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'Virtual' experimentation on algorithm optimality

机译:算法优化的“虚拟”实验

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摘要

Learning programming in general, and algorithms in particular, demands to carry out a variety of practical activities, including experiments. In this paper, we summarize our instructional experience experimenting with algorithm optimality and we discuss the main issues raised. First, we introduce experimentation with algorithms. Afterwards, we briefly present the tools we developed for experimentation with optimality (GreedEx, GreedExCol and OptimEx) and we illustrate the kind of results that are expected by using a number of (exact and nonexact) greedy algorithms. We also describe our experiences in actual courses. Of special relevance are the students' difficulties and misconceptions we identified, as well as the interventions we performed to remove them. Finally, we relate these experiences with a number of relevant educational issues, namely learning goals, instructional methods, and how to address students' difficulties.
机译:通常,学习编程,尤其是学习算法,要求进行各种实践活动,包括实验。在本文中,我们总结了在算法优化方面进行实验的教学经验,并讨论了提出的主要问题。首先,我们介绍算法实验。随后,我们简要介绍了为进行最优性实验而开发的工具(GreedEx,GreedExCol和OptimEx),并说明了使用多种(精确和非精确)贪婪算法所期望的结果类型。我们还将描述我们在实际课程中的经验。与我们特别相关的是我们发现的学生的困难和误解,以及我们为消除这些困难而采取的干预措施。最后,我们将这些经验与许多相关的教育问题相关联,即学习目标,教学方法以及如何解决学生的困难。

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