A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

G. M. Dhananjaya, R. H. Goudar, Anjanabhargavi Kulkarni, Vijayalaxmi N. Rathod, Geetabai S. Hukkeri

Research output: Contribution to journalArticlepeer-review

Abstract

This review delves into using e-learning technology and personalized recommendation systems in education. It examines 60 articles from prominent databases and identifies the different methods used in recommendation systems, such as collaborative and content-based approaches with a recent shift towards machine learning. However, the current personalized recommendation system faces challenges such as a lack of understanding of the content, student discontinuity, language barriers, confusion in selecting study materials, and inadequate infrastructure and funding. The review proposes using new digital technologies to address these issues, including Fluxy AI, Twin technology, AI-powered virtual proctoring, and Alter Ego. These technologies can create a dynamic and interactive learning environment, providing tailored learning experiences for students and insights for educators to provide targeted support and guidance. The integration of these technologies can improve individualized learning, increase understanding capacity and enhance the learning experience for students with speech disorders.

Original languageEnglish
Pages (from-to)34019-34041
Number of pages23
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering

Fingerprint

Dive into the research topics of 'A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review'. Together they form a unique fingerprint.

Cite this