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Ramon Mayor Martins; Christiane G. Von Wangenheim; Marcelo F. Rauber; Adriano F. Borgatto; Jean C. R. Hauck – ACM Transactions on Computing Education, 2024
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…
Descriptors: Artificial Intelligence, Computer Science Education, Socioeconomic Status, Correlation
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Ramon Mayor Martins; Christiane Gresse Von Wangenheim; Marcelo Fernando Rauber; Jean Carlo Rossa Hauck; Melissa Figueiredo Silvestre – Informatics in Education, 2024
Knowledge about Machine Learning (ML) is becoming essential, yet it remains a restricted privilege that may not be available to students from a low socio-economic status background. Thus, in order to provide equal opportunities, we taught ML concepts and applications to 158 middle and high school students from a low socio-economic background in…
Descriptors: Middle School Students, High School Students, Low Income Students, Socioeconomic Status
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Ramon Mayor Martins; Christiane Gresse Von Wangenheim – Informatics in Education, 2024
Information technology (IT) is transforming the world. Therefore, exposing students to computing at an early age is important. And, although computing is being introduced into schools, students from a low socio-economic status background still do not have such an opportunity. Furthermore, existing computing programs may need to be adjusted in…
Descriptors: Information Technology, Socioeconomic Status, Social Class, Computer Literacy