Prof. Kobi Gal

This paper explores the use of data analytics for identifying creativity in visual programming. Visual programming environments are increasingly included in the schools curriculum. Their potential for promoting creative thinking in students is an important factor in their adoption. However, there does not exist a standard approach for detecting creativity in students' programming behavior, and analyzing programs manually requires human expertise and is time consuming. This work provides a computational tool for measuring creativity in visual programming that combines theory from the literature with data mining approaches. It adapts classical dimensions of creative processes to our setting, and considers new aspects such as visual elements of the visual programming projects. We apply our approach to the Scratch programming environment, measuring the creativity score of hundreds of projects. We show a preliminary comparison between our metrics and teacher ratings.