The Role of a Reference Synthetic Data Generator within the Field of Learning Analytics.

Alan Mark Berg
Stefan T. Mol
Gábor Kismihók
Niall Sclater

Abstract


This paper details the anticipated impact of synthetic ‘big’ data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at sector wide level and the other at the institutional level - the Jisc learning analytics architecture and the UvAInform learning analytics project running at the University of Amsterdam - we explore the need for an on demand tool for generating a wide range of synthetic data. We argue that the application of synthetic data will not only accelerate the creation of complex and layered learning analytics infrastructure but also help to address the ethical and privacy risks involved during service development.

Keywords


Learning analytics; simulation; synthetic data; student consent service; Jisc learning analytics architecture

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DOI: http://dx.doi.org/10.18608/jla.2016.31.7

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