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

Korfiatis, N. ORCID: (2013) Big Data for Enhancing Learning Analytics:A Case for Large-Scale Comparative Assessments. In: Metadata and Semantics Research. Communications in Computer and Information Science, 390 . Springer-Verlag Berlin Heidelberg, Berlin/Heidelberg, pp. 225-233. ISBN 978-3-319-03436-2

Full text not available from this repository. (Request a copy)


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.

Item Type: Book Section
Uncontrolled Keywords: bigdata,tel,learning analytics,comparative assessments
Faculty \ School: Faculty of Social Sciences > Norwich Business School
UEA Research Groups: Faculty of Social Sciences > Research Groups > Innovation, Technology and Operations Management
Faculty of Social Sciences > Research Centres > Centre for Competition Policy
Depositing User: Pure Connector
Date Deposited: 21 Jan 2015 16:16
Last Modified: 19 Apr 2023 01:26
DOI: 10.1007/978-3-319-03437-9_23

Actions (login required)

View Item View Item