In this chapter, we challenge an increase in the uncritical application of algorithmic processes for providing automatically generated feedback for students, within a neoliberal framing of contemporary higher education. Initially, we discuss our concerns alongside networked learning principles, which developed as a critical pedagogical response to new online learning programmes and platforms. These principles now overlap too, with the notion that we are living in ‘postdigital’ times, where automatically generated feedback never stands alone, but is contested and supplemented by physical encounters and human feedback. First, we make observations on the e-marking platform Turnitin, alongside other rapidly developing artificial intelligence (AI) systems. When generic (but power-laden) maps are incorporated into both student and staff ‘perceived’ spaces through AI, we surface the aspects of feedback that risk being lost. Second, we draw on autoethnographic understandings of our own lived experience of performing radically reflexive feedback within a Master’s in Education programme. A radically reflexive form of feedback may not follow a pre-defined map, but it does offer a vehicle to restore individual student and staff voices and critical self-navigation of both physical and virtual learning spaces. This needs to be preserved in the ongoing shaping of the contemporary ‘postdigital’ university.
|Title of host publication||Mobility, Data and Learner Agency in Networked Learning|
|Editors||Nina Bonderup Dohn, Petar Jandric, Thomas Ryberg, Maarten de Laat|
|Publication status||Published - 27 Mar 2020|
Bibliographical note© Springer Nature B.V. 2020. The final publication is available at Springer via https://www.springer.com/gp/book/9783030369101
Beattie, A. R., & Hayes, S. (2020). Whose domain and whose ontology? Preserving human radical reflexivity over the efficiency of automatically generated feedback alone. In N. Bonderup Dohn, P. Jandric, T. Ryberg, & M. de Laat (Eds.), Mobility, Data and Learner Agency in Networked Learning (pp. 83-99). Springer. https://doi.org/10.1007/978-3-030-36911-8_6