Lost in Translation: Reimagining the Machine Learning Life Cycle in Education

TL;DR

This paper critically examines how machine learning in education aligns with educational principles, highlighting a gap in problem formulation and application translation, and proposes an extended ML life cycle to better integrate societal goals.

Contribution

It introduces an extended ML life cycle tailored for education, emphasizing the importance of aligning technical ML problems with educational and societal objectives.

Findings

Identifies a gap in problem formulation and interpretation in ML4Ed papers.

Highlights misalignment between ML technical goals and educational objectives.

Proposes an extended ML life cycle to improve integration of ML in education.

Abstract

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.