Several years ago we presented an experimental, discovery-learning approach to the active learning of greedy algorithms. This paper presents GreedEx, a visualization tool developed to support this didactic method. The paper states the design goals of GreedEx, makes explicit the major design decisions adopted, and describes its main characteristics in detail. It also describes the experience of use, the usability evaluations conducted, and the evolution of GreedEx in these years in response to the findings of the usability evaluations. Finally, the positive results obtained in an evaluation of educational effectiveness are shown. The paper has three main contributions. First, the GreedEx system itself is an innovative system for experimentation and discovery learning of greedy algorithms. Second, GreedEx is different from other visualization systems in its support to higher levels of learning, in particular evaluation tasks. Finally, GreedEx is an example of a medium-term research project, where an educational system was designed from explicit learning goals and was later refined in a user-centered design process involving instructors and students, before carrying out a successful evaluation of educational effectiveness.

Costs and benefits are recurrent issues that concern all the computing areas, including computer and software engineering. Mastery of optimization algorithms is essential in these fields, but their didactics hardly has received any attention. To fill this gap, the interactive system GreedEx was designed to support the active learning of greedy algorithms by means of experimentation. In this article we describe GreedExCol, a collaborative extension of GreedEx that complements its experimental phase with a discussion phase held by the students in each team. The contributions of the article are threefold. Firstly, we present GreedExCol, a CSCL system aimed at supporting collaborative discussion on experimental results of optimality for greedy algorithms. Secondly, GreedExCol was evaluated with respect to educational effectiveness, obtaining statistically significant improvements of the collaborative, experimental approach over an individual, experimental approach without the support of GreedExCol. Thirdly, GreedExCol was evaluated in the same two groups with respect to motivation, obtaining a statistically significant increase of implicit motivation for students in the experimental group. Overall, we present a medium-term effort for developing an innovative learning system and a comprehensive evaluation of its impact over the students. ß 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:790-804, 2015; View this article online at wileyonlinelibrary.com/journal/cae;

No abstract

This chapter advocates for an approach to constructing educational tools that consists in designing small systems aimed at achieving clear educational goals and evaluating them in actual teaching situations. The authors addressed this approach with a number of small systems. In this chapter, they describe their experience in the development, use, and evaluation of two educational systems: SRec and GreedEx. The former is a highly interactive program animation system of recursion, and the latter is an interactive assistant aimed at learning the role of selection functions in greedy algorithms by means of experimentation. The evaluations allowed the authors to identify faults and weaknesses of the systems, and these results were used to enhance the systems. Moreover, their approach has yielded very high values with respect to effectiveness and student satisfaction.

This chapter advocates for an approach to constructing educational tools that consists in designing small systems aimed at achieving clear educational goals and evaluating them in actual teaching situations. The authors addressed this approach with a number of small systems. In this chapter, they describe their experience in the development, use, and evaluation of two educational systems: SRec and GreedEx. The former is a highly interactive program animation system of recursion, and the latter is an interactive assistant aimed at learning the role of selection functions in greedy algorithms by means of experimentation. The evaluations allowed the authors to identify faults and weaknesses of the systems, and these results were used to enhance the systems. Moreover, their approach has yielded very high values with respect to effectiveness and student satisfaction.