Affective State Prediction in a Mobile Setting Using Wearable Biometric Sensors and Stylus [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Wampfler, Rafael; Klingler, Severin; Solenthaler, Barbara; Schinazi, Victor R.; Gross, Markus
Titel	Affective State Prediction in a Mobile Setting Using Wearable Biometric Sensors and Stylus [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Affective Behavior; Prediction; Problem Solving; Mathematics; Handheld Devices; Educational Technology; Technology Uses in Education; Classification; Measurement Equipment; Physiology; Undergraduate Students; Feedback (Response) + Suchen Sie Ihr Suchwort? Affective disturbance; Active behaviour; Affektive Störung; Vorhersage; Problemlösen; Mathematik; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Classification system; Klassifikation; Klassifikationssystem; Messinstrument; Physiologie
Abstract	The role of affective states in learning has recently attracted considerable attention in education research. The accurate prediction of affective states can help increase the learning gain by incorporating targeted interventions that are capable of adjusting to changes in the individual affective states of students. Until recently, most work on the prediction of affective states has relied on expensive and stationary lab devices that are not well suited for classrooms and everyday use. Here, we present an automated pipeline capable of accurately predicting (AUC up to 0.86) the affective states of participants solving tablet-based math tasks using signals from low-cost mobile bio-sensors. In addition, we show that we can achieve a similar classification performance (AUC up to 0.84) by only using handwriting data recorded from a stylus while students solved the math tasks. Given the emerging digitization of classrooms and increased reliance on tablets as teaching tools, stylus data may be a viable alternative to bio-sensors for the prediction of affective states. [For the full proceedings, see ED599096.] (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