With the rapid proliferation of machine learning technologies in the education sphere, we address an urgent need to investigate whether the development of these machine learning technologies supports holistic education principles and goals. We present findings from a cross-disciplinary interview study of education researchers, investigating whether the stated or implied "social good" objectives of ML4Ed research papers are aligned with the ML problem formulation, objectives, and interpretation of results. Our findings shed light on two main alignment gaps: the formulation of an ML problem from education goals and the translation predictions to interventions.

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