Commonsense computing (episode 5): algorithm efficiency and balloon testing

Authors:
Robert McCartney;Dennis J. Bouvier;Tzu-Yi Chen;Gary Lewandowski;Kate Sanders;Beth Simon;Tammy VanDeGrift
Affiliations:
University of Connecticut, Storrs, CT, USA;Southern Illinois University Edwardsville, Edwardsville, IL, USA;Pomona College, Pomona, CA, USA;Xavier University, Cincinnati, OH, USA;Rhode Island College, Providence, RI, USA;University of California San Diego, La Jolla, CA, USA;University of Portland, Portland, OR, USA
Venue:
ICER '09 Proceedings of the fifth international workshop on Computing education research workshop
Year:
2009

Citing 7
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Abstract

This paper investigates what students understand about algorithm efficiency before receiving any formal instruction on the topic. We gave students a challenging search problem and two solutions, then asked them to identify the more efficient solution and to justify their choice. Many students did not use the standard worst-case analysis of algorithms; rather they chose other metrics, including average-case, better for more cases, better in all cases, one algorithm being more correct, and better for real-world scenarios. Students were much more likely to choose the correct algorithm when they were asked to trace the algorithms on specific examples; this was true even if they traced the algorithms incorrectly.