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1. Seeing Eye to Eye? Comparing Faculty and Student Perceptions of Biomolecular Visualization Assessments

   https://doi.org/10.3390/educsci14010094  

   Beckham, Josh T.; Dries, Daniel R.; Hall, Bonnie L.; Mitton-Fry, Rachel M.; Engelman, Shelly; Burch, Charmita; Acevedo, Roderico; Mertz, Pamela S.; Vardar-Ulu, Didem; Agrawal, Swati; et al (January 2024, Education Sciences)

   Jodie Jenkinson, Susan Keen (Ed.)

   While visual literacy has been identified as a foundational skill in life science education, there are many challenges in teaching and assessing biomolecular visualization skills. Among these are the lack of consensus about what constitutes competence and limited understanding of student and instructor perceptions of visual literacy tasks. In this study, we administered a set of biomolecular visualization assessments, developed as part of the BioMolViz project, to both students and instructors at multiple institutions and compared their perceptions of task difficulty. We then analyzed our findings using a mixed-methods approach. Quantitative analysis was used to answer the following research questions: (1) Which assessment items exhibit statistically significant disparities or agreements in perceptions of difficulty between instructors and students? (2) Do these perceptions persist when controlling for race/ethnicity and gender? and (3) How does student perception of difficulty relate to performance? Qualitative analysis of open-ended comments was used to identify predominant themes related to visual problem solving. The results show that perceptions of difficulty significantly differ between students and instructors and that students’ performance is a significant predictor of their perception of difficulty. Overall, this study underscores the need to incorporate deliberate instruction in visualization into undergraduate life science curricula to improve student ability in this area. Accordingly, we offer recommendations to promote visual literacy skills in the classroom. 

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2. FairSpace: An Interactive Visualization System for Constructing Fair Consensus from Many Rankings

   https://doi.org/10.1111/cgf.70132  

   Shrestha, H; Cachel, K; Alkhathlan, M; Rundensteiner, E; Harrison, L (June 2025, Computer Graphics Forum)

   Abstract Decisions involving algorithmic rankings affect our lives in many ways, from product recommendations, receiving scholarships, to securing jobs. While tools have been developed for interactively constructing fair consensus rankings from a handful of rankings, addressing the more complex real‐world scenario— where diverse opinions are represented by a larger collection of rankings— remains a challenge. In this paper, we address these challenges by reformulating the exploration of rankings as a dimension reduction problem in a system called FairSpace. FairSpace provides new views, including Fair Divergence View and Cluster Views, by juxtaposing fairness metrics of different local and alternative global consensus rankings to aid ranking analysis tasks. We illustrate the effectiveness of FairSpace through a series of use cases, demonstrating via interactive workflows that users are empowered to create local consensuses by grouping rankings similar in their fairness or utility properties, followed by hierarchically aggregating local consensuses into a global consensus through direct manipulation. We discuss how FairSpace opens the possibility for advances in dimension reduction visualization to benefit the research area of supporting fair decision‐making in ranking based decision‐making contexts. Code, datasets and demo video available at:osf.io/d7cwk 

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3. Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

   https://doi.org/http://doi.ieeecomputersociety.org/10.1109/TVCG.2018.2865240  

   Moritz, Dominik; Wang, Chenglong; Nelson, Gregory L; Lin, Haiden; Smith, Adam M; Howe, Bill; Heer, Jeff (July 2018, IEEE transactions on visualization and computer graphics)

   There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments. 

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4. Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

   Moritz, Dominik; Wang, Chenglong; Nelson, Greg L.; Lin, Halden; Smith, Adam M.; Howe, Bill; Heer, Jeffrey (April 2019, InfoVis)

   There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments. 

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5. Leveraging Machine Learning to Analyze Semantic User Interactions in Visual Analytics

   https://doi.org/10.3390/info15060351  

   Jeong, Dong Hyun; Jeong, Bong Keun; Ji, Soo Yeon (June 2024, Information)

   In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users’ analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying ‘inconclusive’ decisions is 83%. Overall, the proposed approach improves the understanding of users’ analytical decisions and provides a robust method for evaluating user interactions in visualization tools. 

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