Eye-tracking indicators of emotions during problem solving
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Faculty of Mathematics Physics and Technical Science Pedagogical University of Cracow, Poland
Publication date: 2017-11-16
JoMS 2017;34(3):181-196
Emotions, especially stress, affect a student’s intellectual functioning, achievement and effectiveness in problem solving. Sources of stress include new and difficult situations in which there is frustration, essentially the set of unpleasant emotions associated with the inability to implement necessities or the difficulty in solving a task or problem. The reaction to stress is often an escape from the frustrating situation or resignation, which in the case of learning leads to a reduction of effectiveness. Therefore, an important aspect of teachers’ work is helping students to relieve stress and maintain their motivation to learn. This is particularly essential for school subjects that are generally considered by students to be difficult. This article discusses the results of studies in which the eye-tracking technique was used to identify emotions, especially stress, experienced during problem solving in physics, mathematics, computer science and biology. In this experiment, eye movement parameters and survey data were analysed with the aim of obtaining information on the subjective assessment of the stress level experienced during problem solving in the field of science. Participants included 45 pupils (middle school students). The results confirm the possibility of using eye-tracking data to diagnose negative emotions. The results of the studies might also be useful for teachers, who might be able to design a system of rapid intervention and student support, with the goal of stopping students from a quick resignation of solving problems. This study fits into the new trend of neurodidactics for the development of interdisciplinary research in the area of teaching.
Alshehri, M., Alghowinem, Sh. (2013). An exploratory study of detecting emotion states using eye-tracking technology, „Science and Information Conference”, October 7–9, London, UK, pp. 428–433.
Błasiak, W., Godlewska, M., Rosiek, R., Wcisło, D. (2012). Spectrum of physics comprehension, „European Journal of Physics”, vol. 33, pp. 565–571.
Burleson, W. (2006). Affective Learning Companions: strategies for empathetic agents with real-time multimodal affective sensing to fostermeta-cognitive and meta-affective approaches to learning, motivation, and perseverance, Thesis for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology, p. 11.
Caine, G., Caine R. (2014). Seeing education from the perspective of natural learning. In the F.M. Duffy Reports, vol. 19, no. 1, pp. 1–43.
Davidson, R.J., Begley, S. (2013). Życie emocjonalne mózgu. Translated by Beata Radwan, Jagiellonian University Publishing, pp. 21–24.
Erk, S., Kiefer, M., Grothe, J., Wunderlich, A.P., Spitzer, M., Walter, H. (2003). Emotional context modulates subsequent memory effect, NeuroImage 18, pp. 439–447.
Fredrickson, B.L. (2004). The broaden-and-build theory of positive emotions, Philosophical Transaction of the Royal Society B., 359, pp. 1367–1377.
Gruhn, W. (2004). Neurodidactics – a new scientific trend in music education?, Paper presented at the XXVI ISME International Conference, Tenerife, Spain, pp. 1–8.
Hatamikia, S., Maghooli, K., Nasrabadi, A.M. (2014). The emotion recognition system based on autoregressive model and sequential forward feature selection of electroencephalogram signals, „Journal of Medical Signals and Sensors”, 4(3), pp. 194–201.
Jaques, N., Conati, C., Harley, J., Azevedo, R. (2014). Predicting affect from gaze data during interaction with an intelligent tutoring system, Intelligent Tutoring Systems, Proceedings of the 12th International Conference, Honolulu, USA, pp. 29–38.
Krauzowicz, J. (2013). Stress – builder or destroyer of cognitive processes?, Annales Academia e Medica e Stetinensis, 59, pp. 84–92.
Marshall, S.P. (2007). Identifying cognitive state from eye metrics, Aviation, Space and Environmental Medicine 78, pp. 165–175.
Muldner, K., Burleson, W. (2015). Utilizing sensor data to model students’ creativity in a digital environment, Computers in Human Behavior 42, pp. 127–137.
Muldner, K., Christopherson, R., Atkinson, R., Burleson, W. (2009). Investigating the utility of eye-tracking information on affect and reasoning for user, Proceedings of the 17th Int. Conf. on User Modeling Adaptation, and Personalization, Trento, Italy, pp. 138–149.
Prendinger, H., Ishizuka, M. (2005). The empathic companion: A character-based interface that addresses users’ affective states, APAI 19, 3–4, pp. 267–285.
Smilek, D., Carriere, J.S., Cheyne, J.A. (2010). Out of mind, out of sight eye blinking asindicator and embodiment of mind wandering, Psych. Sci. 21, 6, pp. 786–789. DOI:10.1177/0956797610368063.
Spitzer, M. (2006). Brain research and learning over the life cycle, Personalising Education, OECD, Paris, pp. 47–62.
Stolińska, A., Andrzejewska, M., Błasiak, W., Godlewska, M., Pęczkowski, P., Rosiek, R., Rożek, B., Sajka, M., Wcisło, D. (2014). Analysis of saccadic eye movements of experts and novices when solving text tasks, New Technologies in Science Education. Cracow: Pedagogical University of Cracow, pp. 7–20.
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