An Experimental Method for the Active Learning of Greedy Algorithms

Abstract

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 in their teaching of algorithms. Secondly, the refinement process we have carried out, which was a multifaceted, medium-term action research, can be of interest to researchers of technology-supported computing education, since it illustrates how the didactic method was integrated into our educational practice.