Our peer-reviewed research on visualization education and assessment
Dinh, L., Friedman, A., & Hawley, K. (2024). Examining peer review network dynamics in higher education visual communication courses using ERGM. Computers and Education Open, 7, 100222.
This study examines the network dynamics of peer review in visual communication courses using Exponential Random Graph Models (ERGM), providing insights into how students interact and learn from each other through structured feedback.
Friedman, A., & Beasley, Z. (2024). Using Textual Analysis to Examine Student Engagement in Online Undergraduate Science Education. Journal of Statistics and Data Science Education.
This research employs textual analysis techniques to investigate patterns of student engagement in online undergraduate science courses, providing insights into how students interact with course materials and peer feedback in virtual learning environments.
Friedman, A. (2022). Visualizing protein data sets in R through a student peer-review rubric. Biochemistry and Molecular Biology Education.
This study presents a peer-review rubric designed for evaluating student-created protein visualizations in R, demonstrating how structured feedback mechanisms can enhance learning outcomes in bioinformatics and molecular biology education.
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(6), 9-17.
This research applies sentiment analysis to peer review feedback in visualization courses, revealing patterns in how students critique visualizations and offering pedagogical insights for improving visualization education.
Friedman, A. (2019). Toward peer-review software and a rubric application in visual analytics classes: A case study. Education for Information, Special issue: Visual Learning by Education for Information, 35(3), 243-263.
A case study examining the implementation of peer-review software and rubric-based evaluation in visual analytics education, demonstrating improved learning outcomes and student engagement through structured feedback mechanisms.
Friedman, A., & Schneider, E. (2018). Developing a Visualization Education Curriculum in the Age of Big Data Using the Dick and Carey Model. Visual Communication Quarterly, 25(4).
This paper presents a systematic approach to developing visualization education curricula using the Dick and Carey instructional design model, addressing the challenges of teaching data visualization in the era of big data and complex information systems.
Friedman, A. (2018). Digitizing Peircean semiotics to analyze students' peer-review comments: classroom use of an application. Social Semiotics, 38(5).
This research applies Peircean semiotic theory to the digital analysis of student peer-review comments, presenting a novel application that helps instructors understand the quality and nature of student feedback in visualization education contexts.