Using EDA measures to detect emotional expressions during family science activities, a methodological perspective

References

• Avelar, D., et al., 2022. Children and parents’ physiological arousal and emotions during shared and independent e-book reading: a preliminary study. International journal of child-computer interaction, 33, 100507. https://doi.org/10.1016/j.ijcci.2022.100507.
   Google Scholar
• Bai, J., 1997. Estimating multiple breaks one at a time. Econometric theory, 13 (3), 315–352. https://doi.org/10.1017/S0266466600005831.
   Web of Science ®Google Scholar
• Bai, J., and Perron, P., 1998. Estimating and testing linear models with multiple structural changes. Econometrica, 66 (1), 47–78. https://doi.org/10.2307/2998540.
   Web of Science ®Google Scholar
• Bai, J., and Perron, P., 2003. Computation and analysis of multiple structural change models. Journal of applied econometrics, 18 (1), 1–22. https://doi.org/10.1002/jae.659.
   Web of Science ®Google Scholar
• Bellocchi, A., C. Quigley, and K. Otrel-Cass, eds. 2016. Exploring emotions, aesthetics and wellbeing in science education research (Vol. 13). New York, NY: Springer.
   Google Scholar
• Bellocchi, A., and Ritchie, S.M., 2015. “I was proud of myself that I didn’t give up and I did it”: experiences of pride and triumph in learning science. Science education, 99 (4), 638–668. https://doi.org/10.1002/sce.21159.
   Web of Science ®Google Scholar
• Berntson, G.G., and Cacioppo, J.T., 2000. From homeostasis to allodynamic regulation. In: J.T Cacioppo, L.G. Tassinary, and G.G. Berntson, eds. Handbook of psychophysiology. New York: Cambridge University Press, 459–481.
   Google Scholar
• Bradley, M.M., and Lang, P.J., 1994. Measuring emotion: the self-assessment Manikin and the semantic differential. Journal of behaviour therapy and experimental psychiatry, 25 (1), 49–59. https://doi.org/10.1016/0005-7916(94)90063-9.
   PubMed Web of Science ®Google Scholar
• Brodien Hapairai, P.M., Walters, G., and Li, S., 2018. The effectiveness of ad-induced emotion in reducing tourist risk perceptions towards politically unstable destinations. Tourism recreation research, 43 (4), 483–496. https://doi.org/10.1080/02508281.2018.1470737.
   Web of Science ®Google Scholar
• Cain, R. & Lee, V.R., 2016. Measuring electrodermal activity to capture engagement in an afterschool maker program. In Proceedings of the 6th annual conference on creativity and fabrication in education, 78–81. https://doi.org/10.1145/3003397.3003409
   Google Scholar
• Dawson, M.E., Schell, A.M., and Filion, D.L., 2017. The electrodermal system. In: J.T. Cacioppo, L.G. Tassinary, and G.G. Berntson, eds. Handbook of psychophysiology. Cambridge University Press, 217–243.
   Google Scholar
• Eisenhauer, S., 2019. Youths’ individual pathways towards contextual well-being: utilizing electrodermal activity as an ethnographic tool at a theatre after-school program. Ethos (Berkeley, Calif), 47 (2), 168–189. https://doi.org/10.1111/etho.12235.
   Web of Science ®Google Scholar
• Falk, J.H., et al., 2018. The contribution of science-rich resources to public science interest. Journal of research in science teaching, 55 (3), 422–445. https://doi.org/10.1002/tea.21425.
   Web of Science ®Google Scholar
• Hadinejad, A., et al., 2019. Emotional responses to tourism advertisements: the application of FaceReader™. Tourism recreation research, 44 (1), 131–135. https://doi.org/10.1080/02508281.2018.1505228.
   Web of Science ®Google Scholar
• Hektner, J.M., Schmidt, J.A., and Csikszentmihalyi, M., 2007. Experience sampling method: measuring the quality of everyday life. Thousand Oaks, CA: Sage.
   Google Scholar
• Hetland, A., et al., 2018. Skiing and thinking about it: moment-to-moment and retrospective analysis of emotions in an extreme sport. Frontiers in psychology, 9 (971), 9–24. https://doi.org/10.3389/fpsyg.2018.00009.
   PubMed Web of Science ®Google Scholar
• Horvers, A., et al., 2021. Detecting emotions through electrodermal activity in learning contexts: a systematic review. Sensors, 21 (23), 7869. https://doi.org/10.3390/s21237869.
   PubMed Web of Science ®Google Scholar
• Hufnagel, E., and Kelly, G.J., 2018. Examining emotional expressions in discourse: methodological considerations. Cultural studies of science education, 13 (4), 905–924. https://doi.org/10.1007/s11422-017-9806-4.
   Web of Science ®Google Scholar
• Immordino-Yang, M.H., and Damasio, A.R., 2007. We feel, therefore we learn: the relevance of affective and social neuroscience to education. Mind, brain, and education, 1, 3–10.
