Youth engagement during making: using electrodermal activity data and first-person video to generate evidence-based conjectures
Information and Learning Sciences
Article publication date: 14 June 2021
Issue publication date: 16 July 2021
This paper aims to introduce and explores the use of electrodermal activity (EDA) data as a tool for obtaining data about youth engagement during maker learning activities.
EDA and survey data were collected from a yearlong afterschool maker program for teens that met weekly and was hosted at a children’s museum. Data from four youth who were simultaneously present for eight weeks were examined to ascertain what experiences and activities were more or less engaging for them, based on psychophysiological measures.
Most of the focal youth appeared to show higher levels of engagement by survey measures throughout the program. However, when examined by smaller time intervals, certain activities appeared to be more engaging. Planning of maker activities was one space where engagement was higher. Completing sewing projects with minimal social interaction appeared to be less engaging. Specific activities involving common maker technologies yielded mixed levels of engagement.
Some research is emerging that uses EDA data as a basis for generating inferences about various states while participating in maker learning activities. This paper provides a novel analysis building on some techniques established in the still emergent body of prior research in this area.
The author acknowledges the valuable contributions of Liam Fischback, Ryan Cain, Diamond Dang, and Kourtney Schut as well as the participating makerspace personnel and youth. This work was supported in part by funding from the National Science Foundation under Grant CNS-1623401. The opinions expressed herein are those of the author and do not necessarily reflect those of the National Science Foundation.
Lee, V.R. (2021), "Youth engagement during making: using electrodermal activity data and first-person video to generate evidence-based conjectures", Information and Learning Sciences, Vol. 122 No. 3/4, pp. 270-291. https://doi.org/10.1108/ILS-08-2020-0178
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