Student Vulnerability And Agency In Networked, Digital Learning

Amidst vast changes sweeping the higher education landscape, there is an increasing need to use data to increase the effectiveness of teaching and learning, and subsequently, ensure accountability and efficiency in an increasingly resource-constrained and competitive higher education landscape (Altbach et al, 2009). Learning analytics as an emerging discipline and practice promises to contribute to more effective teaching, learning and resource allocation through the collection, analysis and use of student data (Prinsloo & Slade, 2014). As teaching and learning move progressively online and digital, the amount of student data increases exponentially, opening opportunities for data-informed strategies and pedagogies. Though there is no doubt that the collection, analysis and use of student digital data do offer huge potential, there are also a number of risks and ethical challenges such as the belief that data is neutral; the role of algorithms and the algorithmic turn in higher education; the assumptions and epistemologies informing the collection and analysis and use of data; and the increasing possibilities for discriminating against already vulnerable and at-risk students (Slade & Prinsloo, 2013; Prinsloo & Slade, 2014). This paper follows Prinsloo (2014) who proposes that ‘Learning analytics are a structuring device, not neutral, informed by current beliefs about what counts as knowledge and learning, coloured by assumptions about gender/race/class/capital/literacy and in service of and perpetuating existing or new power relations.’ Though the collection, analysis and use of student digital data aims to decrease students’ vulnerability and risks of failing or dropping out, there is also the possibility that in the light of the asymmetrical power relationship between student and institutions of higher learning, students’ vulnerability may actually be exacerbated. As higher education institutions (HEIs) optimise the potential of learning analytics, this paper prop