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In this Barrett Honors Thesis, I developed a model to quantify the complexity of Sankey diagrams, which are a type of visualization technique that shows flow between groups. To do this, I created a carefully controlled dataset of synthetic Sankey diagrams of varying sizes as study stimuli. Then, a pair of online crowdsourced user studies were conducted and analyzed. User performance for Sankey diagrams of varying size and features (number of groups, number of timesteps, and number of flow crossings) were algorithmically modeled as a formula to quantify the complexity of these diagrams. Model accuracy was measured based on the performance of users in the second crowdsourced study. The results of my experiment conclusively demonstrates that the algorithmic complexity formula I created closely models the visual complexity of the Sankey Diagrams in the dataset.
To do this, a 4-week long pilot curriculum was created, implemented, and tested through an optional class at I Am Zambia, available to women who had already graduated from the year-long I Am Zambia Academy program. A total of 18 women ages 18-24 chose to enroll in the course. There were a total of 10 lessons, taught over 20 class period. These lessons covered four main computational thinking frameworks: introduction to computational thinking, algorithmic thinking, pseudocode, and debugging. Knowledge retention was tested through the use of a CS educational tool, QuizIt, created by the CSI Lab of School of Computing, Informatics and Decision Systems Engineering at Arizona State University. Furthermore, pre and post tests were given to assess the successfulness of the curriculum in teaching students the aforementioned concepts. 14 of the 18 students successfully completed the pre and post test.
Limitations of this study and suggestions for how to improve this curriculum in order to extend it into a year long course are also presented at the conclusion of this paper.