Development of Face Expression Recognition Model to Support Learning Feedback in Higher Education

Abstract

Face expressions offer a non-verbal channel for understanding student engagement and feedback in higher education learning environment. With the rise of affective computing, face expression recognition (FER) applications have gained attention for their ability to the recognize and respond to learners’ emotional cues in real time. Nevertheless, developing a stable FER model often involves complex deep learning architectures and large-scale annotated datasets. Therefore, this study presents the development of a FER model using Google Teachable Machine (GTM) to support learning feedback in higher education. The proposed FER model can classify five categories of face expressions. A dataset comprising 600 face images was collected and divided into 85% for training and 15% for validation/testing. Model performance was evaluated using accuracy, precision, recall and F1-score metrics. The confusion matrix showed reliable performance for all face expression categories, validating the effectiveness of GTM for accessible FER model.