article · Smart Learning Environments
This research developed a refined convolutional neural network (CNN) model to accurately detect students' emotions, addressing the challenge of understanding emotional states in online and remote learning environments. Traditional educational methods often overlook student emotions, which can lead to disengagement. The study preprocessed the FER2013 facial expression recognition dataset, using normalisation and augmentation. The CNN architecture was designed with multiple layers to classify seven basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. The model was trained and validated on an 80-20 dataset split, incorporating techniques like learning rate reduction and early stopping. It achieved a 95% test accuracy, demonstrating high precision and recall across all emotion categories.
Recognising students' emotions in real-time can revolutionise education by allowing teaching methods to adapt to individual needs. This is particularly important for online learning, where direct interaction is limited, helping to improve engagement, motivation, and overall learning outcomes for students.
This research presents an applied technology for real-time emotion detection in educational settings, particularly beneficial for online and remote learning platforms. It could be integrated into intelligent tutoring systems or learning analytics tools to provide educators and platforms with insights into student engagement and emotional states. The high accuracy suggests it is a robust solution, potentially near-market for developers of educational software and e-learning solutions.
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Abstract The integration of artificial intelligence in educational environments has the potential to revolutionize teaching and learning by enabling real-time analysis of students’ emotions, which are crucial determinants of engagement, motivation, and learning outcomes. However, accurately detecting and responding to these emotions remains a significant challenge, particularly in online and remote learning settings where direct teacher-student interactions are limited. Traditional educational approaches often fail to account for the emotional states of students, which can lead to disengagement and reduced learning effectiveness. The current study addresses this problem by developing a refined convolutional neural network (CNN) model designed to detect students’ emotions with high accuracy, using the FER2013 facial expression recognition dataset. The methodology involved preprocessing the dataset, including normalization and augmentation techniques, to ensure robustness and generalizability of the model. The CNN architecture was carefully designed with multiple convolutional, batch normalization, and dropout layers to optimize its ability to classify seven basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. The model was trained and validated on an 80-20 split of the dataset, with additional measures such as learning rate reduction and early stopping implemented to enhance performance and prevent overfitting. The results demonstrated that the CNN model achieved a test accuracy of 95%, with consistently high precision and recall across all emotion categories. This high level of accuracy indicates that the model is effective at recognizing subtle differences in facial expressions, making it suitable for real-time application in educational settings.
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DOI: 10.1186/s40561-025-00374-5
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