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Lonneke Boels; Alex Lyford; Arthur Bakker; Paul Drijvers – Frontline Learning Research, 2023
Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how…
Descriptors: Data Interpretation, Interpretive Skills, Statistical Distributions, Graphs
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Saskia Schreiter; Markus Vogel – Educational Studies in Mathematics, 2025
The ability to interpret and compare data distributions is an important educational goal. Inherent in the statistical concept of distribution is the need to focus not only on individual data points or small groups of data points (so-called local view), but to perceive a distribution as a whole, allowing to recognize global features such as center,…
Descriptors: Eye Movements, Statistical Distributions, Data Interpretation, Data Analysis
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Lonneke Boels; Arthur Bakker; Wim Van Dooren; Paul Drijvers – Educational Studies in Mathematics, 2025
Many students persistently misinterpret histograms. This calls for closer inspection of students' strategies when interpreting histograms and case-value plots (which look similar but are different). Using students' gaze data, we ask: "How and how well do upper secondary pre-university school students estimate and compare arithmetic means of…
Descriptors: Secondary School Students, Learning Strategies, Data Interpretation, Graphs
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Sebastian Becker; Lynn Knippertz; Stefan Ruzika; Jochen Kuhn – Physical Review Physics Education Research, 2023
Linear functions are an essential part of school and university education. Nevertheless, this topic is challenging for many students--especially in STEM topics. In this article, we contribute to the understanding of learning difficulties in the context of mathematical and physical problems. Here, we present the results of an eye-tracking study on…
Descriptors: Persistence, Context Effect, Learning Strategies, Eye Movements
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Gold, Anne U.; Atkins, Rachel; McNeal, Karen S. – Scholarship and Practice of Undergraduate Research, 2021
Research Experiences for Undergraduate (REU) programs often introduce students to scientific research and STEM career possibilities. However, the program impact on students and their research skill development is not well understood. In a case study with 10 REU students, the authors used eye-tracking and self-report data to determine student…
Descriptors: Undergraduate Students, Graphs, Data Interpretation, Scientific Research
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Moreno-Estevaa, Enrique Garcia; White, Sonia L. J.; Wood, Joanne M.; Black, Alex A. – Frontline Learning Research, 2018
In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of…
Descriptors: Artificial Intelligence, Man Machine Systems, Mathematics Education, Eye Movements
Vonder Embse, Charles Bernard, Jr. – 1987
A graph is an example of a symbol system which encodes information through the juxtaposition of marks in a two dimensional plane; decoding the meaning of a graph uses specific attributes of the human visual system. This study was undertaken to identify some variables critical to graph interpretation as a first step in designing better curriculum…
Descriptors: College Mathematics, Data Interpretation, Eye Fixations, Eye Movements