algorithms. It also makes use of the ontology concept of understanding and learning style recognition.

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