Human-Centred Analytics/AI in Education

Note: this page has been updated as these special issues were published.

A heads-up that three collections will hit the streets this year focused on how we can design so that human needs and values are well and truly centre-stage in educational tools powered by data, analytics and AI. It will be good to have detailed ‘insider accounts’ from researcher/developers who are reflecting deeply on how values are baked into their design practices and the infrastructures they are building, and how different stakeholders can engage meaningfully in shaping design. I’m excited about the papers shaping up for these volumes, so watch out for their releases mid- and end-2019…

Human-Centred Learning AnalyticsJournal of Learning Analytics, 6(2), pp. 1–94 (Eds.) Simon Buckingham Shum, Rebecca Ferguson, & Roberto Martinez-Maldonado

What’s the Problem with Learning Analytics? Journal of Learning Analytics, 6(3), pp. 5-42. (Ed.) Simon Buckingham Shum.

Diverse reflections on an article by Neil Selwyn, based on his provocative keynote address to the 2018 International Conference on Learning Analytics & Knowledge. Commentaries from Carolyn Rosé, Rebecca Ferguson, Paul Prinsloo & Alfred Essa. [Replay the keynote]

Buckingham Shum, S.J. & Luckin, R. (2019), Eds: Learning Analytics and AI: Politics, Pedagogy and PracticesBritish Journal of Educational Technology (50th Anniversary Special Issue), 50, (6), pp.2785-2973.

While there is a growing chorus of justifiably cautionary voices about the dark sides of data, algorithms and machine intelligence when used uncritically in education, sometimes these are from commentators some distance from the ‘nuts and bolts’. This issue will provide accounts from insiders, all of whom have agreed to engage with the theme of “Politics, Pedagogy and Practices”, whose dynamics play out at many organisational scales:

Practices: We are seeking informed accounts of how these technologies come into being — the social and material practices of designing analytics and AI educational tools, and the related practices of educators and other stakeholders needed to deploy these tools.

Pedagogy: For some critics, analytics and AI equate to adopting a retrograde pedagogy from the industrial era. Any mention of quantification, or machine intelligence, evokes connotations of behaviourism or instructivism. Contributions to this issue will question such simplistic assumptions, illustrating a range of pedagogies and associated outcomes.

Politics:From international educational datasets gathered by governments and corporations, to personal apps, in a broad sense politics infuse any socio-technical infrastructure, because it mediates values and power. How do the researchers and developers of these tools frame their work in relation to concerns around values, ethics, and societal impact?

This issue will be written for a broad audience, introducing what is or soon will be possible, and describing strategies for taking into account data/algorithm/AI ethics. Written also for seasoned researchers, it will synthesise and clarify contemporary debates, providing a reference point for both teaching, teacher development and research.

Dec. 2020 update:

Momentum has continued to build around HCLA, leading to the First International Workshop on HCLA next April at LAK2021.

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