Authors : Sathya.C; Dhatchana.P

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/ybvwpf44

Scribd : https://tinyurl.com/2y2entex

DOI : https://doi.org/10.5281/zenodo.14613833

Abstract : Understanding and classifying student actions within educational environments is a vital component of boosting learning results and well-being. This study presents a novel method to student activity categorisation by employing facial expression detection technologies. The technology is intended to record and evaluate pupils' facial expressions, understand their emotional states, and then classify their actions. This study investigates the application of deep learning models for face emotion identification using a dataset that includes both academic and non-academic activities. The system can recognise emotions such as happiness, sorrow, rage, and surprise. The extracted emotion traits are then used to characterise student actions, revealing whether a student is engaged, attentive, puzzled, or indifferent, among other states. This strategy has the potential to improve educational settings by offering real-time insights into student conduct and allowing for timely adjustments to improve learning experiences and outcomes. It also offers up possibilities for personalised educational support and the creation of intelligent learning systems. In this research, we will construct a system to extract face characteristics using the Grassmann method. And identify the emotions of students at certain times. Predict the active state using emotion categorisation and provide reports to the administrator. Furthermore, this technique shows potential for the creation of adaptive learning systems that react to students' emotional states, delivering extra help or challenges as needed. For example, a virtual tutor may modify the difficulty of exercises based on a student's emotional reactions, producing a dynamic and responsive learning experience.

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