Recurrent Convolution Neural Network with Group-Wise Enhancement for Student English Classroom Learning Behavior Recognition

Due to the increase in the number of students joining colleges across the world, current college classrooms are larger, and this has a stressed university lecturer teaching those large classes and conducting lectures efficiently. Therefore, the Recurrent Convolution Neural Network with Group-Wise Enhancement (RCNNGWE) is proposed in this research for English classroom learning behavior recognition. The bilateral filter is used for preprocessing to reduce noise when reserving edges and enhancing the visual feature clarity. Then, the feature extraction is performed by Scale-Invariant Feature Transform (SIFT) which extracts visual features to scale and orientation. The RCNNGWE works well because it is able to simulate environment within the classroom as well as the different behaviors of students in the class. To further propagate key behavioral patterns the proposed model, need to localize groupwise operations which helps to improve recognition accuracy. The GWE continues to enrich the functionality of the proposed model by grouping inputs and processing them in a separate aggregate, which enables identification of behaviors of students. The RCNNGWE obtained 93.75% accuracy, 87.27% f1-score, 86.41% precision, 87.26% mean Average Precision (mAP) and 91.64% recall for Student Learning Behavior (SLB) dataset.