Data-driven Analysis of Gender Differences and Similarities in Scratch Programs

TL;DR

This study analyzes gender differences in Scratch programming among children aged 8-10, revealing distinct topic preferences and programming styles that can inform more effective, inclusive computer science education.

Contribution

It introduces a novel combination of topic modeling and automated code analysis to explore gender-specific programming behaviors in Scratch.

Findings

Girls prefer unicorns, dancing, music, and create simpler programs.

Boys favor gloomy themes, sports, and develop more complex code with loops and conditionals.

Gender differences in topics and programming complexity can influence learning outcomes.

Abstract

Block-based programming environments such as Scratch are an essential entry point to computer science. In order to create an effective learning environment that has the potential to address the gender imbalance in computer science, it is essential to better understand gender-specific differences in how children use such programming environments. In this paper, we explore gender differences and similarities in Scratch programs along two dimensions: In order to understand what motivates girls and boys to use Scratch, we apply a topic analysis using unsupervised machine learning for the first time on Scratch programs, using a dataset of 317 programs created by girls and boys in the range of 8-10 years. In order to understand how they program for these topics, we apply automated program analysis on the code implemented in these projects. We find that, in-line with common stereotypes, girls prefer topics that revolve around unicorns, celebrating, dancing and music, while boys tend to prefer gloomy topics with bats and ghouls, or competitive ones such as soccer or basketball. Girls prefer animations and stories, resulting in simpler control structures, while boys create games with more loops and conditional statements, resulting in more complex programs. Considering these differences can help to improve the learning outcomes and the resulting computing-related self-concepts, which are prerequisites for developing a longer-term interest in computer science.