  Promoting creativity is considered an important goal of education, but
creativity is notoriously hard to measure.In this paper, we make the journey
fromdefining a formal measure of creativity that is efficientlycomputable to
applying the measure in a practical domain. The measure is general and relies
on coretheoretical concepts in creativity theory, namely fluency, flexibility,
and originality, integratingwith 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.
