Concentration is important for students to conduct efficient learning in a class, and an effective assessment of students' concentration level in a class is useful for students to review class materials after lessons, as well as for lecturers to adjust their teaching strategies for self-improvement. Although a number of concentration assessment approaches have been proposed, conventional approaches are generally time/money expensive (e.g., expert opinions), inaccurate (e.g., computer vision-based approaches), and intrusive (e.g., wearable sensor-based approaches). In this study, we propose a novel approach, called Concentration Level Assessment System (CLAS), which combines a markovian Doze-and-Wake Model (DAWM) and the emerging crowdsourcing technique to enable effective concentration assessment of class videos. Using realistic datasets of class videos, we conduct a comprehensive set of synthetic analysis and Internet experiments, the results demonstrate that CLAS is capable of yielding an accuracy up to 98% with 86% cost savings. Moreover, CLAS is simple, effective, and scalable, and it shows promises in facilitating advanced applications for efficiency, productivity, and safety in the future.
|出版狀態||已發佈 - 2013|
|事件||2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013 - Taipei, 臺灣|
持續時間: 2013 十二月 6 → 2013 十二月 8
|其他||2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013|
|期間||2013/12/06 → 2013/12/08|
ASJC Scopus subject areas
- Artificial Intelligence