EuroVisEducation2024

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https://diglib.eg.org/handle/10.2312/3607002

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    Designing Born-Accessible Courses in Data Science and Visualization: Challenges and Opportunities of a Remote Curriculum Taught by Blind Instructors to Blind Students
    (The Eurographics Association, 2024) Seo, JooYoung; O'Modhrain, Sile; Xia, Yilin; Kamath, Sanchita; Lee, Bongshin; Coughlan, James M.; Firat, Elif E.; Laramee, Robert S.; Andersen, Nicklas Sindelv
    While recent years have seen a growing interest in accessible visualization tools and techniques for blind people, little attention is paid to the learning opportunities and teaching strategies of data science and visualization tailored for blind individuals. Whereas the former focuses on the accessibility and usability issues of data visualization tools, the latter is concerned with the learnability of concepts and skills for data science and visualization. In this paper, we present novel approaches to teaching data science and visualization to blind students in an online setting. Taught by blind instructors, nine blind learners having a wide range of professional backgrounds participated in a two-week summer course. We describe the course design, teaching strategies, and learning outcomes.We also discuss the challenges and opportunities of teaching data science and visualization to blind students. Our work contributes to the growing body of knowledge on accessible data science and visualization education, and provides insights into the design of online courses for blind students.
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    Impacts of Student LLM Usage on Creativity in Data Visualization Education
    (The Eurographics Association, 2024) Ahmad, Mak; Ma, Kwan-Liu; Firat, Elif E.; Laramee, Robert S.; Andersen, Nicklas Sindelv
    Large language models (LLMs) offer new possibilities for enhancing data visualization education, but the impacts on student experiences remain underexplored. Leveraging tenets of behaviorism, constructivism and experiential learning theories, our mixed-methods study examines LLM integration strategies. We conducted two experiments with different groups of students. The first experiment involved 95 Masters of Business Analytics students who created data narratives based on the Titanic dataset either with or without LLM assistance. The second experiment involved 30 Masters of Information and Data Science students who suggested effective visual encodings for different scenarios with or without LLM assistance in a Viz of the Day activity. We collected quantitative data from surveys and project scores and qualitative data from open-ended responses. Our results show that LLMs can enhance students' ability to create clear, accurate, and effective data stories and visualizations, but they can also pose challenges, such as requiring careful prompt crafting, producing inconsistent or inaccurate outputs, and potentially reducing students' creativity and critical thinking. We discuss how our findings suggest a nuanced balance between LLM guidance and human creativity in data storytelling education and practice, and provide specific directions for future research on LLMs and data visualization.
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    More Than Chatting: Conversational LLMs for Enhancing Data Visualization Competencies
    (The Eurographics Association, 2024) Ahmad, Mak; Ma, Kwan-Liu; Firat, Elif E.; Laramee, Robert S.; Andersen, Nicklas Sindelv
    This study investigates the integration of Large Language Models (LLMs) like ChatGPT and Claude into data visualization courses to enhance literacy among computer science students. Through a structured 3-week workshop involving 30 graduate students, we examine the effects of LLM-assisted conversational prompting on students' visualization skills and confidence. Our findings reveal that while engagement and confidence levels increased significantly, improvements in actual visualization proficiency were modest. Our study underscores the importance of prompt engineering skills in maximizing the educational value of LLMs and offers evidence-based insights for software engineering educators on effectively leveraging conversational AI. This research contributes to the ongoing discussion on incorporating AI tools in education, providing a foundation for future ethical and effective LLM integration strategies.
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    Mapping the Landscape of Data Visualizations in Schools and Educational Resources
    (The Eurographics Association, 2024) Boucher, Magdalena; Stoiber, Christina; Kejstova, Magdalena; Kandlhofer, Martin; Ertl, Alena; Kriglstein, Simone; Aigner, Wolfgang; Firat, Elif E.; Laramee, Robert S.; Andersen, Nicklas Sindelv
    This study explores the use of data visualizations in school education, examining how they are integrated into teaching practices and materials. By conducting semi-structured interviews with 15 teachers across Austria and Slovakia, coupled with a thorough classification of 5,655 data visualizations in 54 Austrian school textbooks, we gained insights into the landscape of visualization types used in educational settings. Despite the discovery of a wide array of visualization types, our analysis reveals a predominant reliance on simple business charts, highlighting a gap in the variety of methods and resources available for effectively teaching a wider range of visualizations. From our research, we derive lessons learned that pave the way for future development of educational methods and materials to enhance visualization literacy in schools.
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    EuroVis 2024 Education Papers: Frontmatter
    (The Eurographics Association, 2024) Firat, Elif E.; Laramee, Robert S.; Andersen, Nicklas Sindelv; Firat, Elif E.; Laramee, Robert S.; Andersen, Nicklas Sindelv
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    Vis Repligogy: Towards a Culture of Facilitating Replication Studies in Visualization Pedagogy and Research
    (The Eurographics Association, 2024) Syeda, Uzma Haque; South, Laura; Raynor, Justin; Panavas, Liudas; Saffo, David; Morriss, Tommy; Dunne, Cody; Borkin, Michelle A.; Firat, Elif E.; Laramee, Robert S.; Andersen, Nicklas Sindelv
    In this paper, we present the Vis Repligogy framework that enables conducting replication studies in the class. Replication studies are crucial to strengthening the data visualization field and ensuring its foundations are solid and methods accurate. Although visualization researchers acknowledge the epistemological significance of replications and their necessity to establish trust and reliability, the field has made little progress to support the publication of such studies and, importantly, provide methods to the community to encourage replications. Therefore, we contribute Vis Repligogy, a novel framework to systematically incorporate replications within visualization course curricula that not only teaches students replication and evaluation methodologies but also results in executed replication studies to validate prior work. To validate the feasibility of the framework, we present case studies of two graduate data visualization courses that implemented it. These courses resulted in a total of five replication studies. Finally, we reflect on our experience implementing the Vis Repligogy framework to provide useful recommendations for future use. We envision this framework will encourage instructors to conduct replications in their courses, help facilitate more replications in visualization pedagogy and in research, and support a culture shift towards reproducible research. Supplemental materials of this paper are available at https://osf.io/ncb6d/.