Abstract
The chapter describes research on using eye-tracking technology to analyze the visual attention of the participants while they are solving a multiple-choice problem involving both mathematics and physics. The study involved over a hundred people of varying levels of knowledge and skills in mathematics and physics. Among them were academics (in physics, mathematics, and computer science), university students of physics, mathematics, computer science, and biology, as well as secondary school students. The included analysis refers to paper by Tsai et al. (2012) and comprises of the verification of their hypothesis H1 in the context of H3. Our results confirm the general trend of devoting the most visual attention to the chosen option, but indicate essential differences and several limitations. We have empirically proved that the trend is inverted or disrupted in numerous cases. We have distinguished several reasons for that, such as high motivation to solve the problem together with high scientific expertise (and thus high criticism), or the opposite—significantly low motivation, level of knowledge insufficient to solve the task, as well as lack of conviction about the correctness of the chosen answer. We also analyzed how the participants’ problem-solving strategies influenced their visual attention. In the analysis of the research results, we have distinguished and described diverse task-solving strategies, i. e., two kinds of strategies of eliminating wrong answers, two kinds of strategies of singling out the correct answer—with or without verifying it, a strategy involving increased cognitive load associated with the need to indicate the incorrect answer during the decision-making process, a strategy influenced by the participant’s precariousness, and a strategy of answering by chance—blindly guessing.
In this chapter, we have also discussed the role and importance of long fixations (longer than approx. 500 ms) and have proposed a methodology based on the use of the relative percentage change in pupil diameter within the time and area accompanying the longest fixations. This methodology can be helpful for identifying an increase in cognitive load while solving tasks and during the decision-making process.
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Sajka, M., Rosiek, R. (2021). Analysis of Aspects of Visual Attention When Solving Multiple-Choice Science Problems. In: Devetak, I., Glažar, S.A. (eds) Applying Bio-Measurements Methodologies in Science Education Research. Springer, Cham. https://doi.org/10.1007/978-3-030-71535-9_10
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