Chiragh Goel
Apeejay School, Pitampura
Download PDF
http://doi.org/10.37648/ijrst.v14i02.008
E-learning is one of the many fields in which machine learning (ML) technology has transformed. Personalised material distribution, automated evaluations, and adaptive learning experiences are all provided by ML technology. Large amounts of educational information are analysed by algorithms in ML-powered e-learning platforms, allowing for personalised learning routes, effective evaluations, and predictive insights into learner behaviour. A detailed review of machine learning technologies and their uses in e-learning from 2001 to 2021 is given in this paper. We look at the advantages and disadvantages of several machine learning algorithms, methods, and resources that improve e-learning platforms. In addition, we examine case studies of effective ML applications in e-learning, talk about the drawbacks and moral dilemmas of these technologies, and suggest possible future paths, such as combining immersive and explainable AI. Through this investigation, we intend to offer a thorough grasp of how machine learning has changed e-learning and its prospects for the future.
Keywords: E-learning; Machine Learning (ML); Personalised Learning; Adaptive Learning; Automated Evaluations; Predictive Insights in Education
Disclaimer: All papers published in IJRST will be indexed on Google Search Engine as per their policy.
