Towards a Fine-grained Analysis of Complexity of Programming Tasks

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.