Big Data for Enhanced Learning Analytics: A Case for Large-Scale Comparative Assessments

Proceedings of the International Conference on Metadata and Semantics Research (Forthcoming)

7 Pages Posted: 10 Jul 2013 Last revised: 5 Sep 2013

See all articles by Nikolaos Korfiatis

Nikolaos Korfiatis

University of East Anglia (UEA) - Norwich Business School

Date Written: August 19, 2013

Abstract

Recent attention on the potentiality of cost-effective infrastructures for capturing and processing large amounts of data, known as Big Data has received much attention from researchers and practitioners on the field of analytics. In this paper we discuss on the possible benefits that Big Data can bring on TEL by using the case of large scale comparative assessments as an example. Large scale comparative assessments can pose as an intrinsic motivational tool for enhancing the performance of both learners and teachers as well as becoming a support tool for policy makers. We argue why data from learning processes can be characterized as Big Data from the viewpoint of data source heterogeneity (variety) and discuss some architectural issues that can be taken into account on implementing such an infrastructure on the case of comparative assessments.

Keywords: Bigdata, TEL, Learning Analytics, Comparative assessments

Suggested Citation

Korfiatis, Nikolaos, Big Data for Enhanced Learning Analytics: A Case for Large-Scale Comparative Assessments (August 19, 2013). Proceedings of the International Conference on Metadata and Semantics Research (Forthcoming), Available at SSRN: https://ssrn.com/abstract=2291679

Nikolaos Korfiatis (Contact Author)

University of East Anglia (UEA) - Norwich Business School ( email )

Norwich
NR4 7TJ
United Kingdom

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