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Graphs form an integral part during STEM education and the understanding of graphs is essential for the interpretation of data, critical thinking and reasoning in physics. Different previous works have shown that students struggle with specific difficulties, such as a confusion of slope and height, interpreting changes in height and changes in slope and the area underneath a curve. In this work we use eye tracking to study the question how the students&#39; conceptual understanding of the slope and area concept is linked to the visual attention to different areas of a graph. Using machine learning and different gaze-based metrics, the eye-tracking data reveals characteristic visual strategies during solving of quantitative slope and area problems. The results allow the optimization of classifying correct and incorrect answers and the identification of underlying students’ difficulties.

AAPT SM 20

Students’ visual strategies during physics line-graph problems

line-graphs

eye tracking

machine learning

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. The program featured plenary speakers, sessions, workshops and many special activities.

For more information please visit the 2020 AAPT Virtual Summer Meeting page at: https://www.aapt.org/Conferences/SM2020/index.cfm.


The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. 

Graphs form an integral part during STEM education and the understanding of graphs is essential for the interpretation of data, critical thinking and reasoning in physics. Different previous works have shown that students struggle with specific difficulties, such as a confusion of slope and height, interpreting changes in height and changes in slope and the area underneath a curve. In this work we use eye tracking to study the question how the students' conceptual understanding of the slope and area concept is linked to the visual attention to different areas of a graph. Using machine learning and different gaze-based metrics, the eye-tracking data reveals characteristic visual strategies during solving of quantitative slope and area problems. The results allow the optimization of classifying correct and incorrect answers and the identification of underlying students’ difficulties.

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Students’ visual strategies during physics line-graph problems

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