Datafication refers to how the world can be seen, explained, and opened up to forms of action or intervention through digital data. The datafication of society extends to all aspects of daily life and has implications for education across all sectors.
The data trails we generate as digital citizens, and the surveillance regimes that feed on these; data driven decision making by institutions, governments and corporations and their attendant questions of privacy and ethics; the impact of data on the media and the political sphere; the growing intensity of datafication through the Internet of Things and artificial intelligence; and the concentration of influence in particular algorithms and platforms are all elements of societal datafication which impact on university teaching at structural, pedagogic and policy levels.
For the special issue on ‘The Datafication of Higher Education Teaching’ we invite papers which engage critically with this theme. Proposals for papers which connect with any of the issues outlined below – and relevant others – are welcome.
Understandings of datafication in HE teaching
In higher education teaching there is sometimes an assumption that data and technology can ‘solve’ problems relating to teaching quality, learner support, plagiarism, attendance and engagement in an unproblematic way. Often prompting over-simplistic adoption models, this kind of solutionist model aligns with an undertanding of higher education teaching as performance-driven and marketised, surfacing profound conflicts around the purpose of higher education itself. Teacher agency and expertise is in some contexts being delegated to data dashboards and data-enabled visualisations, raising questions around the future of teacher professionalism in the datafied society.
Political and social contexts of datafication
Datafication in higher education can indeed be seen as the coming together of a range of social and political factors in parallel with technological change. These include unbundling and privatization, changing patterns of engagement and recruitment at the global scale, the normalisation of ubiquitous surveillance, massification of higher education, new forms of market regulation, and subsequent effects on staff workload, academic precarity and public perception of the value of universities. Universities are working with an ever-diminishing pool of government resources, within a context infused by market values which are – arguably – driving quantification and metrics as ways of evaluating quality of teaching and student experience.
Datafication in the global context
The prevalence of datatification and its consequences are phenomena arising in and from the data-rich global north. This raises a number of issues about the implications for data-poor higher education systems that are more likely to be concentrated in the global south. The paucity of data and data systems in these contexts can lead to importing models without critically engaging with the situatedness of the underlying assumptions, for example, the relationship between data analytics and student success. The particular ways of identifying, analysing, interpreting data in the global north when borrowed or imposed can also render global south practices invisible, as can be seen in university rankings.
Data forms and uses
Accompanying social and political change, technological advances are opening up new ways to generate and use data about HE teaching. Application and progression datasets combined to predict patterns of admission and withdrawal, and analytics designed to identify students at risk for targeted support are already well-used in universities. Teaching quality is increasingly subject to ratings and metrics produced at accelerating timescales. Data generated manually by student use of learning management systems and access to library and other services can be subjected to learning analytics designed to map engagement, sometimes promising to aid prediction of ultimate student success or failure. New systems are being built to track student experience and satisfaction with even further precision and at greater scope, with longitudinal outcomes datasets becoming available for linking taught courses to long-term earnings and career outcomes. Adaptive learning platforms are coming online accompanied with promises of personalized education, where part of the pedagogy is delegated to machine learning-based recommendation systems and algorithmic ‘learning playlist’ generators.
More speculatively, with sensor and device-based tracking of individuals technically possible, location analytics, facial recognition, and wearable biometrics have potential for data-enabled student tracking, attendance and ‘engagement’ monitoring. Growing interest in the Internet of Things and artificial intelligence in education accompanies promises of intelligent tutoring assistants and teacherbots. Meanwhile environmentalists are sounding the alarm about the potentially harmful ecological footprint caused by the power needs of AI and the Internet of Things, raising questions about the environmental responsibilities of the datafied university.
Deadline for submission of abstracts: 11 January 2019
Invitation to submit full papers: 25 January 2019
Deadline for full papers: 1 June 2019
Publication: January/February 2020
Please submit abstracts here. The closing date for submission is 11 January 2019.