LAUSR

Significance Given the rapid proliferation of machine learning technologies in education, we investigate the extent to which development of Machine learning (ML) technologies supports holistic education principles and goals. We present findings from a cross-disciplinary interview study of education researchers, examining whether and how the stated or implied educational objectives of ML4Ed research papers are aligned with the ML problem formulation, objectives, and interpretation of results. Our findings shed light on two translational challenges: 1) integrating educational goals into the formulation of technical ML problems and 2) translating predictions by ML methods to real-world interventions. We use these insights to propose an extended ML lifecycle, which may apply to the use of ML in other domains.