Using machine learning to understand students' gaze patterns on graphing tasks

Authors Alex Lyford, Lonneke Boels
Published in Conference Proceedings: ICOTS 11 2022
Publication date 2022
Research groups Mathematical and analytical competence of professionals
Type Lecture


Graphs are ubiquitous. Many graphs, including histograms, bar charts, and stacked dotplots, have proven tricky to interpret. Students’ gaze data can indicate students’ interpretation strategies on these graphs. We therefore explore the question: In what way can machine learning quantify differences in students’ gaze data when interpreting two near-identical histograms with graph tasks in between? Our work provides evidence that using machine learning in conjunction with gaze data can provide insight into how students analyze and interpret graphs. This approach also sheds light on the ways in which students may better understand a graph after first being presented with other graph types, including dotplots. We conclude with a model that can accurately differentiate between the first and second time a student solved near-identical histogram tasks.

Language English
Published in Conference Proceedings: ICOTS 11 2022
Key words machine learning, statistics education, students' gaze data, interpretation strategies
Digital Object Identifier 10.52041/iase.icots11.T8D2