Affective State Prediction in a Mobile Setting using Wearable Biometric Sensors and Stylus

Rafael Wampfler, Severin Klingler, Barbara Solenthaler, Victor R. Schinazi, Markus Gross

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

12 Citations (Scopus)


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 alter-
native to bio-sensors for the prediction of affective states.
Original languageEnglish
Title of host publicationEDM 2019 - Proceedings of the 12th International Conference on Educational Data Mining
EditorsCollin F. Lynch, Agathe Merceron, Michel C. Desmarais, Roger Nkambou
PublisherInternational Educational Data Mining Society (IEDMS)
Number of pages10
ISBN (Electronic)978-1-7336736-0-0
Publication statusPublished - Jul 2019
Externally publishedYes
EventTwelfth International Conference on Educational Data Mining 2019 - Montreal, Canada
Duration: 2 Jul 20195 Jul 2019
Conference number: 12th


ConferenceTwelfth International Conference on Educational Data Mining 2019
Abbreviated titleEDM 2019
Internet address


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