Study of Process-Focused Assessment Using an Algorithm for Facial Expression Recognition Based on a Deep Neural Network Model

This study proposes an approach for process-focused assessment (PFA) utilizing the concept of deep neural networks with a sequence of facial images. Recently, process-based assessment has received significant attention compared to result-based assessment in the field of education. Continuously evaluating and quantifying student engagement, as well as understanding and interacting with teachers in study activities are considered important factors. However, to achieve PFA, from the technical and systematic perspectives, the real-time monitoring of the learning process of students is desired, which requires time consumption and extremely high attention to each student. This study proposes an approach to develop an efficient method for evaluating the process of learning and studying students in real time using facial images. We developed a method for PFA by learning facial expressions using a deep neural network model. The model learns and classifies facial expressions into three categories: easy, neutral, and difficult. Because the demand for online learning is increasing, PFA is required to achieve efficient, convenient, and confident assessment. This study chiefly considers a sequence of 2D image data of students solving some exam problems. The experimental results demonstrate that the proposed approach is feasible and can be applied to PFA in classrooms.