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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationMetadata and Semantics Research
Subtitle of host publication7th Research Conference, MTSR 2013, Thessaloniki, Greece, November 19-22, 2013. Proceedings
Place of PublicationBerlin/Heidelberg
PublisherSpringer-Verlag Berlin Heidelberg
Number of pages9
ISBN (Electronic)978-3-319-03437-9
ISBN (Print)978-3-319-03436-2
Publication statusPublished - 2013

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer International Publishing
ISSN (Print)1865-0929


  • Bigdata
  • TEL
  • Learning Analytics
  • Comparative assessments

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