TY - GEN
T1 - Modeling Creativity in Visual Programming: From Theory to Practice.
AU - Kovalkov, Anastasia
AU - Paassen, Benjamin
AU - Segal, Avi
AU - Gal, Kobi
AU - Pinkwart, Niels
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021
Y1 - 2021
N2 - 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 creativity and applying the measure in a practical domain. The measure relies on core theoretical concepts in creativity theory, namely fluency, flexibility, and originality, We adapt the creativity measure for Scratch projects. We designed a machine learning model for predicting the creativity of Scratch projects, trained and evaluated on ratings collected from expert human raters. Our results show that the automatic creativity ratings achieved by the model aligned with the rankings of the projects of the expert raters more than the experts agreed with each other. This is a first step in providing computational models for describing creativity that can be applied to educational technologies, and to scale up the benefit of creativity education in schools. [For the full proceedings, see ED615472.]
AB - 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 creativity and applying the measure in a practical domain. The measure relies on core theoretical concepts in creativity theory, namely fluency, flexibility, and originality, We adapt the creativity measure for Scratch projects. We designed a machine learning model for predicting the creativity of Scratch projects, trained and evaluated on ratings collected from expert human raters. Our results show that the automatic creativity ratings achieved by the model aligned with the rankings of the projects of the expert raters more than the experts agreed with each other. This is a first step in providing computational models for describing creativity that can be applied to educational technologies, and to scale up the benefit of creativity education in schools. [For the full proceedings, see ED615472.]
M3 - Conference contribution
BT - International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM)
ER -