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ERIC Number: EJ1126811
Record Type: Journal
Publication Date: 2016
Pages: 22
Abstractor: As Provided
ISSN: EISSN-1929-7750
The Role of a Reference Synthetic Data Generator within the Field of Learning Analytics
Berg, Alan M.; Mol, Stefan T.; Kismihók, Gábor; Sclater, Niall
Journal of Learning Analytics, v3 n1 p107-128 2016
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 the sector-wide level (the Jisc learning analytics architecture) and the other at the institutional level (the UvAInform learning analytics project 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 will also help to address the ethical and privacy risks involved during service development.
Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Evaluative; Information Analyses
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Netherlands (Amsterdam)
Grant or Contract Numbers: N/A