Abstract
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.
Original language | English |
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Pages | 228-233 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2013 |
Event | 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013 - Taipei, Taiwan Duration: 2013 Dec 6 → 2013 Dec 8 |
Other
Other | 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013 |
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Country/Territory | Taiwan |
City | Taipei |
Period | 2013/12/06 → 2013/12/08 |
Keywords
- concentration assessment
- crowdsourcing
- experiment
ASJC Scopus subject areas
- Artificial Intelligence