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1. Scaffolding Middle School Students’ Engagements with Historical Data Visualizations

   Jiang, M; Searle, KA (June 2024, International Society of the Learning Sciences)

   Lindgren, R; Asino, T I; Kyza, E A; Looi, C K; Keifert, D T; Suárez, E (Ed.)

   We designed and implemented a data visualization project in a seventh-grade classroom focusing on the loss of tribal lands in Montana, United States of America. We aim to understand how students engaged with various scaffolds and how those scaffolds supported them in critically engaging with historical data through making hands-on projects. We analyzed data from multiple sources, including classroom implementation transcripts, student-created artifacts, and pre- and post-surveys. We observed that while students enjoyed creating data visualizations, they struggled to interpret them within their historical context despite the provision of multiple forms of scaffolds. We believe it is important to design a system of scaffolds to further support students in critically engaging with historical data and in developing critical data literacy. 

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2. Supporting Multidimensional Data Analysis for High-School Students in the Era of Machine Learning

   https://doi.org/10.22318/icls2024.287761  

   Zhou, Xiaofei; Tang, Jingwan; Xiao, Qinqin; Zhou, Yushan; Bai, Zhen (June 2024, International Society of the Learning Sciences)

   Machine Learning (ML) opens exciting scientific opportunities in K-12 STEM classrooms. However, students struggle with interpreting ML patterns due to limited data literacy. Face glyphs offer unique benefit by leveraging our brain’s facial feature processing. Yet, they have limitations like lacking contextual information and data biases. To address this, we created three enhanced face glyph visualizations: feature-independent and feature-aligned range views, and the sequential feature inspector. In a study with 25 high school students, feature-aligned range visualization helped contextual analysis, and the sequential feature inspector reduced missing data risks. Face glyphs also benefit the global interpretation of data. 

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3. Deep Learning on Eye Gaze Data to Classify Student Distraction Level in an Educational VR Environment

   https://doi.org/10.2312/egve.20211326  

   Asish, S. M.; Hossain, E.; Kulshreshth. A. K.; Borst, C. W. (January 2021, International Conference on Artificial Reality and Telexistence Eurographics Symposium on Virtual Environments (ICAT-EGVE))

   Educational VR may increase engagement and retention compared to traditional learning, for some topics or students. However, a student could still get distracted and disengaged due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting distraction. For example, we previously considered gaze visualizations to help teachers understand student attention to better identify or guide distracted students. However, it is not practical for a teacher to monitor a large numbers of student indicators while teaching. To help filter students based on distraction level, we consider a deep learning approach to detect distraction from gaze data. The key aspects are: (1) we created a labeled eye gaze dataset (3.4M data points) from an educational VR environment, (2) we propose an automatic system to gauge a student's distraction level from gaze data, and (3) we apply and compare three deep neural classifiers for this purpose. A proposed CNN-LSTM classifier achieved an accuracy of 89.8\% for classifying distraction, per educational activity section, into one of three levels. 

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4. Using slow reveal graphs to build data literacy skills and increase student engagement

   Hipps, Nina; Bruhn, Jacqueline (January 2026, Science Scope)

   Data literacy is an integral part of overall literacy in the twenty-first century. After participating in a National Science Foundation–funded BIORETS (Research Experiences for Teachers Sites in Biological Sciences) program that emphasized the importance of data literacy, I was motivated to focus more intentionally on data literacy in my middle school classroom. Providing students with strategies for making sense of data is an important component of data literacy. In an effort to develop this data literacy skill, I introduced Slow Reveal Graphs, a teaching strategy that promotes sensemaking about data. By removing contextual information from data visualizations presented in textbooks and the media and asking students to interpret the information as it is provided, students engage in problem solving, gain confidence, and grow their computational thinking skills. As a result, students engage more deeply with the content of graphs and other visual representations of data. 

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5. Building Insights Through Observation: Integrating Art and Science to Support Sensemaking

   https://doi.org/10.1080/08872376.2025.2463906  

   Semmens, Kathryn; Sickler, Jessica; Maxfield, Keri; Goldner, Mark; Curry, Dave; Peddicord, Hilary; Busey, Amy; Kochevar, Randall; Bardar, Erin; Carr, Rachel Hogan (March 2025, Science Scope)

   Data are invaluable for making sense of the world, but critical thinking and observation skills are necessary to support ­sensemaking and improve data literacy. To develop these skills and build student capacity to connect data to content knowledge and interpret multiple meanings, a new education model was created for middle school science classrooms in ­partnership with teachers from across the U.S. The Building Insights through Observation (BIO) model uses hands-on, arts-based instructional approaches with science content (specifically geospatial data visualizations) to lead students in sensemaking about global phenomena. BIO builds on techniques established in the visual arts, focusing on how learners engage with, make meaning from, and think critically about visual information. In BIO, sensemaking is supported through students collectively exploring phenomena through a combination of art and datasets, reimagining data visualization themselves, and identifying wonderings that drive future questions. The BIO goals, approach, and key features of the model are described along with its application to plate tectonics as an example. The framework is highly adaptable for most environmental and Earth science topics and supports differentiation for all learners, allowing for slowing down and discussion of ideas and interpretations, which support student-driven sensemaking of data and Earth science content. 

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