Learning Analytics and AI: Politics, Pedagogy and Practices
By Simon Buckingham-Shum & Rose Luckin
Available via the British Educational Research Association (https://doi.org/10.1111/bjet.12880)
The following is an extract from the front page of this publication.
Politics, pedagogy and practices
This special issue provides resources to tackle this challenge, by engaging with these concerns under the banner of three themes: Politics, Pedagogy and Practices:
- The politics theme acknowledges the widespread anxiety about the ways that data, algorithms and machine intelligence are being, or could be, used in education. From international educational datasets gathered by governments and corporations, to personal apps, in a broad sense ‘politics’ infuse all information infrastructures, because they embody values and redistribute power. While applauding the contributions that science and technology studies, critical data studies and related fields are making to contemporary debates around the ethics of big data and AI, we wanted to ask, how do the researchers and developers of LA/AI tools frame their work in relation to these concerns?
- The pedagogies theme addresses the critique from some quarters that LA/AI’s requirements to formally model skills and quantify learning processes serve to perpetuate instructivist pedagogies (eg, Wilson & Scott, 2017), branded somewhat provocatively as behaviourism (Watters, 2015). While there has clearly been huge progress in STEM-based intelligent tutoring systems (see du Boulay, 2019; Rosé, McLaughlin, Liu, & Koedinger, 2019), what is the counter-argument that LA/AI empowers more diverse pedagogies?
- The practices theme sought accounts of how these technologies come into being. What design practices does one find inside LA/AI teams that engage with the above concerns? Moreover, once these tools have been deployed, what practices do educators use to orchestrate these tools in their teaching?
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