Recognition of Student Engagement State in a Classroom Environment Using Deep and Efficient Transfer Learning Algorithm

A student's engagement in a real classroom environment usually varies with respect to time. Moreover, both genders may also engage differently during lecture procession. Previous research measures students' engagement either from the assessment outcome or by observing their gestures in online or real but controlled classroom environments with limited students. However, most works either manually assess the engagement level in online class environments or use limited features for automatic computation. Moreover, the demographic impact on students' engagement in the real classroom environment is limited and needs further exploration. This work is intended to compute student engagement in a real but least controlled classroom environment with 45 students. More precisely, the main contributions of this work are twofold. First, we proposed an efficient transfer-learning-based VGG16 model with extended layer, and fine-tuned hyperparameters to compute the students' engagement level in a real classroom environment. Overall, 90% accuracy and 0.5 N seconds computational time were achieved in terms of computation for engaged and non-engaged students. Subsequently, we incorporated inferential statistics to measure the impact of time while performing 14 experiments. We performed six experiments for gender impact on students' engagement. Overall, inferential analysis reveals the positive impact of time and gender on students' engagement levels in a real classroom environment. The comparisons were also performed by various transfer learning algorithms. The proposed work may help to improve the quality of educational content delivery and decision making for educational institutions.