Design and Development of a Cutting-Edge Machine Learning-Driven Virtual Learning Platform to Revolutionize Online Education and Im-prove Student Learning during COVID-19

Mavis Malachi Ejiofor, Taiwo Abdulahi Akintayo, Agbonze Nosa Godwin

Abstract

Instructors in virtual classes are facing previously unheard-of difficulties in sustaining student engagement and attendance as the COVID-19 pandemic continues to alter the education landscape. To solve this pressing problem, we have created facial analysis technology that enables teachers to track students' engagement and attention in real-time.

Our user-friendly platform uses cutting-edge face detection technology and machine learning to give teachers a visual dashboard that shows disengaged students as red boxes and engaged students as green boxes. This cutting-edge tool helps teachers determine which students need more encouragement or support, guaranteeing individualized attention and better learning results.

Our tool provides instructors with features, such as automated attendance records and early departure detection, that go beyond simple attendance tracking and help them optimize online class management. Our solution seeks to humanize online learning by utilizing facial analysis to provide students with a more engaging and productive learning environment.

10.22178/pos.106-33


Keywords


Facial analysis; Python; Machine learning; student engagement; instructor support; virtual classroom; COVID-19

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Copyright (c) 2024 Mavis Malachi Ejiofor, Taiwo Abdulahi Akintayo, Agbonze Nosa Godwin

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