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.
翻译:从预测学生辍学,到协助大学入学,以及推动MOOC的崛起,教学中日益普遍采用机器学习技术。鉴于这些新用途的迅速增长,迫切需要调查ML技术如何支持长期教育原则和目标。在这项工作中,我们利用与教育专家访谈的质量见解,揭示了这一复杂的景观。这些访谈包括了对ML教育(ML4Ed)论文的深入评价,这些论文在过去十年中在杰出应用ML会议中发表。我们的中心研究目标是批判性地审查这些论文所宣布或暗示的教育和社会目标如何与它们所处理的MOC问题相一致。这就是,技术问题的制定、目标、方法和对结果的解释在多大程度上支持长期的教育原则和目标。我们发现,在ML生活周期的两个部分存在着跨学科差距,而且特别突出:从教育目标中提出ML问题,将预测转化为干预。我们利用这些见解来提出一个扩大的ML生活周期,这也可能适用于ML研究的技术问题、目标、目标、方法和对结果的解释,并结合ML研究的当前社会研究,以及ML研究,以及ML研究中不断增长的社会研究。