Optimizing Educational Interaction: An Advanced Real-Time Attention Recognition in Online Education Environment
Optimizing Educational Interaction: An Advanced Real-Time Attention Recognition in Online Education Environment |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-8 |
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| Year of Publication : 2024 | ||
| Author : Namita Shinde, Mayur Dilip Jakhete, Shekhar Shinde, Archana Vyas, Rajesh Ramdas Karhe, Neeru Malik, Pooja Deshmukh, Kedarnath Chaudhary |
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| DOI : 10.14445/22315381/IJETT-V72I8P135 | ||
How to Cite?
Namita Shinde, Mayur Dilip Jakhete, Shekhar Shinde, Archana Vyas, Rajesh Ramdas Karhe, Neeru Malik, Pooja Deshmukh, Kedarnath Chaudhary,"Optimizing Educational Interaction: An Advanced Real-Time Attention Recognition in Online Education Environment," International Journal of Engineering Trends and Technology, vol. 72, no. 8, pp. 378-388, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I8P135
Abstract
It is important to recognize a student's emotional condition in both traditional and virtual learning settings. In order to solve this difficulty, this research suggests a novel method that uses patterns of eye and head movements to infer emotions and measure learner engagement. We emphasize the need for enhanced system efficacy, value, and user interaction by using sophisticated emotion recognition algorithms in our approach. Our goal is to assess how deeply a student is involved in educational activities. In online learning environments, the suggested system not only detects and tracks students' attention in real time but also sets up a feedback mechanism for improved material delivery. We measure the degree of focus exhibited by a student by closely examining their head and eye movements. The system uses graphs to classify and display this data, providing insightful information about student interest and involvement. Subsequently, this information is relayed to educators as feedback, enabling them to optimize the learning environment for a more tailored and effective educational experience.
Keywords
Image recognition, Face recognition, Education Tools, Convolutional neural networks, Computational intelligence.
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