Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

An Experimental Method for the Active Learning of Greedy Algorithms

An Experimental Method for the Active Learning of Greedy Algorithms An Experimental Method for the Active Learning of Greedy Algorithms ´ ´ J. ANGEL VELAZQUEZ-ITURBIDE, Universidad Rey Juan Carlos Greedy algorithms constitute an apparently simple algorithm design technique, but its learning goals are not simple to achieve. We present a didactic method aimed at promoting active learning of greedy algorithms. The method is focused on the concept of selection function, and is based on explicit learning goals. It mainly consists of an experimental method and the interactive system, GreedEx, that supports it. We also present our experience of five years using the didactic method and the evaluations we conducted to refine it, which are of two kinds: usability evaluations of GreedEx and analysis of students' reports. Usability evaluations revealed a number of opportunities of improvement for GreedEx, and the analysis of students' reports showed a number of misconceptions. We made use of these findings in several ways, mainly: improving GreedEx, elaborating lecture notes that address students' misconceptions, and adapting the class and lab sessions and materials. As a consequence of these actions, our didactic method currently satisfies its initial goals. The article has two main contributions. First, the didactic method itself can be valuable for computer science educators http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Computing Education (TOCE) Association for Computing Machinery

An Experimental Method for the Active Learning of Greedy Algorithms

Loading next page...
 
/lp/association-for-computing-machinery/an-experimental-method-for-the-active-learning-of-greedy-algorithms-lmkMZ0IXGC
Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1946-6226
DOI
10.1145/2534972
Publisher site
See Article on Publisher Site

Abstract

An Experimental Method for the Active Learning of Greedy Algorithms ´ ´ J. ANGEL VELAZQUEZ-ITURBIDE, Universidad Rey Juan Carlos Greedy algorithms constitute an apparently simple algorithm design technique, but its learning goals are not simple to achieve. We present a didactic method aimed at promoting active learning of greedy algorithms. The method is focused on the concept of selection function, and is based on explicit learning goals. It mainly consists of an experimental method and the interactive system, GreedEx, that supports it. We also present our experience of five years using the didactic method and the evaluations we conducted to refine it, which are of two kinds: usability evaluations of GreedEx and analysis of students' reports. Usability evaluations revealed a number of opportunities of improvement for GreedEx, and the analysis of students' reports showed a number of misconceptions. We made use of these findings in several ways, mainly: improving GreedEx, elaborating lecture notes that address students' misconceptions, and adapting the class and lab sessions and materials. As a consequence of these actions, our didactic method currently satisfies its initial goals. The article has two main contributions. First, the didactic method itself can be valuable for computer science educators

Journal

ACM Transactions on Computing Education (TOCE)Association for Computing Machinery

Published: Nov 1, 2013

There are no references for this article.