Reimagining the machine learning life cycle to improve educational outcomes of students.

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 massive open online courses (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

SUBMITTER: Liu LT 

PROVIDER: S-EPMC9992853 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature