The hidden architecture of higher education: building a big data infrastructure for the ‘smarter university’

Abstract

Universities are increasingly organized and managed through digital data. The collection, processing and dissemination of Higher Education data is enabled by complex new data infrastructures that include both human and nonhuman actors, all framed by political, economic and social contingencies. HE data infrastructures need to be seen not just as technical programs but as practical relays of political objectives to reform the sector. This article focuses on a major active data infrastructure project in Higher Education in the United Kingdom. It examines the sociotechnical networks of organizations, software programs, standards, dashboards and visual analytics technologies that constitute the infrastructure, and how these technologies are fused to governmental imperatives of market reform. The analysis foregrounds how HE is being reimagined through the utopian ideal of the 'smarter university' while simultaneously being reformed through the political project of marketization.

Sustainable Management of Digital Transformation in Higher Education: Global Research Trends

“…This tool involves teaching at a lower cost, which enhances the capabilities of users and creates a personalized student profile. This will enhance the teaching areas in which it presents difficulties, in order to create a unique course through the e-learning system [42]. On the other hand, HEIs are using AI in order to personalize the student admission process, and identify which applicants are most likely to succeed in their degrees and masters.…”

Prerequisites for artificial intelligence in further education: identification of drivers, barriers, and business models of educational technology companies

“…Digital technologies make it possible to collect data on teaching and learning processes in the field of further education. The potential of AI-based learning and teaching, which requires the analysis of large amounts of data, promotes the development of new, refined smart learning tools, algorithm software, and predictive modeling (Mayer-Schonberger & Cukier, 2014;Williamson, 2018). The EdTech sector creates such new, innovative, and smarter teaching and learning models that improve the performance of individuals or whole organizations and lead to new business models in all areas of education (i.e., school, university, vocational training, and further education) (Startup Genome, 2018).…”

Reconsidering data in learning analytics: opportunities for critical research using a documentation studies framework

“…Analyzing the documentation of students in data adds a new perspective to critique current and future educational data mining technologies, practices, and sociopolitical interests. Furthermore, it complements and extends the usefulness of a robust toolbox of applicable theories and approaches, including: infrastructure studies (see Williamson 2018), critical data studies (see Illiadis and Russo 2016; Selwyn 2015), sociology of quantification (Espeland and Stevens 2008;Hardy 2015), data and information ethics (see Floridi and Taddeo 2016;Rubel and Jones 2016), and others. Asking how students are "made into" and "considered as" data creates a new framework for addressing important critical questions.…”