Video-Based Affect Detection in Noninteractive Learning Environments [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Chen, Yuxuan; Bosch, Nigel; D'Mello, Sidney
Institution	International Educational Data Mining Society
Titel	Video-Based Affect Detection in Noninteractive Learning Environments [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Affective Behavior; Nonverbal Communication; Video Technology; Comprehension; Visual Perception; Student Attitudes; Human Body; College Students; Classification + Suchen Sie Ihr Suchwort? Affective disturbance; Active behaviour; Affektive Störung; Non-verbal communication; Nonverbale Kommunikation; Verstehen; Verständnis; Visuelle Wahrnehmung; Schülerverhalten; Menschlicher Körper; Collegestudent; Classification system; Klassifikation; Klassifikationssystem
Abstract	The current paper explores possible solutions to the problem of detecting affective states from facial expressions during text/diagram comprehension, a context devoid of interactive events that can be used to infer affect. These data present an interesting challenge for face-based affect detection because likely locations of affective facial expressions within videos of students' faces are entirely unknown. In the current study, students engaged in a text/diagram comprehension activity after which they self-reported their levels of confusion, frustration, and engagement. Data were chosen from various locations within the videos, and texture-based facial features were extracted to build affect detectors. Varying amounts of data were used as well to determine an appropriate window of data to analyze for each affect detector. Detector performance was measured using Area Under the ROC Curve (AUC), where chance level is 0.5 and perfect classification is 1. Confusion (AUC = 0.637), engagement (AUC = 0.554), and frustration (AUC = 0.609) were detected at above-chance levels. Prospects for improving the method of finding likely positions of affective states are also discussed. [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