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Pattern recognition in programming to support the evaluation of Computational Thinking

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Computational thinking is a high-level skill that involves both critical and creative thinking. This research proposes an advanced method for analyzing source code to assess computational thinking skills in elementary school students. We present a large-scale approach that examines source code from various programming exercises using a generalized abstract syntax tree, enabling language-independent analysis. Clustering techniques are applied to identify different levels of computational thinking development in areas such as parallelism, data representation, abstraction and decomposition, control flow, and programming structure. The results of the automated assessment provide insights into students' computational thinking abilities across diverse populations, highlighting opportunities for improving the educational framework.

Original languageEnglish
Title of host publicationEDUNINE 2025 - 9th IEEE Engineering Education World Conference
Subtitle of host publicationEducation in the Age of Generative AI: Embracing Digital Transformation - Proceedings
EditorsClaudio da Rocha Brito, Melany M. Ciampi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331542788
DOIs
StatePublished - 2025
Event9th IEEE Engineering Education World Conference, EDUNINE 2025 - Montevideo, Uruguay
Duration: 23 Mar 202526 Mar 2025

Publication series

NameEDUNINE 2025 - 9th IEEE Engineering Education World Conference: Education in the Age of Generative AI: Embracing Digital Transformation - Proceedings

Conference

Conference9th IEEE Engineering Education World Conference, EDUNINE 2025
Country/TerritoryUruguay
CityMontevideo
Period23/03/2526/03/25

Keywords

  • Computational thinking assessment
  • clustering techniques
  • code analysis
  • generic abstract syntax tree
  • pattern recognition
  • software metrics

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