Austin Cory Bart, Ryan Whitcomb, Dennis Kafura, Clifford A. Shaffer, and Eli Tilevich

Virginia Tech

Computing with
CORGIS: Diverse,
Real-world Datasets
for Introductory
Computing
REPRINT

From Proceedings of 2017 ACM SIGCSE Conference. Reprinted with permission

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 cross-disciplinary 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 CORGIS 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.

INTRODUCTION

As computing skills become required for an increasing number of disciplines, CS educators are meeting the demand by creating courses for non-CS majors. Students are drawn from many disciplines, including the sciences, arts, and humanities, and they have myriad, divergent career paths before them distinct from those of Computer Science majors. This different population needs a different approach, one that all students will perceive as authentic and beneficial, while simultaneously enabling problems of a computational nature [ 9].

We suggest that most students would benefit from computational techniques that empower them to manage the ev-er-growing amounts of data present in every field [ 14]. Therefore, a Data Science context, where students write algorithms to manipulate real-world data and interpret the results, could provide a compelling context. However, integrating data into