GreedExCol, A CSCL tool for experimenting with greedy algorithms

Abstract

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;

Combining program visualization with programming workspace to assist students for completing programming laboratory task

“…GreedEx (Velázquez-Iturbide & Pérez-Carrasco, 2009) is an AT which lets learners to try and evaluate their own-defined greedy algorithm. This tool is extended to GreedExCol (Debdi, Paredes-Velasco & Velázquez-Iturbide, 2015) by incorporating collaborative learning. On the other, Complexitor (Elvina & Karnalim, 2017) lets learners to understand how algorithm time complexity is defined (by providing the implementation of target algorithm or source code).…”

Introducing a Practical Educational Tool for Correlating Algorithm Time Complexity with Real Program Execution

“…Algorithm-oriented educational tool puts more stress on algorithmic steps. At CS undergraduate major, it has been frequently used to cover complex topic such as data structures [21,22] and algorithm strategies [3,[23][24][25][26]. When perceived from how they focus on visualization, algorithm-oriented educational tools can be further classified into twofold: algorithm visualization and standard algorithm-oriented educational tool.…”

“…On the other hand, standard algorithm-oriented educational tool does not focus on visualization as its main feature. Several examples of such tool are GreedEx [26], GreedExCol [25], and Complexitor [3].…”

TASNOP: A tool for teaching algorithms to solve network optimization problems

“…In the literature there are numerous ICT tools for educational purposes. Recent proposals related to the content considered by TASNOP include: a system for supporting collaborative discussion on experimental results of optimality for greedy algorithms ; an educational software tool to support teaching of concepts related to the optimization field ; a mobile device based test tool for demonstrating the working of eight different optimization algorithms (genetic, artificial immune, differential evolution, particle swarm optimization, simulated annealing, tabu search, artificial bee colony, and ant colony) ; a visualization system to teach dynamic programming algorithms ; and a simulation tool to support teaching of concepts of urban traffic flow . However, it is important to emphasize that TASNOP differs from these and other known tools, especially regarding to the set of handled problems and algorithms.…”