Exploring the Relationship between Learning of Machine Learning Concepts and Socioeconomic Status Background among Middle and High School Students: A Comparative Analysis

Abstract

As Machine Learning (ML) becomes increasingly integrated into our daily lives, it is essential to teach ML to young people from an early age including also students from a low socioeconomic status (SES) background. Yet, despite emerging initiatives for ML instruction in K-12, there is limited information available on the learning of students from a low SES background. To address this gap, our study conducted an analysis of comparing the students' performance assessment scores of ML concepts as a result of the ML4ALL! course among 266 middle and high school students from different socioeconomic backgrounds. The results demonstrated an understanding of ML concepts among students from all SES backgrounds. Although some differences were observed regarding specific parts of the ML development process, these were not substantial enough to identify SES as a determining factor affecting the performance assessment score. Also, when considering the background together with other demographic factors such as sex assigned at birth or educational stage, no significant difference of the students' performance assessment scores was observed. These findings provide a first indication that a low SES background must not be a barrier to ML competencies and that effective and inclusive ML teaching strategies can ensure equitable access to ML education across diverse socioeconomic backgrounds.