Implementation of deep reinforcement learning models for emotion detection and personalization of learning in hybrid educational environments.

Abstract

The integration of artificial intelligence in education has shown great potential to improve student's learning experience through emotion detection and the personalization of learning. Many educational settings lack adequate mechanisms to dynamically adapt to students' emotions, which can negatively impact their academic performance and engagement. This study addresses this problem by implementing a deep reinforcement learning model to detect emotions in real-time and personalize teaching strategies in a hybrid educational environment. Using data from 500 students, captured through cameras, microphones, and biometric sensors and pre-processed with advanced techniques such as histogram equalization and noise reduction, the deep reinforcement learning model was trained and validated to improve the detection accuracy of emotions and the personalization of learning. The results showed a significant improvement in the accuracy of emotion detection, going from 72.4% before the implementation of the system to 89.3% after. Real-time adaptability also increased from 68.5 to 87.6%, while learning personalization rose from 70.2 to 90.1%. K-fold cross-validation with k = 10 confirmed the robustness and generalization of the model, with consistently high scores in all evaluated metrics. This study demonstrates that integrating reinforcement learning models for emotion detection and learning personalization can transform education, providing a more adaptive and student-centered learning experience. These findings identify the potential of these technologies to improve academic performance and student engagement, offering a solid foundation for future research and implementation.