Stuck in the Middle? Making Sense of the Impact of Micro, Meso and Macro Institutional, Structural and Organisational Factors on Implementing Learning Analytics

Abstract

Despite evidence that learning analytics has become institutionalised within higher education since its emergence in 2011 (Ferguson, 2012; Gašević, Dawson, & Siemens, 2015), there remain questions regarding its impact on informing curricula, pedagogy and ultimately, on student success (Ferguson et al, 2016; Ferguson & Clow, 2017; Kitto, Shum, & Gibson, 2018). A variety of factors may impact on the implementation of learning analytics (e.g., Leitner, Khalil, &Ebner, 2017; Lonn, McKay, & Teasley, 2017; Scheffel, Drachsler, & Specht, 2015). Despite its huge potential to inform and support learning, learning analytics may become stuck in the middle of, inter alia, the need to balance operational needs and resource allocation, and different perceptions of learning, agency and loci of control in learning, teaching and macro-societal factors. In this conceptual paper, we propose an institutional cartography of learning analytics and explore the impact of a number of micro, meso and macro institutional factors that may impact and shape the institutionalisation of learning analytics. As a conceptual basis for developing this cartography, we utilise the Subotzky and Prinsloo (2011) socio-critical model for understanding student success.