An optimized deep convolutional neural network for adaptive learning using feature fusion in multimodal data
Swadha Gupta, Parteek Kumar, Rajkumar Tekchandani
The outbreak of COVID-19 has caused an unprecedented increase in the usage of e-Learning platforms. The closure of educational institutions globally has significantly impacted the traditional education system. Without physical interaction, engaging students in an e-Learning environment has become a major challenge for teachers and e-Learning platform providers. Unlike a face-to-face classroom, where teachers can easily monitor students' behaviour and adapt the learning content according to their needs, this is impossible in an e-Learning environment. This study introduces an optimized deep learning-based approach for detecting student engagement levels to ensure students remain connected to the learning process. The proposed optimized model uses facial emotion recognition and head movement detection to track real-time engagement levels for big data as real-time-based face and head datasets are analysed. The system leverages facial landmark detection to monitor head movements and deep learning models such as VGG19 and ResNet50 for facial emotion recognition. The system combines the output of both approaches to accurately predict the student engagement state as either 'engaged' or 'disengaged' with an accuracy rate of 91.67%.
Year of publication: |
2023
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Authors: | Gupta, Swadha ; Bhatia, Parteek ; Tekchandani, Rajkumar |
Published in: |
Decision analytics journal. - Amsterdam : Elsevier, ISSN 2772-6622, ZDB-ID 3106160-6. - Vol. 8.2023, Art.-No. 100277, p. 1-15
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Subject: | Deep learning | Optimization | Convolutional neural network | e-Learning | Facial emotions | Head-movement | Neuronale Netze | Neural networks | Lernprozess | Learning process | Emotion | E-Learning | E-learning | Theorie | Theory |
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