Big Data for Enhanced Learning Analytics: A Case for Large-Scale Comparative Assessments
Goethe University Frankfurt
August 19, 2013
Proceedings of the International Conference on Metadata and Semantics Research (Forthcoming)
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.
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