A hybrid deep learning model with feature engineering technique to enhance teacher emotional support on students’ engagement for sustainable education

Understanding students' emotional conditions throughout the learning process is a significant feature of enhancing learning quality. In an academic setting, an extent of emotion is achieved physically or automatically by utilizing a computer. Emotions like curiosity, hope, interest, confusion, enjoyment, anger, pride, shame, frustration, anxiety, and boredom often arise throughout the learning procedure. In educational surroundings, emotions experienced have a robust relationship with a student's academic attainment and personal development. However, developing an emotion detection model utilizing a harmless, contactless, and illumination-independent imaging modality is very challenging. Recently, the arrival of Artificial Intelligence (AI) and deep learning (DL) has opened up novel possibilities for tackling these tasks by automating the procedure of student emotion recognition through facial expression study. The DL-based techniques are utilized to improve the teacher's emotional support for students' engagement for sustainable education. This paper presents a Hybrid Deep Learning Model with Feature Engineering to Enhance Teacher Emotional Support on Students' Engagement (HDLMFE-ETESSE) model for sustainable education. The aim is to progress an effective student emotion recognition to enhance student engagement and learning outcomes for sustainable education. Initially, the image pre-processing stage applies face normalization and facial alignment methods to improve image quality. Furthermore, the ptive separable convolutionX (AdaptSepCX) attention network system is utilized for feature extraction to identify and isolate the most relevant features from raw data. Finally, the hybrid of a convolutional neural network and a bidirectional gated recurrent unit (C-BiG) models is employed for the classification process. The experimental analysis of the HDLMFE-ETESSE approach is examined under the student-engagement dataset. The comparison study of the HDLMFE-ETESSE approach portrayed a superior accuracy value of 98.58% over existing models.