RANCANG BANGUN APLIKASI DETEKSI STUDENT ENGAGEMENT SECARA REALTIME MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK BERBASIS WEBCAM

Online and hybrid learning require support systems that can assist educators in assessing student engagement more objectively. Student engagement cannot be adequately understood solely through attendance or click-based activity, as engagement encompasses behavioral, cognitive, emotional, and social dimensions. This study develops a real-time student engagement detection application based on facial expressions by integrating MediaPipe Face Detection with the Mini-Xception model. The FER-2013 dataset was used to train a seven-class facial expression classification model, comprising angry, disgust, fear, happy, neutral, sad, and surprise. The resulting facial expression classifications were subsequently mapped into two engagement categories: Engaged and Disengaged. The research methodology comprised image preprocessing, data augmentation, Mini-Xception model design, model training, classification evaluation, engagement mapping evaluation, and computational resource utilization monitoring.