Towards a Fine-grained Analysis of Complexity of Programming Tasks

Rodrigo Duran
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Abstract

Bloom's and SOLO taxonomies have been used to describe the complexity of computer science tasks and student's outcome. However, using these taxonomies have coarse granularity and programming tasks with very different demands could be equally classified at the same level. My research proposes a new framework using Neo-Piagetian stages of development based on the Model of Hierarchical Complexity (MHC) that enable formal definition and fine-grained evaluation of programming tasks nuances in paradigms, languages, and constructs. By empirically validating the model, I expect it to be a valuable tool to provide best practices to develop pedagogical approaches and tools.
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面向编程任务复杂性的细粒度分析
Bloom和SOLO分类法被用来描述计算机科学任务的复杂性和学生的成果。然而,使用这些分类法具有粗粒度,并且具有非常不同需求的编程任务可以在同一级别上进行相同的分类。我的研究提出了一个基于分层复杂性模型(MHC)的新皮亚吉发展阶段的新框架,该框架能够对范式、语言和结构中的编程任务细微差别进行正式定义和细粒度评估。通过对模型的经验验证,我希望它能成为一个有价值的工具,为开发教学方法和工具提供最佳实践。
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