Translating Head Motion into Attention - Towards Processing of Student's Body-Language [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Raca, Mirko; Kidzinski, Lukasz; Dillenbourg, Pierre
Institution	International Educational Data Mining Society
Titel	Translating Head Motion into Attention - Towards Processing of Student's Body-Language [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (7 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Nonverbal Communication; Attention; Motion; Video Technology; Students; Measurement Techniques; Classification; Computers + Suchen Sie Ihr Suchwort? Non-verbal communication; Nonverbale Kommunikation; Aufmerksamkeit; Bewegungsablauf; Student; Schüler; Schülerin; Studentin; Messtechnik; Classification system; Klassifikation; Klassifikationssystem; Digitalrechner
Abstract	Evidence has shown that student's attention is a crucial factor for engagement and learning gain. Although it can be accurately assessed ad-hoc by an experienced teacher, continuous contact with all students in a large class is difficult to maintain and requires training for novice practitioners. We continue our previous work on investigating unobtrusive measures of body-language in order to predict student's attention during the class, and provide teachers with a support system to help them to "scale-up" to a large class. Our work here is focused on head-motion, by which we aim to mimic large-scale gaze tracking. By using new computer vision techniques we are able to extract head poses of all students in the video-stream from the class. After defining several measures about head motion, we checked their significance and attempted to demonstrate their value by fitting a mixture model and training support vector machines (SVM) classifiers. We show that drops in attention are reflected in a decreased intensity of head movement. We were also able to reach 61.86% correct classifications of student attention on a 3-point scale. [For complete proceedings, see ED560503.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01