Mapping an emergent field of ‘computational education policy’: Policy rationalities, prediction and data in the age of Artificial Intelligence

Gulson, Kalervo N.; Webb, P. Taylor

doi:10.1177/0034523717723385

Cited by 42 publications

(20 citation statements)

References 34 publications

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“…The promises of granular and socio-emotional personalisation (Williamson, 2019), and machinic neutrality and objectivity in educational assessment (Perrotta & Selwyn, 2019) are not just imported from other fields where datafication is pervasive; they have been adapted to the specificities of education. This adaptation reflects a distinctive trajectory towards a form of 'computational rationality' that frames educational problems in terms of calculability and prediction (Gulson & Webb, 2017). We also suggest that the introduction of Google (and its parent company Alphabet), with its search and advertising business model, into education entails a different platform logic than other educational platforms such as learning management systems.…”

mentioning

confidence: 85%

Automation, APIs and the distributed labour of platform pedagogies in Google Classroom

Perrotta

Gulson

Williamson

et al. 2020

Critical Studies in Education

117

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mentioning

confidence: 85%

Automation, APIs and the distributed labour of platform pedagogies in Google Classroom

Perrotta

Gulson

Williamson

et al. 2020

Critical Studies in Education

117

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“…Yine başka bir araştırmada öğrenmeyi iyileştirmek ve başarı farkını azaltmak için, yükseköğretim kurumlarına sistematik hükümet yatırımlarının; kapasite geliştirme, eğitimcilerin bilgi ve becerilerini geliştirmeye odaklanması durumunda da daha etkili olacağını göstermiştir (Darling-Hammond, 2014). Zira, yapay zekânın yükseköğretim politikası ve yönetişimi üzerindeki etkisi üzerine yapılan çalışmaların daha az incelendiği görülmektedir (Gulson ve Webb, 2017).…”

Section: Tartışma Ve Sonuçunclassified

Eğitimde Yeni Bir Paradigma: “Yükseköğretimde Yapay Zekâ”

Taşçi¹,

Çelebi²

2020

OPUS Uluslararası Toplum Araştırmaları Dergisi

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The developments in technology in recent years have deeply affected societies. Artificial intelligence, one of these developments, has also created a revolution in human life. One of the areas that artificial intelligence affects is higher education. Artificial intelligence, which emerged as a result of the digital revolution, is a challenge to higher education. In this respect, how ready are higher education institutions for artificial intelligence? What revolution can artificial intelligence, an output of the digital age, create in higher education institutions? How ready are higher education leaders mentally and strategically for artificial intelligence? What are the ethical limitations of artificial intelligence in higher education? What are the benefits and challenges of artificial intelligence in improving student success, facilitating student enrollment, and better management of higher education resources? questions are a new paradigm in higher education. In the perspective of these questions, this study suggests that artificial intelligence can strengthen universities when used correctly in higher education. However, to realize this thesis, higher education institutions need to reassess and transform themselves in order to continue their mission of research, education and society.Nowadays,many universities strive to devote time and develop themselves to teach their students the skills and knowledge needed to provide the best possible education. Morever, it is especially important for educators and especially higher education leaders and faculty members to understand how artificial intelligence can support educators. In this respect, in this study, questions the role of artificial intelligence in higher education, as well as the applications of artificial intelligence in higher education, and discusses how optimistic institutions of higher education institutions can prepare for future artificial intelligence

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“…Combining and analysing these different types of data is made possible by the introduction of machine learning (e.g. Gulson and Webb, 2017). Indeed, the study of data infrastructure will very soon demand, if it does not already, close engagement with the development and use of Artificial Intelligence in education policy and practice.…”

Section: Conclusion: Power Topologies and Extra-statecraftmentioning

confidence: 99%

Emerging data infrastructures and the new topologies of education policy

Gulson

Sellar

2018

Environ Plan D

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This paper examines how datafication is creating new topologies of education policy. Specifically, we analyse how the creation of data infrastructures that enable the generation, communication and representation of digital data are changing relations of power, including both centralised and dispersed forms, and space in education. The paper uses conceptual resources from cultural topology and infrastructure studies to provide a framework for analysing spatial relations between educational data, discourses, policies and practices, in new governance configurations. The paper outlines a case study of an emergent data infrastructure in Australian schooling, the National Schools Interoperability Program, to provide empirical evidence of the movement, connection and enactment of digital data across policy spaces. Key aspects of this case include the ways that data infrastructure is: i) enabling new private and public connections across policy topologies; ii) creating a new role for technical standards in education policy; and, iii) changing the topological spaces of education governance. 2 Emerging data infrastructures and the new topologies of education policy This paper examines how datafication is creating new topologies of education policy. We aim to provide new theorisations and empirical evidence to understand how the creation of data infrastructures, which enable the generation, communication and representation of digital data, is contributing to the emergence of digital educational governance (Williamson, 2016) and changing relations of power and space in education. We aim to engage in conversation with two key literatures. The first is the literature on critical geographies of education (Pini, Gulson, Kraftl & Duffy-Jones, 2017; Nguyen, Cohen & Huff, 2017), or more specifically the geographies of education policy (e.g., Basu, 2007). This work has primarily focused on the ways in which education policy performs a spatial ordering of organisations, such as schools, and especially the connections between policy, schools and urban change (Butler, Hamnett & Ramsden, 2013; Gulson, 2011). Relational notions of space and place, including mobility (e.g., Cohen, 2017), have informed these inquiries. In this paper, we build on this work by utilising topological theorisations of space (Martin and Secor, 2014) that have recently been introduced into studies of education policy and data in education (e.g., Lewis & Hardy, 2017; Thompson & Cook, 2014). The second literature is that which makes new and novel connections between public policy, infrastructure and governance (Sage et al., 2015; Prince, 2016), with a focus on data. Lawn (2013a: 8) argues that 'the creation and flow of data has become a powerful governing tool in education,' in a similar manner to the focus on calculation, metrics and benchmarking in urban policy (e.g., McCann, 2008). In education producing the right second order objectives (numbers) has become as important as first order objectives (pedagogy, curriculum, assessment) in new performative accountabil...

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Mapping an emergent field of ‘computational education policy’: Policy rationalities, prediction and data in the age of Artificial Intelligence

Cited by 42 publications

References 34 publications

Automation, APIs and the distributed labour of platform pedagogies in Google Classroom

Automation, APIs and the distributed labour of platform pedagogies in Google Classroom

Eğitimde Yeni Bir Paradigma: “Yükseköğretimde Yapay Zekâ”

Emerging data infrastructures and the new topologies of education policy

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