Implementation of an adaptive E-learning platform with facial emotion recognition

Bala, Myneni Madhu; Akkineni, Haritha; Sirivella, Siva Abhishek; Ambati, Siddharth; Sai, Krishna Vamshi Potharaju Venkata

doi:10.1007/s00542-023-05420-1

Cited by 2 publications

(1 citation statement)

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“…Furthermore, a recent study by [15] introduced an innovative approach that leverages biometric characteristics such as pupil detection, head movement recognition [16], facial emotions recognition [17], and eye gaze movements to assess the level of student engagement in academic pursuits [18]. This strategy makes use of a variety of approaches, including machine learning, OpenCV, Haar cascade, local binary patterns, and Principal Component Analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

Student Recognition and Activity Monitoring in E-Classes Using Deep Learning in Higher Education

Alruwais,

Zakariah

2024

IEEE Access

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Monitoring student activity manually constantly is a laborious endeavor. Over the past few years, there has been a rapid expansion in the usage of cameras and the automatic identification of odd surveillance behavior. Different computer vision algorithms have been used to observe and monitor realworld activities. Most educational institutions are already offering online programs to lessen the impact of this epidemic on the education industry. However, ensuring that students are correctly identified, and their behaviors are monitored is crucial to make these online learning sessions dynamic and equivalent to the conventional offline classroom. In this study, we have introduced brand-new deep learning-based algorithms that continuously track a student's mood, including rage, contempt, happiness, sorrow, fear, and surprise. The effectiveness of student identification and activity monitoring in online classrooms was also studied using deep learning and a CNN model that reaches 99% accuracy. Our approach was superior because of its many convolutional layers, dropout regularization, and batch normalization. It caught crucial properties and decreased overfitting. By identifying them more frequently, deep learning techniques can enhance student engagement and learning outcomes in e-learning situations, according to the research. With these techniques, educators and instructors may support students more effectively by better comprehending their behavior and offering specialized and individualized support, improving academic performance and student activity evaluation.

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Section: Introductionmentioning

confidence: 99%