Deployment of Machine Learning in Online And E-Learning: Bridging the Human Component for Enhanced Learner Outcomes in Higher Education

Abstract: The advent of Machine Learning (ML) has brought transformative changes to the landscape of online and distance education. This paper explores the pivotal role of ML in advancing sustainable online and e-learning within higher education by enhancing adaptability, personalization, and learner engagement. This review outlines the emergence and forms of ML that comprised supervised, unsupervised, semi-supervised, reinforcement, and deep machine learning. It went further to highlight the pedagogical implications and practical applications of these various machine learning in virtual learning environments. It also examines the evolving role of Learning Management Systems (LMS) and User Experience (UX) in shaping learners’ engagement and retention. In addition,  it presents information on  the potency of  machine learning as relates to the human components by predicting student behavior, emotions and performance which has been  the greatest  concern of scholars in its integration in educational processes thereby  bridging emotional, behavioral, and cognitive gaps in virtual instruction, Thus, this paper advocates for the integration of ML as a sustainable solution to personalize learning, prediction of  student  behavior and performance while fostering  inclusive, data-driven pedagogical practices in higher education.

Machine learning, e-Learning, Online Learning, Human Components, learner Outcomes and Higher Education

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