Development of Scene Segmentation to Improve Work Efficiency of Learner Monitoring

Since the outbreak of COVID-19, many universities have introduced video conference systems and e-learning systems, but these issues still make it difficult to obtain actual learning time. However, during operation of these systems, the participants or learner may not watch the video because they have also other tasks. For this reason, we have developed some prototype systems for monitoring learner behavior at watching learning content. In this paper, we describe the development of video scene segmentation based on a video correlation matrix, which reflects learner behavior, aiming to enhance the efficiency of learner monitoring. From our evaluation, we have been able to segment scenes from videos capturing learners based on the difference in correlation values between adjacent frames.