In this poster we present BRIDGES, a software infrastructure for programming assignments in data structures and algorithms courses, that has been in use at multiple institutions over the past 2 years. BRIDGES was developed to engage students at the sophomore level in critical foundational courses, to improve retention and reduce attrition rates. BRIDGES provides two key capabilities: (1) easy to use interface to real world datasets spanning social networks, entertainment (movies on IMDB, song lyrics), scientific data (real-time USGIS Earthquake Data), civic issues (crime data), and literature (books); and (2) a visualization of the acquired data can be used inmore »
BRIDGES: A System to Enable Creation of Engaging Data Structures Assignments with Real-World Data and Visualizations
Although undergraduate enrollment in Computer Science has remained strong and seen substantial increases in the past decade, retention of majors remains a significant concern, particularly for students at the freshman and sophomore level that are tackling foundational courses on algorithms and data structures. In this work, we present BRIDGES, a software infrastructure designed to enable the creation of more engaging assignments in introductory data structures courses by providing students with a simplified API that allows them to populate their own data structure implementations with live, real-world, and interesting data sets, such as those from popular social networks (e.g., Twitter, Facebook). BRIDGES also provides the ability for students to create and explore {\em visualizations} of the execution of the data structures that they construct in their course assignments, which can promote better understanding of the data structure and its underlying algorithms; these visualizations can be easily shared via a weblink with peers, family, and instructional staff. In this paper, we present the BRIDGES system, its design, architecture and its use in our data structures course over two semesters.
- Award ID(s):
- 1245841
- Publication Date:
- NSF-PAR ID:
- 10074263
- Journal Name:
- ACM SIGCSE 2016
- Page Range or eLocation-ID:
- 18 to 23
- Sponsoring Org:
- National Science Foundation
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