Modeling and Measuring High School Students’ Computational Thinking Practices in Science

Golnaz Arastoopour Irgens*, Sugat Dabholkar, Connor Bain, Philip Woods, Kevin Hall, Hillary Swanson, Michael Horn, Uri Wilensky

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Despite STEM education communities recognizing the importance of integrating computational thinking (CT) into high school curricula, computation still remains a separate area of study in K-12 contexts. In addition, much of the research on CT has focused on creating generally agreed-upon definitions and curricula, but few studies have empirically tested assessments or used contemporary learning sciences methods to do so. In this paper, we outline the implementation of an assessment approach for a 10-day high school biology unit with computational thinking activities that examines student pre-post responses as well as responses to embedded assessments throughout the unit. Using pre-post scores, we identified students with both positive and negative gains and examined how each group’s CT practices developed as they engaged with the curricular unit. Our results show that (1) students exhibited science and computational learning gains after engaging with a science unit with computational models and (2) that the use of embedded assessments and discourse analytics tools reveals how students think differently with computational tools throughout the unit.

Original languageEnglish (US)
Pages (from-to)137-161
Number of pages25
JournalJournal of Science Education and Technology
Issue number1
StatePublished - Feb 1 2020


  • Assessment
  • Biology
  • Computational thinking
  • Learning analytics
  • Science learning

ASJC Scopus subject areas

  • Education
  • Engineering(all)

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