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.
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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.
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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.
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As I gaze out of my window, I am met with a totem. This totem is gray and windowless, nestled in between offices and academic buildings. Behind it is a park, and the longer I stare, the deeper it becomes embedded in the natural landscape, after a bit I forget it’s there. But in the corner of my eye I can see another one; another totem. This one intimidates me with its red glow. These buildings came to serve as mystical pillars of data flows to me, they became sites of reification, sites where the cloud finally condensed and data rained down. They assumed a posthuman status; high-tech facilities where humans are only needed to keep other humans out. I always imagined data as something abstract, as a floating entity, but as my encounters with these pillars started a process of materialization, it simultaneously sparked a desire to interrogate and to demystify.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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Er is al redelijk wat kennis ten aanzien van kijkgedrag in de sport, maar deze inzichten hebben voornamelijk betrekking op statische situaties. Momenteel zijn er onderzoeken gaande die inzicht proberen te krijgen in het kijkgedrag tijdens dynamische sportsituaties. Dit artikel beschrijft aan de hand van een onderzoek binnen het hockey welke uitdagingen er daarbij zijn.
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Poster presented at the 14th Congress of the European Society for Research in Mathematics Education, Free University of Bozen-Bolsano, Italy.
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A Manifesto The group of some 17 participants interrupted the UDHR text in real time, infusing it with inclusive terminology, queering its binary language and expanding its gaze to other lifebeings, making it a manifesto for a new world. The newly formulated Universal Declaration of Human and More-Than-Human Rights and Responsibility for a New World would be the manifesto for an alliance of those who insisted on an end to capitalist practices and their destructive effects on the planet.
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This article examines the impact of the COVID-19 pandemic on the sign language interpreting profession drawing on data from a fourth and final survey conducted in June 2021 as part of a series of online “living surveys” during the pandemic. The survey, featuring 331 respondents, highlights significant changes in the occupational conditions and practices of sign language interpreters due to the sudden shift towards remote video-mediated interpreting. The findings reveal a range of challenges faced by interpreters, including the complexities of audience design, lack of backchanneling from deaf consumers, the need for heightened self-monitoring, nuanced conversation management, and team work. Moreover, the study highlights the physical and mental health concerns that have emerged among interpreters as a result of the shift in working conditions, and a need for interpreters to acquire new skills such as coping with the multimodal nature of online interpreting. While the blend of remote, hybrid, and on-site work has introduced certain advantages, it also poses new challenges encompassing workload management, online etiquette, and occupational health concerns. The survey’s findings underscore the resilience and adaptability of SLIs in navigating the shift to remote interpreting, suggesting a lasting transformation in the profession with implications for future practice, training, and research in the post-pandemic era.
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Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult’s home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague
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