Incorporating visualization research in introductory programming course: case studies
Author: 
Sunghee KimAuthors Info & Claims
Abstract
The importance of early research experience for undergraduate students has been stressed time and time again. This paper presents three case studies in which non-CS major students could gain a visualization research experience in their first programming course. In all case studies, students were given real climate data to visualize. In the first case study, students visualized spatial correlation between two variables (weather conditions) on a map so that viewers could infer areas in which the two variables were highly correlated in a positive or negative way, or areas with little to no correlation. In the second and third case studies, students generated single variable visualization and multidimensional visualization of two or four variables. In each of the three case studies the students were led through the process of understanding data, exploring different representations, and designing and implementing an agreed-upon visual representation. Increased number of students decided to take the next course in Computer Science compared to previous years without a research project. Feedback from the students suggests that they enjoyed using data they could understand and found the process and the final product rewarding and applicable to projects in their major and courses.
References
[1]
{BHW06} Bair A., House D., Ware C.: Texturing of layered surfaces for optimal viewing. Transactions on Visualization and Computer Graphics 12, 5 (Sept. 2006), 1125--1132. 7
[2]
{Che73} Chernoff H.: The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association 68, 342 (June 1973), 361--368. 5
[3]
{CLKH14} Chung D., Laramee R., Kehrer J., Hauser H.: Glyph-based Multi-field Visualization. Springer, 2014. C.D. Hansen and M. Chen and C.R. Johnson and A.E. Kaufman and H. Hagen. 7
[4]
{CMS99} Card S., Mackinlay J., Shneiderman B.: Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann, 1999. 7
[5]
{Gah98} Gahegan M.: Visualization techniques for exploratory spatial analysis. Computers, Environment and Urban Systems 1 (1998), 43--56. 7
[6]
{HSKTH07} Hagh-Shenas H., Kim S., Tateosian L., Healey C.: Weaving vs. blending: A quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. Transactions on Visualization and Computer Graphics 13, 6 (Nov. 2007), 1270--1279. 3
[7]
{KHI*03} Kosara R., Healey C., Interrante V., Laidlaw D., Ware C.: Thoughts on user studies: Why, how, and when. Computer Graphics and Applications 23, 4 (2003), 20--25. 8
[8]
{Lop07} Lopatto D.: Undergraduate research experiences support science career decisions and active learning. CBE Life Science Education 6, 4 (2007), 297--306. 1
[9]
{MDB87} McCormick B., DeFanti T., Brown M.: Definition of visualization. Siggraph 21, 6 (Nov. 1987), 3. 2
[10]
{Mil07} Miller J.: Attribute blocks: Visualizing multiple continuously defined attributes. Computer Graphics and Applications 27, 3 (May 2007), 57--69. 3
[11]
{ML17} McNabb L., Laramee R.: Survey of surveys (sos) - mapping the landscape of survey papers in information visualization. Computer Graphics Forum 36, 3 (June 2017), 589--617. 8
[12]
{WB97} Whitley K., Blackwell A.: Visual programming: The outlook from academia and industry. In Proc. Workshop on Empirical Studies of Programmers '97 (1997), pp. 180--208. 2
[13]
{Web07} Webb S.: The importance of undergraduate research. Science (July 2007). 1
[14]
{WH01} Walter J., Healey C.: Attribute preserving dataset simplification. In Proc. Visualization '01 (2001), pp. 113--120. 7, 8
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Eurographics Association
Goslar, Germany
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Published: 16 April 2018
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