Affiliation:
1. CUKUROVA UNIVERSITY, FACULTY OF ENGINEERING, DEPARTMENT OF COMPUTER ENGINEERING, COMPUTER ENGINEERING PR.
Abstract
Data visualization is a powerful tool that simplifies complex datasets into easily comprehensible visual representations, making it easier to identify patterns and relationships within the data. To fully understand data visualization, individuals must develop visual literacy skills, which entails effectively understanding, interpreting, and creating visualizations. Assessing visualization literacy skills through literacy tests is crucial, and educational tools play a significant role in advancing these skills by providing guidance and resources. However, determining the suitable settings for data collection in literacy tests and evaluating educational tools is a complex and demanding task. This paper presents a comparative analysis based on two literacy studies introducing literacy tests and evaluating the effectiveness of pedagogical tools for Treemap and Parallel Coordinates Plot (PCP) through classroom and crowdsourcing experiments. The analysis focuses on key factors, including data collection, study time and resources, data quality, data validity, and challenges encountered during experiments. The findings underscore the significance of considering specific research questions and target populations when selecting experimental settings for visualization literacy and educational purposes.
Publisher
Cukurova Universitesi Muhendislik-Mimarlik Fakultesi Dergisi
Reference21 articles.
1. 1. Sadiku, M., Shadare, A.E., Musa, S.M., Akujuobi, C.M., Perry, R., 2016. Data Visualization. International Journal of Engineering Research and Advanced Technology (IJERAT), 2(12), 11-16.
2. 2. Ruchikachorn, P., Mueller, K., 2015. Learning Visualizations by Analogy: Promoting Visual Literacy Through Visualization Morphing. IEEE Transactions on Visualization and Computer Graphics, 21(9), 1028-1044.
3. 3. Kwon, B.C., Lee, B., 2016. A Comparative Evaluation on Online Learning Approaches Using Parallel COordinate Visualization. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 993-997.
4. 4. Heer, J., Bostock, M., 2010. Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 203-212.
5. 5. Fırat, E.E., Laramee, R.S., 2018. Towards a Survey of Interactive Visualization for Education. EG UK Computer Graphics and Visual Computing, Eurographics Proceedings, 91-101.