Görselleştirme Okuryazarlığı Çalışmalarında Değerlendirmenin Karşılaştırmalı Bir İncelemesi

Yıl 2023, , 391 - 399, 28.07.2023

Elif Emel Fırat

https://doi.org/10.21605/cukurovaumfd.1334205

Öz

Veri görselleştirme, karmaşık veri kümelerini kolayca anlaşılır görsel temsillere basitleştirerek veriler içindeki kalıpları ve ilişkileri belirlemeyi kolaylaştıran güçlü bir araçtır. Veri görselleştirmeyi tam olarak anlamak için bireylerin, görselleştirmeleri etkili bir şekilde anlama, yorumlama ve oluşturmayı içeren görsel okuryazarlık becerilerini geliştirmesi gerekir. Görselleştirme okuryazarlık becerilerini okuryazarlık testleri aracılığıyla değerlendirmek çok önemlidir ve eğitim araçları, rehberlik ve kaynaklar sağlayarak bu becerilerin geliştirilmesinde önemli bir rol oynar. Ancak okuryazarlık testlerinde veri toplama için uygun ortamların belirlenmesi ve eğitim araçlarının değerlendirilmesi karmaşık ve zahmetli bir iştir. Bu makale, okuryazarlık testlerini tanıtan, sınıf ve kitle kaynak deneyleri yoluyla Ağaç Haritası ve Paralel Koordinatlar Grafiği için pedagojik araçların etkinliğini değerlendiren iki okuryazarlık çalışmasına dayanan karşılaştırmalı bir analiz sunar. Analiz, veri toplama, çalışma süresi ve kaynakları, veri kalitesi, veri geçerliliği ve deneyler sırasında karşılaşılan zorluklar gibi temel faktörlere odaklanır. Bulgular, görselleştirme okuryazarlığı ve eğitim amaçları için deneysel ortamları seçerken belirli araştırma sorularını ve hedef popülasyonları dikkate almanın önemini vurgulamaktadır.

Anahtar Kelimeler

Veri görselleştirme, Görselleştirme okuryazarlığı, Sınıf tabanlı değerlendirme, Kitle kaynak kullanımlı değerlendirme

A Comparative Review of Evaluation in Visualization Literacy Studies

Öz

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.

Anahtar Kelimeler

Data visualization, Visualization literacy, Classroom-based evaluation, Crowdsourcing evaluation

Kaynakça

• 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. 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. 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. 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. 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.
• 6. Firat, E.E., Joshi, A., Laramee, R.S., 2022. Interactive Visualization Literacy: The State-of-the-Art. Information Visualization, 21(3), 285-310.
• 7. Bishop, F., Zagermann, J., Pfeil, U., Sanderson, G., Reiterer, H., Hinrichs, U., 2019. Construct-a-Vis: Exploring the Free-form Visualization Processes of Children. IEEE Transactions on Visualization and Computer Graphics, 26(1), 451-460.
• 8. Gäbler, J., Winkler, C., Lengyel, N., Aigner, W., Stoiber, C., Wallner, G., Kriglstein, S., 2019. Diagram Safari: A Visualization Literacy Game for Young Children. In Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, 389-396.
• 9. Alper, B., Riche, N.H., Chevalier, F., Boy, J., Sezgin, M., 2017. Visualization Literacy at Elementary School. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 5485-5497.
• 10. Fuchs, J., Isenberg, P., Bezerianos, A., Miller, M., Keim, D., 2019. Educlust- A Visualization Application for Teaching Clustering Algorithms. In Eurographics 2019-40th Annual Conference of the European Association for Computer Graphics, 1-8.
• 11. Krekhov, A., Michalski, M., Krüger, J., 2019. Integrating Visualization Literacy Into Computer Graphics Education using the Example of Dear Data. arXiv preprint arXiv:1907.04730.
• 12. Baker, R.S., Corbett, A.T., Koedinger, K.R., 2001. Toward a Model of Learning Data Representations. In Proceedings of the 23rd annual Conference of the Cognitive Science Society, 45-50.
• 13. Wang, Z., Sundin, L., Murray-Rust, D., Bach, B., 2020. Cheat Sheets for Data Visualization Techniques. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1-13.
• 14. Borgo, R., Micallef, L., Bach, B., McGee, F., Lee, B., 2018. Information Visualization Evaluation Using Crowdsourcing. In Computer Graphics Forum 37(3), 573-595.
• 15. Maltese, A.V., Harsh, J.A., Svetina, D., 2015. Data Visualization Literacy: Investigating Data Interpretation Along the Novice-Expert Continuum. Journal of College Science Teaching, 45(1), 84-90.
• 16. Lee, S., Kim, S.H., Hung, Y.H., Lam, H., Kang, Y.A., Yi, J.S., 2015. How Do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking. IEEE Transactions on Visualization and Computer Graphics, 22(1), 499-508.
• 17. Boy, J., Rensink, R.A., Bertini, E., Fekete, J.D. 2014. A Principled Way of Assessing Visualization Literacy. IEEE transactions on Visualization and Computer Graphics, 20(12), 1963-1972.
• 18. Lee, S., Kim, S.H., Kwon, B.C., 2016. Vlat: Development of a Visualization Literacy Assessment Test. IEEE Transactions on Visualization and Computer Graphics, 23(1), 551-560.
• 19. Firat, E., Denisova, A., Laramee, R., 2020. Treemap literacy: A Classroom-based Investigation. Eurographics Education, In Eurographics Proceedings, 29-38.
• 20. Firat, E.E., Denisova, A., Wilson, M.L., Laramee, R.S., 2022. P-Lite: A Study of Parallel Coordinate Plot Literacy. Visual Informatics, 6(3), 81-99.
• 21. Palan, S., Schitter, C., 2018. Prolific. Ac-A Subject Pool for Online Experiments. Journal of Behavioral and Experimental Finance, 17, 22-27.