Conference Presentations

Friedman, A., Hawley, K., Rosen, P., & Rahman, M. D. (2024). Enhancing Student Feedback Using Predictive Models in Visual Literacy Courses. In 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-8). IEEE. Kos, Greece.

This paper explores the use of predictive models to enhance feedback quality in visual literacy education, demonstrating how machine learning can support more effective peer review processes.

Friedman, A., Hawley, K., & Rosen, P. (2022). Visual Peer Review in Educational Settings. Paper presented at Research Conference.

An exploration of visual peer review methodologies in educational contexts, examining best practices and challenges in implementing structured feedback systems.

Beasley, Z., Friedman, A., & Rosen, P. (2021). Through the Looking Glass: Insights into Visualization Pedagogy through Sentiment Analysis of Peer Review Text. IEEE Computer Graphics and Applications, 41(5), 59-70. Presented at IEEE VIS 2022.

This work analyzes sentiment patterns in peer review text to understand how students engage with visualization critique and provide insights for pedagogy improvement, including course content focus, engagement across student groups, and expert intervention effectiveness.

Friedman, A., & Beasley, Z.J. (2020). Teaching R with Peer Review and a New Rubric. Presentation at useR! 2020.

This presentation demonstrates the integration of peer review methodologies and rubric-based assessment in teaching R programming for data visualization, providing educators with practical tools for improving student learning outcomes.

Beasley, Z., Friedman, A., Piegl, L., & Rosen, P. (2020). Leveraging Peer Feedback to Improve Visualization Education. In 2020 IEEE Pacific Visualization Symposium (PacificVis) (pp. 101-105). IEEE. Tianjin, China.

This paper demonstrates how structured peer feedback can significantly improve learning outcomes in visualization courses through systematic evaluation and critique, including projects that reuse code and visualizations in a feedback-guided, continual improvement process.

Friedman, A. (2018). Teaching R via Peer Review. Presentation at useR! Conference 2018, Brisbane, Australia.

This presentation explores pedagogical approaches for teaching R programming through peer review mechanisms, demonstrating how collaborative feedback can enhance student understanding of statistical programming and data visualization.

Friedman, A., & Rosen, P. (2018). MyReviewers Visualization Peer Review. Presentation at The 5th International Conference on Writing Analytics, St. Petersburg, FL.

This presentation introduces the MyReviewers platform for visualization peer review, demonstrating how writing analytics can be applied to assess and improve the quality of student feedback in visualization education.

Friedman, A., & Rosen, P. (2017). Leveraging Peer Review in Visualization Education: A Proposal for a New Mode. In IEEE Pedagogy of Data Visualization Workshop, Phoenix, AZ.

This seminal paper proposes a new pedagogical mode for visualization education that leverages peer review as a core learning mechanism. It establishes the theoretical framework and practical foundation for the Visual Peer Review project, demonstrating how structured peer feedback can transform visualization teaching and learning.