Computing with CORGIS

Abstract

To successfully bring introductory computing to non-CS majors, one needs to create a curriculum that will appeal to students from diverse disciplines. Several educational theories emphasize the need for introductory contexts that align with students' long-term goals and are perceived as useful. Data Science, using algorithms to manipulate real-world data and interpreting the results, has emerged as a field with crossdisciplinary value, and has strong potential as an appealing context for introductory computing courses. However, it is not easy to find, clean, and integrate datasets that will satisfy a broad variety of learners. The CORGIS project (https://think.cs.vt.edu/corgis) enables instructors to easily incorporate data science into their classroom. Specifically, it provides over 40 datasets in areas including history, politics, medicine, and education. Additionally, the COR-GIS infrastructure supports the integration of new datasets with simple libraries for Java, Python, and Racket, thus empowering introductory students to write programs that manipulate real data. Finally, the CORGIS web-based tools allow learners to visualize and explore datasets without programming, enabling data science lessons on day one. We have incorporated CORGIS assignments into an introductory course for non-majors to study their impact on learners' motivation, with positive initial results. These results indicate that external adopters are likely to find the CORGIS tools and materials useful in their own pedagogical pursuits.

Persist: Persistent and Reusable Interactions in Computational Notebooks

“…The study employed a within-subject design using two datasets: records about avalanches from the Utah Avalanche Center [4] and Video Games sales data from the Corgis Dataset Project [16].…”

EL Greco Platform: A novel Python programming learning platform that uses a real robot

“…One of them is BlockPy [6], which is dedicated to Python and presents many interesting features. Among them are guided feedback on the code output, integration of CORGIS [7], generation of line plots, scatter plots, and histograms. It can also convert written Python code to Blockly blocks through a Python to Blocks translation technique [8].…”

AI and Parallelism in CS1: Experiences and Analysis

“…For example, Anderson et al (2015) The Thirty-Seventh AAAI Conference on Artificial Intelligence use much interdisciplinary data from science, engineering, business, and the humanities in their CS1. Bart et al (2017) also describe students' enjoyment of a significant data focus in introductory programming. That work also describes the CORGIS project in which dozens of datasets are provided pre-cleaned, to facilitate incorporation into CS1.…”