Report on Female Participation in Informatics degrees in Europe

TL;DR

This paper analyzes European data on female participation in Informatics degrees, exploring trends, socio-economic influences, and geographical variations to understand persistent gender gaps in the field.

Contribution

It introduces an enriched dataset combining education and economic data, and applies clustering analysis to identify patterns in female participation across Europe.

Findings

Identified correlations between female participation and economic/geographical factors.

Revealed variations in gender participation trends across different European regions.

Provided insights into socio-economic influences on female enrollment in Informatics.

Abstract

This study aims to enrich and leverage data from the Informatics Europe Higher Education (IEHE) data portal to extract and analyze trends in female participation in Informatics across Europe. The research examines the proportion of female students, first-year enrollments, and degrees awarded to women in the field. The issue of low female participation in Informatics has long been recognized as a persistent challenge and remains a critical area of scholarly inquiry. Furthermore, existing literature indicates that socio-economic factors can unpredictably influence female participation, complicating efforts to address the gender gap. The analysis focuses on participation data from research universities at various academic levels, including Bachelors, Masters, and PhD programs, and seeks to uncover potential correlations between female participation and geographical or economic zones. The dataset was first enriched by integrating additional information, such as each country's GDP and relevant geographical data, sourced from various online repositories. Subsequently, the data was cleaned to ensure consistency and eliminate incomplete time series. A final set of complete time series was selected for further analysis. We then used the data collected from the internet to assign countries to different clusters. Specifically, we employed Economic Zone, Geographical Area, and GDP quartile to cluster countries and compare their temporal trends both within and between clusters. We analyze the results for each classification and derive conclusions based on the available data.