Automatic Creativity Measurement in Scratch Programs Across Modalities

Anastasia Kovalkov, Benjamin Paasen, Avi Segal, Niels Pinkwart, Kobi Gal

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Promoting creativity is considered an important goal of education, but creativity is notoriously hard to measure. In this article, we make the journey from defining a formal measure of creativity, that is, efficiently computable to applying the measure in a practical domain. The measure is general and relies on core theoretical concepts in creativity theory, namely fluency, flexibility, and originality, integrating with prior cognitive science literature. We adapted the general measure for projects in the popular visual programming language Scratch. We designed a machine learning model for predicting the creativity of Scratch projects, trained and evaluated on human expert creativity assessments in an extensive user study. Our results show that opinions about creativity in Scratch varied widely across experts. The automatic creativity assessment aligned with the assessment of the human experts more than the experts agreed with each other. This is a first step in providing computational models for measuring creativity that can be applied to educational technologies, and to scale up the benefit of creativity education in schools.

Original languageEnglish
Pages (from-to)740-753
Number of pages14
JournalIEEE Transactions on Learning Technologies
Issue number6
StatePublished - 1 Dec 2021


  • Automatic assessment tools
  • computer science education
  • creativity
  • distances
  • scratch

ASJC Scopus subject areas

  • Education
  • Engineering (all)
  • Computer Science Applications


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