TY - GEN
T1 - Map-based algorithm visualization with METAL highway data
AU - Teresco, James D.
AU - Fathi, Razieh
AU - Ziarek, Lukasz
AU - Bamundo, Maria Rose
AU - Pengu, Arjol
AU - Tarbay, Clarice F.
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/2/21
Y1 - 2018/2/21
N2 - We present the algorithm visualization capabilities of the METAL project. Using METAL's graph data which represents highway systems, a selection of interactive algorithm visualizations are performed. Progress of the algorithm is shown by changing the colors of the graph's vertices and/or edges overlaid on Google Maps and in color-coded tabular form, including contents of important data structures. Advantages include the real-world data set and the variety of data sizes available, enhancing student engagement. While many visualizations and visualization tools exist for graph and related algorithms, most focus on small, synthetic graphs. We describe our algorithm visualization capabilities, which include implementations of sequential search, graph traversals, Dijkstra's algorithm, and convex hulls. These can be executed on graphs ranging in size from a few vertices and edges to hundreds. We also present results of a survey of students who have used METAL's algorithm visualizations.
AB - We present the algorithm visualization capabilities of the METAL project. Using METAL's graph data which represents highway systems, a selection of interactive algorithm visualizations are performed. Progress of the algorithm is shown by changing the colors of the graph's vertices and/or edges overlaid on Google Maps and in color-coded tabular form, including contents of important data structures. Advantages include the real-world data set and the variety of data sizes available, enhancing student engagement. While many visualizations and visualization tools exist for graph and related algorithms, most focus on small, synthetic graphs. We describe our algorithm visualization capabilities, which include implementations of sequential search, graph traversals, Dijkstra's algorithm, and convex hulls. These can be executed on graphs ranging in size from a few vertices and edges to hundreds. We also present results of a survey of students who have used METAL's algorithm visualizations.
KW - Algorithm visualization
KW - Graph algorithms
KW - Pedagogical tools
UR - https://www.scopus.com/pages/publications/85046040377
U2 - 10.1145/3159450.3159583
DO - 10.1145/3159450.3159583
M3 - Conference contribution
AN - SCOPUS:85046040377
T3 - SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education
SP - 550
EP - 555
BT - SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education
PB - Association for Computing Machinery, Inc
T2 - 49th ACM Technical Symposium on Computer Science Education, SIGCSE 2018
Y2 - 21 February 2018 through 24 February 2018
ER -