Learning analytics as assemblage: Criticality and contingency in online education

Abstract

Recently, the possibilities for leveraging ''big data'' in research and pedagogy have given rise to the growing field of ''learning analytics'' in online education. While much of this work has focused on quantitative metrics, some have called for critical perspectives that interrogate such data as an interplay between technical infrastructures and contingent social practices. Following such calls, this article conceptualizes ''learning analytics'' as an assemblage of technical, designed, and sociocognitive dimensions. Drawing on DeLanda's articulation of assemblage theory, we examine the ways online learning unfolds within and across these scales by using illustrative quantitative and qualitative data-click-data, user-generated content, and student interviews-from three online higher education courses. We consider how insights generated from such a stance might contribute to critical perspectives on how power circulates in online learning environments-a framework we call ''critical learning analytics.'' We conclude by offering some possibilities for which such a framework might be put to use-not only to map learning analytics as assemblage, but also to imagine how they might be assembled otherwise to promote more ethical instruction and more equitable student flourishing.

Mapping the Ethics of Learning Analytics in Higher Education: A Systematic Literature Review of Empirical Research

“…Three studies in total presented mixed locations. In more detail, the studies conducted in North America were located in the United States (Jones & VanScoy, 2019;Jones, 2019a;Jones, 2019b;Klein, Lester, Rangwala, & Johri, 2019;Sun, Mhaidli, Watel, Brooks, & Schaub, 2019;Scott & Nichols, 2017). The studies conducted in Europe were located in Germany (Ifenthaler & Schumacher, 2016) and the UK (Adejo & Connolly, 2017;Tsai et al, 2019;Whitelock-Wainwright, Gašević, Tejeiro, Tsai, & Bennett, 2019;Slade, Prinsloo, & Khalil, 2019;.…”

“…Only Klein et al (2019) reported observation data. Only Scott and Nichols (2017) reported clickstream data and usergenerated content collection methods. Lawson et al (2016) reported text mining.…”

“…Statistical analysis was also reported in eight studies. Two studies do not explicitly report on the data analysis methods applied, although they clearly state the methodological approaches underpinning their studies (Lawson et al, 2016;Scott & Nichols, 2017). Among the works applying statistical methods, we observe the use of the statistical package for social analysis (e.g., Ifenthaler & Schumacher, 2016;West et al, 2016;Adejo & Connolly, 2017;Mahroeian et al, 2017), including descriptive (e.g., Arnold & Sclater, 2017), inferential statistics (e.g., Slade et al, 2019), exploratory and confirmatory factor analysis (e.g., Whitelock-Wainwright et al, 2019), as well as Rasch analysis (e.g., West et al, 2018).…”

“…Only one quasi-experimental study (Ifenthaler & Schumacher, 2016) focused on students perceptions concerning privacy principles. One study employed mixed methods (Scott & Nichols, 2017) combining clickstream data, data generated with interviews, and questionnaires.…”

Rethinking Availability in Multimodal Composing: Frictions in Digital Design

“…Crucially, these dimensions do not operate in isolation. In previous studies, Phil (first author) showed how shifts in one form of infrastructure (e.g., an app interface, a platform algorithm, a software company’s funding strategy) can support or work against other infrastructural relations, which in turn influence how, for whom, and for what purposes a platform operates (Dixon‐Román, Nichols, & Nyame‐Mensah, 2020; Scott & Nichols, 2017). For digital multimodal activities, this means that no part of the composing process—from the literacy practices used, to the availability of modal artifacts, to the completed compositions themselves—is untouched by the infrastructures that underwrite their associated technologies.…”