   Web of Science ®Google Scholar
• Lee, V.R., Fischback, L., and Cain, R., 2019. A wearables-based approach to detect and identify momentary engagement in afterschool Makerspace programs. Contemporary educational psychology, 59, 101789. https://doi.org/10.1016/j.cedpsych.2019.101789.
   Web of Science ®Google Scholar
• Li, S., et al., 2018. Using skin conductance and facial electromyography to measure emotional responses to tourism advertising. Current issues in tourism, 21 (15), 1761–1783. https://doi.org/10.1080/13683500.2016.1223023.
   Web of Science ®Google Scholar
• Mano, H., 1996. Assessing emotional reactions to TV ads: A replication and extension with a brief adjective checklist. ACR North American advances, 23 (1), 63–69.
   Google Scholar
• Matsumoto, R.R., et al., 1990. Fundamentals of neuroscience, edited by J.T. Cacioppo and L.G. Tassinary, eds. Principles of psychophysiology: physical, social, and inferential elements. New York: Cambridge University Press, 58–112.
   Google Scholar
• Mauss, I.B., and Robinson, M.D., 2009. Measures of emotion: a review. Cognition and emotion, 23 (2), 209–237. https://doi.org/10.1080/02699930802204677.
   PubMed Web of Science ®Google Scholar
• National Research Council, 2009. Learning science in informal environments: People, places, and pursuits. Washington, DC: National Academies Press.
   Google Scholar
• Noordewier, M.K., and Breugelmans, S.M., 2013. On the valence of surprise. Cognition and emotion, 27 (7), 1326–1334.
   PubMed Web of Science ®Google Scholar
• Pekrun, R., 2006. The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educational psychology review, 18 (4), 315–341.
   Web of Science ®Google Scholar
• Pekrun, R., 2014. Emotions and learning. Vol. 24. Geneva: International Academy of Education (IAE).
   Google Scholar
• Poels, K., and Dewitte, S., 2006. How to capture the heart? Reviewing 20 years of emotion measurement in advertising. Journal of advertising research, 46 (1), 18–37. https://doi.org/10.2501/S0021849906060041.
   Web of Science ®Google Scholar
• Rappolt-Schlichtmann, G., et al., 2017. Core emotion and engagement in informal science learning. Exhibition, 36 (1), 42–51.
   Google Scholar
• R Core Team, 2021. R: a language and environment for statistical computing (Version 4.1.1) [Computer software]. R Foundation for Statistical Computing. Available from: https://www.R-project.org/
   Google Scholar
• Robinson, M.D., and Clore, G.L., 2002. Episodic and semantic knowledge in emotional self-report: evidence for two judgment processes. Journal of personality and social psychology, 83 (1), 198–215. https://doi.org/10.1037/0022-3514.83.1.198.
   PubMed Web of Science ®Google Scholar
• RStudio Team, 2021. RStudio: integrated development for R (Version 1.4.1717) [Computer software]. RStudio, PBC. Available from: http://www.rstudio.com/
   Google Scholar
• Russell, J.A., and Mehrabian, A., 1977. Evidence for a three-factor theory of emotions. Journal of research in personality, 11 (3), 273–294. https://doi.org/10.1016/0092-6566(77)90037-X.
   Web of Science ®Google Scholar
• Scherer, K.R., 2009. The dynamic architecture of emotion: evidence for the component process model. Cognition and emotion, 23 (7), 1307–1351. https://doi.org/10.1080/02699930902928969.
   Web of Science ®Google Scholar
• Shaby, N., and Vedder-Weiss, D., 2020. Science identity trajectories throughout school visits to a science museum. Journal of research in science teaching, 57 (5), 733–764. https://doi.org/10.1002/tea.21608.
   Web of Science ®Google Scholar
• Staus, N.L., and Falk, J.H., 2017. The role of emotion in informal science learning: testing an exploratory model. Mind, brain, and education, 11 (2), 45–53. https://doi.org/10.1111/mbe.12139.
   Web of Science ®Google Scholar
• Taylor, S., et al., 2015. Automatic identification of artifacts in electrodermal activity data. In: Proceedings of the 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). Milano, Italy: IEEE, 1934–1937.
   Google Scholar
• Thoits, P.A., 1989. The sociology of emotions. Annual review of sociology, 15, 317–342. https://doi.org/10.1146/annurev.so.15.080189.001533.
   Web of Science ®Google Scholar
• Zeileis, A., et al., 2002. strucchange: an R package for testing for structural change in linear regression models. Journal of statistical software, 7 (2), 1–38. https://doi.org/10.18637/jss.v007.i02.
   Google Scholar
• Zeileis, A., et al., 2003. Testing and dating of structural changes in practice. Computational statistics & data analysis, 44 (1-2), 109–123. https://doi.org/10.1016/S0167-9473(03)00030-6.
   Web of Science ®Google Scholar