Abstract
In on-demand e-learning environments, the lack of direct intervention can lead to a decline in learners’ engagement. To address this issue, systems that estimate the learners’ attitudes and provide feedback have been proposed. However, the acceptability of such systems has not been sufficiently researched. In this study, we investigated the acceptability by people to an e-learning system with internal state feedback, for future personalized learning support. To this end, we developed a system that estimates and visualizes the learner’s internal state in real-time. The system was exhibited in a public space for free use, and users’ impressions were analyzed. To estimate the learners’ internal state, we developed a machine-learning model that recognizes learners’ alertness from facial videos. The system was deployed in an exhibition space, and 131 responses were collected. These responses were coded and analyzed using a co-occurrence network. The result indicated that learners tend to dislike the system due to feelings of being observed by supervisors. In contrast, instructors expressed favorable options toward the introduction of the system.
Atsushi Ashida
Specially-Appointed Assistant Professor
Noriko Takemura
Guest Associate Professor
She is working on ambient intelligence and gait recognition using pattern recognition and machine learning.
Hideaki Hayashi
Associate Professor
Hideaki Hayashi is an associate professor with D3 Center, the University of Osaka. His research interests focus on neural networks, machine learning, and medical data analysis.
Hajime Nagahara
Professor
He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.