The article discusses the challenges of integrating machine learning (ML) in education, such as issues of fairness, transparency, and data privacy. A root cause of these issues is the lack of understanding of the complex dynamics of education. To overcome these challenges, the authors suggest that software practitioners should work closely with educators and students to understand the context of the data and define the ML data specifications. The study involved co-design sessions with ML software practitioners, educators, and students. The findings reveal that beyond a seat at the table, meaningful stakeholder participation in ML requires structured supports such as defined processes for continuous iteration, shared data quality standards, and information scaffolds for both technical and non-technical stakeholders.
Publication date: 9 Nov 2023
Project Page: https://doi.org/XXXXXXX.XXXXXXX
Paper: https://arxiv.org/pdf/2311.05792
