A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment

,M.P.L Perera*

doi:10.35940/ijeat.d2297.0410421

Abstract

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.

Highlights

Efficiency and effectiveness of the Adaptive e-Learning System (AES) varies depending on the methodology employed to gather information about the educational needs and traits of the students, as well as how this information has been processed to improve an intelligent and adaptive learning perspective [7].AES emphasizes the importance of individual differences among the students when they attempt to model an ideal e-Learning environment, concentrating on the identification and meeting their personal educational demands
AI technologies, including Fuzzy Logic (FL), Decision Tree, Bavarian Networks, Neural Networks, Hidden Markov Models are all used in Adaptive education systems and are dedicated to AI policy, as well as adaptive learning
A Bayesian Network (BN) can be a Direct Acyclic Graph (DAG) that describes the likelihood of dissemination associate in an extremely specific way that permits the diffusion of qualified probability as a correct illustration [34]

Summary

INTRODUCTION

The essence of secondary education has dramatically changed since 2000. The participation rate has increased significantly, generating substantial diversity both among the scope and the student population of the programs offered. Adaptive learning or adaptive teaching seems to be a method of obtaining students with personalized instructional material based on their unique learning capabilities It is, in essence, the opportunity to provide customized AI services in educational and corporate settings. Biological evolution, standards of living, the environment – all differ from the students’ perspective and are critical to the student's learning period When it comes to education, the traditional notion of a single classroom or one-size-fits-all is rightly losing its luster [1]. Efficiency and effectiveness of the AES varies depending on the methodology employed to gather information about the educational needs and traits of the students, as well as how this information has been processed to improve an intelligent and adaptive learning perspective [7].AES emphasizes the importance of individual differences among the students when they attempt to model an ideal e-Learning environment, concentrating on the identification and meeting their personal educational demands.

REVIEW OF RECENT AI TECHNIQUES FOR ADAPTIVE EDUCATIONAL SYSTEMS

MASSIVE OPEN ONLINE COURSES (MOOCS)

ARTIFICIAL INTELLIGENT STRATEGIES FOR ADAPTIVE E-LEARNING

NEURAL NETWORKS

Findings

CONCLUSION