Abstract: Promoting creativity is considered an important goal of education, but creativity is notoriously hard to define and measure. In this paper, 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 adapt the general measure for Scratch projects. A machine learning model for predicting the creativity of Scratch projects was trained and evaluated on ratings collected from expert human raters.Our results show that the creativity ratings achieved by the model aligned with the rankings of the projects of the expertraters more than the experts agreed with each other. This isa first step in providing computational models for describing creativity that can be applied to educational technologies,and scaling up the benefits of creativity education to thousands of students.