Paper Type

Complete

Abstract

Machine learning and deep learning are two core research domains in artificial intelligence. In recent years, the roles of artificial intelligence in education—the fundamental concepts of artificial intelligence, machine learning, and deep learning- have been extensively investigated. However, relatively scant attention has been paid to the roles of artificial intelligence(AI), especially machine learning (ML) and deep learning (DL), in supporting the e-learning process and outcomes. The primary objective of this study is to investigate the roles of two artificial intelligence subfields, ML and DL, in managing each of the six components of distance learning systems through the lens of the system's view of the e-learning success model. AI and computer and information technologies have enabled an intelligent learning environment facilitating personalized adaptive learning development. Further, managing dropouts in online education has been an important issue. DL can predict potential obstacles and areas where students will likely struggle by analyzing historical learning management systems (LMS) data and learning patterns. With this insight, educators and platforms can provide targeted support and resources to help students overcome challenges before they lead to dropout.

Paper Number

1077

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Aug 16th, 12:00 AM

Roles of Machine Learning and Deep Learning for Supporting E-Learning Processes and Learning Outcomes through The Lens Of System's View of E-Learning Success Model

Machine learning and deep learning are two core research domains in artificial intelligence. In recent years, the roles of artificial intelligence in education—the fundamental concepts of artificial intelligence, machine learning, and deep learning- have been extensively investigated. However, relatively scant attention has been paid to the roles of artificial intelligence(AI), especially machine learning (ML) and deep learning (DL), in supporting the e-learning process and outcomes. The primary objective of this study is to investigate the roles of two artificial intelligence subfields, ML and DL, in managing each of the six components of distance learning systems through the lens of the system's view of the e-learning success model. AI and computer and information technologies have enabled an intelligent learning environment facilitating personalized adaptive learning development. Further, managing dropouts in online education has been an important issue. DL can predict potential obstacles and areas where students will likely struggle by analyzing historical learning management systems (LMS) data and learning patterns. With this insight, educators and platforms can provide targeted support and resources to help students overcome challenges before they lead to dropout.

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