Extracting User Interests from Operation Logs on Museum Devices for Post-Learning

Nowadays, a variety of information on museum collections online has been stored as digital archives. With the increasing use of smartphones and tablets in daily life, visitors can obtain various knowledge of museum exhibits for pre-learning by using mobile devices and applications. Also, interactive learning systems in museums are very active in the field of information engineering, and interactive on-site learning is necessary for recent education. However, existing learning support systems mainly focused on support for pre-learning or on-site learning, and they are not enough to provide more advanced learning in per-learning or to deepen user interests in on-site learning. Therefore, it is necessary to support diverse knowledge levels of users on museum education for post-learning. In this paper, we aim to utilize video materials related to museums to support post-learning based on user interests by analyzing user interactions for exhibits on multimedia museum devices. For this, we propose a scoring method based on four features of user operation log data: keyword appearance frequency, keyword transition, media type, and media transition. Finally, we verified and discussed the effectiveness of our proposed scoring method through a user study.