Big data and learning analytics: Singular or plural?

Abstract

Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation — learning analytics — with three other big data contexts — the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.

Perspectives on Data and Practices

“…Big data practices are not theory-free, but theory comes in at a different stage (at the design phase). [21] demonstrate this for big data in the physical sciences where theory is in the Large Hadron Collider. According to [4], programmed theory is also known in social science: He states that theories are in instruments used in qualitative data analysis, i.e.…”

Surveillance Imaginaries: Learning from Participatory Speculative Fiction

“…Following research into other story-generating tools, we developed a process to use prompt questions to generate the initial story-objects. The prompt questions were scripted following a detailed analysis of research literature on surveillance in HE (Adams 2010;Costa et al 2018;Hall 2013;Hyslop-Margison and Rochester 2016;Knox 2010;Lorenz 2012;Macfarlane 2013;Melgaço 2015;Picciano 2014;Prinsloo and Slade 2015;Ross and Macleod 2018;Rubel and Jones 2016;Watson et al 2017;Wilson et al 2017aWilson et al , 2017b. We analysed this literature to identify repeated themes and concerns, and we drafted a series of questions that prompted respondents to explore whether these themes or concerns were relevant in relation to an experience they had personally undergone.…”

“…Data collection and processing have become intrinsic to universities' knowledge management, strategic planning, institutional decision-making, quality assurance and enhancement, and efforts to ensure student participation and retention (Bouwma-Gearhart and Collins 2015;Fong and Caldwell 2016). This is resulting in the objectification, quantification, and recording of a very wide range of HE practices, including learning (Beetham et al 2022;Williamson 2018;Wilson et al 2017aWilson et al , 2017b.…”

Disruption and Legitimacy: Big Data in Society

“…This position is consistent with the view of innovation as creative destruction (Schumpeter, 1942), whereby human and social progress require the elimination of less competitive firms. However, expanding the view to take on potential impacts over other parts of the system, the disruptive impact of Big Data becomes more complex, as shown by the growing debate about the opportunities and challenges of Big Data (Chen & Zhang, 2014;Popovič et al, 2018;Stahl et al, 2014;Wilson et al, 2017;Zadeh et al, 2019). The opportunities include rapid yet accurate strategic decision-making (Merendino et al, 2018), value creation (Huang et al, 2017), flexibility of resource allocation (A.…”