Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye-tracking research as an example, background information on eye-tracking and possible applications of eye-tracking in statistics education is provided. Teachers indicated that our eye-tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students.
LINK
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.
DOCUMENT
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 diferent). Using students’ gaze data, we ask: How and how well do upper secondary pre-university school students estimate and compare arithmetic means of histograms and case-value plots? We designed four item types: two requiring mean estimation and two requiring means comparison. Analysis of gaze data of 50 students (15–19 years old) solving these items was triangulated with data from cued recall. We found five strategies. Two hypothesized most common strategies for estimating means were confirmed: a strategy associated with horizontal gazes and a strategy associated with vertical gazes. A third, new, count-and-compute strategy was found. Two more strategies emerged for comparing means that take specific features of the distribution into account. In about half of the histogram tasks, students used correct strategies. Surprisingly, when comparing two case-value plots, some students used distribution features that are only relevant for histograms, such as symmetry. As several incorrect strategies related to how and where the data and the distribution of these data are depicted in histograms, future interventions should aim at supporting students in understanding these concepts in histograms. A methodological advantage of eye-tracking data collection is that it reveals more details about students’ problem-solving processes than thinking-aloud protocols. We speculate that spatial gaze data can be re-used to substantiate ideas about the sensorimotor origin of learning mathematics.
LINK
The traffic safety of cyclists is under pressure. The number of fatalities and injuries is increasing, and the number of single-bicycle accidents is on the rise. However, from a traffic safety perspective, the most concerning trend is the growing number of incidents between motorized vehicles and cyclists. In addition to infrastructural solutions, such as more segregated and wider bike lanes, both industry and government are exploring technological developments to better safeguard cyclist safety. One of the technological solutions being considered is the use of C-V2X communication. C-V2X, Cellular Vehicle-to-X, is a technology that enables short-range signal exchanges between road users, informing them of each other's presence. C-V2X can be used, for example, to alert drivers via dedicated in-car information systems about the presence of cyclists on the road (e.g. at crossings). Although the technology and chipsets have been developed, the application of C-V2X to improve cyclist safety has not yet been thoroughly investigated. Therefore, HAN, Gazelle, and ARK Infomotives are researching the impact of C-V2X (on cyclist safety). Using advanced simulations with a digital twin in an urban environment and rural environment, the study will analyze how drivers respond to cyclist presence signals and determine the maximum penetration rate of ‘connected’ cyclists. Based on this, a pilot study will be conducted in a controlled environment on HAN terrain to validate the direction of the simulation results. The project aligns with the Missiegedreven Innovatiebeleid and the KIA Sleuteltechnologieën, specifically within application of digital and information technologies. This proposal aligns with the innovation domain of Semiconductor Technologies by applying advanced sensor and digital connectivity solutions to enhance cyclist safety. The project fits within the theme of Sleuteltechnologieën en Duurzame Materialen of the strategic research agenda of the VH by utilizing digital connectivity, sensor fusion, and data-driven decision-making for safer mobility solutions.
