Automatic Creativity Measurement in Scratch Programs Across Modalities

Promoting creativity is considered an important goal of education, but\ncreativity is notoriously hard to measure.In this paper, we make the journey\nfromdefining a formal measure of creativity that is efficientlycomputable to\napplying the measure in a practical domain. The measure is general and relies\non coretheoretical concepts in creativity theory, namely fluency, flexibility,\nand originality, integratingwith prior cognitive science literature. We adapted\nthe general measure for projects in the popular visual programming language\nScratch.We designed a machine learning model for predicting the creativity of\nScratch projects, trained and evaluated on human expert creativity assessments\nin an extensive user study. Our results show that opinions about creativity in\nScratch varied widely across experts. The automatic creativity assessment\naligned with the assessment of the human experts more than the experts agreed\nwith each other. This is a first step in providing computational models for\nmeasuring creativity that can be applied to educational technologies, and to\nscale up the benefit of creativity education in schools.\n