A High-Precision Convolutional Neural Network Framework for Emotion Detection and Instructional Decision Support in E-Learning

Abstract

The rapid transition to virtual education has created a significant gap in real - time non - verbal feedback between students and educators. This paper presents an AI - driven facial emotion detection framework specifically plan to bridge this communicatory water parting in online learning environs. use a Convolutional Neural Network (CNN) architecture, the system captures and analyzes student affective states — categorized into Neutral, Sad, and felicitous — in real - sentence. data- based results evidence a full-bodied model performance with an overall accuracy of 83% and a precision score of 1.00, ensuring gamey reliability by eliminating false - positive emotion triggers. The framework integrate a unequaled Faculty Emotion Analysis Dashboard that translates raw emotional data into pedagogi- cal penetration, such as understanding levels and automated instructional recommendation (for example, ”Continue Session” or ”Re - explicate Concept”). Nonetheless, By allow for stable engagement monitoring and actionable feedback, this system empowers educators to make informed, data - driven decisions to enhance student retention and learning outcomes in digital classrooms.