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Deep learning goes to school: toward a relational understanding of AI in education
Learning, Media and Technology ( IF 7.586 ) Pub Date : 2019-11-06 , DOI: 10.1080/17439884.2020.1686017
Carlo Perrotta 1 , Neil Selwyn 1
Affiliation  

ABSTRACT

In Applied AI, or ‘machine learning’, methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning environments, such as Khan Academy and the ASSISTments intelligent tutoring system. Drawing on Science and Technology Studies (STS), we provide a detailed examination of the scholarly work carried out by several data scientists around the use of ‘deep learning’ to predict aspects of educational performance. This approach draws attention to relations between various (problematic) units of analysis: flawed data, partially incomprehensible computational methods, narrow forms of educational’ knowledge baked into the online environments, and a reductionist discourse of data science with evident economic ramifications. These relations can be framed ethnographically as a ‘controversy’ that casts doubts on AI as an objective scientific endeavour, whilst illuminating the confusions, the disagreements and the economic interests that surround its implementations.



中文翻译:

深度学习上学:对教育中对AI的关系理解

摘要

在应用AI或“机器学习”中,诸如神经网络之类的方法用于训练计算机执行任务而无需人工干预。在本文中,我们质疑这些方法在教育中的适用性。特别是,我们考虑了数据科学家最近尝试将AI元素添加到少数在线学习环境中的案例,例如Khan Academy和ASSISTments智能辅导系统。借助科学技术研究(STS),我们对一些数据科学家围绕“深度学习”的使用来预测教育表现的研究成果进行了详细的研究。这种方法引起人们对各种(问题)分析单元之间关系的关注:有缺陷的数据,部分无法理解的计算方法,狭义的教育知识形式渗透到了在线环境中,而数据科学的还原论话语则带来了明显的经济后果。这些关系可以在人种学上被描述为“争议”,​​从而对AI作为一种客观科学努力提出质疑,同时阐明了围绕其实现的困惑,分歧和经济利益。

更新日期:2019-11-06
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