@inproceedings{a771c2cf83df48d69fbda56dcde3f12e,
title = "Modeling Creativity in Visual Programming: From Theory to Practice",
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 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.",
keywords = "Creativity, Creativity Tests, Visual Programming Environments",
author = "Anastasia Kovalkov and Benjamin Paa{\ss}en and Avi Segal and Kobi Gal and Niels Pinkwart",
note = "Publisher Copyright: {\textcopyright} EDM 2021.All rights reserved.; 14th International Conference on Educational Data Mining, EDM 2023 ; Conference date: 29-06-2021 Through 02-07-2021",
year = "2021",
month = jan,
day = "1",
language = "American English",
series = "Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021",
publisher = "International Educational Data Mining Society",
pages = "518--524",
editor = "I-Han Hsiao and Shaghayegh Sahebi and Francois Bouchet and Jill-Jenn Vie",
booktitle = "Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021",
address = "United States",
}