A Coherent Way of Detecting Learner’s Academic Emotions via Live Camera Using CNN and Deep LSTM

Abstract

Academic Emotion Detection is fundamentally a system for detecting emotions. The system's main goal was to identify feelings expressed while attending online lectures during the COVID-19 epidemic. The topic Academic Emotion Detection using Machine Learning focuses on utilizing machine learning and deep learning to identify human face emotions in light of the shift to online learning.Our research has a limited scope, it focuses on four academic emotions: confusion, boredom, engagement, and frustration. A person may experience a wide range of other emotions as well. Here, we have used CNN and Deep LSTM for the prediction of said four emotions and it has been observed it increases the accuracy of prediction and effectiveness. We even incorporated a portion of a questionnaire into our research to compare our results with genuine human experiences.Concurrent Neural Network (CNN), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) are three different algorithms from the deep learning area that we have used in this study to examine how they operate and identify similarities and differences.