The Effectiveness of Using Machine Learning Techniques to Improve Online Education: Advantages, Challenges, and Barrier

Document Type

Conference Proceeding

Publication Date

2024

Abstract

In this paper, we are going to analyze the effectiveness of using machine learning (ML) techniques to improve online education. Nowadays, numerous universities and online education providers have expanded their offerings. For the uninitiated, we can simply refer to online education as distance learning, facilitated through the internet and a computer. Online education, as a component of distance learning, is gaining popularity due to its offerings. In recent years, online education has become an important part of contemporary education. It's important because it allows students to learn or get access to particular study materials without attending a conventional class at a university or college. It provides more flexibility for students to plan their study time instead of allocating specific hours on behalf of attending class in conventional education. The more busy people are the greater the demand for these offerings. High school students, college students, and working adults are all customers of online education. High school students can take online lessons for a variety of reasons: to catch up on needed credits, to accelerate graduation, or to take a college-level course not available at their high school. College students have even more options with online courses, and working adults may be enrolling in an online course for either career enhancement or career change reasons. With the vast target potential of students, online education offers an insurmountable amount of research prospects that could help better understand students and learning in the 21st century. Some of our recent work in cognitive science has been focused on understanding the temporal dynamics of online education using A/B testing and causal inference techniques. This new effort complements this work and uses ML techniques to improve the student's learning experience and efficacy.

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