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.
翻译:促进创造力被认为是教育的一个重要目标,但众所周知,创造性是难以衡量的。在本文中,我们从确定一种正式的创造性衡量标准出发,在实际领域应用该计量标准是有效的。该衡量标准是一般性的,依赖创造力理论中的核心理论概念,即流畅、灵活和独创性,与先前的认知科学文献相结合。我们调整了流行视觉编程语言Scratch项目的一般计量标准。我们设计了一个机器学习模型,用于预测Scratch项目的创造性,在广泛的用户研究中培训和评估人类专家的创造力评估。我们的结果显示,对Scratch的创造力的看法在专家中差异很大。自动的创造力评估与人类专家的评估比专家相互商定的要大。这是提供计算模型的第一步,用以衡量可应用于教育技术的创造力,并扩大学校创造性教育的效益。