Machine Learning Methods for Online Education Case

Title

Machine Learning Methods for Online Education Case

Creator

Rajagopal M.; Ali B.; Sharon Priya S.; Aisha Banu W.; Madhavi M.G.; Punamkumar

Description

Online education has become a popular choice for learners of all ages and backgrounds due to its accessibility and flexibility. However, providing personalized learning experiences for a diverse range of students in online education can be challenging. Machine learning methods can be used to provide personalized learning experiences and improve student engagement in online education. In this case study, We're going to do some research on machine learning. methods in an online education platform. The platform provides courses in various subjects and is designed to be accessible to students from all over the world. The platform collects data on student behavior, such as the courses they enroll in, the time they spend on each course, and their performance on assignments and quizzes. We will explore several machine learning methods that can be applied to this data, including clustering, classification, and recommendation systems. Clustering algorithms can be used to group students based on their learning behavior and preferences, allowing instructors to provide personalized feedback and course recommendations. Classification algorithms can be used to predict student success in a particular course, allowing instructors to intervene and provide additional support if needed. Recommendation systems can be used to suggest courses to students based on their interests and past behavior. We will also discuss the potential benefits and challenges of using machine learning methods in online education. Benefits include increased student engagement, improved learning outcomes, and more efficient use of resources. Challenges include ensuring data privacy and security, preventing algorithmic bias, and maintaining transparency and fairness in the decision-making process. Overall, machine learning methods have the potential to transform online education by providing personalized learning experiences and improving student outcomes. By leveraging the vast amounts of data generated by online education platforms, we can create more effective and efficient learning experiences that meet the needs of students from diverse backgrounds and learning styles. 2023 IEEE.

Source

Proceedings of 8th IEEE International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Education; etc.; Machine Learning; Online class

Coverage

Rajagopal M., CHRIST(Deemed to Be University), Lean Operations and Systems, School of Business and Management, Bangalore, 560029, India; Ali B., Maulana Azad National Urdu University, College of Teacher Education, Hyderabad, 500032, India; Sharon Priya S., B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600100, India; Aisha Banu W., B.S.Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600100, India; Madhavi M.G., SreeVidyanikethan Engineering College, Department of Basic Sciences and Humanities, Tirupati, 517 502, India; Punamkumar, Christuniversity, Bangalore, 560029, India

Rights

Restricted Access

Relation

ISBN: 979-835034779-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICONSTEM56934.2023.10142626

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163154297&doi=10.1109%2fICONSTEM56934.2023.10142626&partnerID=40&md5=4ae76395c316d9146bf951db032c3b91