Comparing the Perceptions of Middle and High School Students from Different Socioeconomic Status Backgrounds on Learning Machine Learning

Machine Learning (ML) is increasingly integral to modern life, requiring its introduction to young individuals across all socioeconomic status (SES) backgrounds. Various initiatives are emerging for the integration of ML into the education of young students including the development of curricula, courses and activities. And although first applications indicate positive findings with regard to the students’ learning, there still is a lack of research on the effect of such teaching efforts across students from different SES backgrounds. Therefore, this study investigates the impact of different SES backgrounds on the students' understanding and application of ML basic concepts through the application of the ML4ALL! course to 266 middle and high school students from different SES backgrounds. The findings reveal significant differences in students' understanding and explanation of ML concepts, indicating that this may be affected by their SES background. However, students' perceived ability to apply ML concepts and the perceived difficulty in learning ML were consistent across all groups. The results indicate that although the SES background may influence students' learning, it does not necessarily limit their perceived ability to engage with AI/ML concepts. The insights from this study may contribute to a better understanding of the perceptions of students from a low SES background regarding AI/ML learning and can assist in facilitating the development of inclusive, effective, and enjoyable educational approaches.