Efficient computation of key performance indicators in a distance learning university
Information Discovery and Delivery
ISSN: 2398-6247
Article publication date: 28 February 2019
Issue publication date: 6 June 2019
Abstract
Purpose
Managing efficiently educational Big Data, produced by Virtual Learning Environments, is becoming a compelling necessity, especially for those universities providing distance learning. This paper aims to propose a possible framework to compute efficiently key performance indicators, summarizing the trends of students’ academic careers, by using educational Big Data.
Design/methodology/approach
The framework is designed and implemented in a distributed fashion. The parallel computation of the indicators through Map and Reduce nodes is carefully described, together with the workflow of data, from the educational sources to a NoSQL database and to the learning analytics engine.
Findings
This framework was tested at eCampus University, an Italian distance learning institution, and it was able to significantly reduce the amount of time needed to compute key performance indicators. Moreover, by implementing a proper data representation dashboard, it resulted in a useful help and support for educational decisions and performance analyses and for revealing possible criticalities.
Originality/value
The framework proposed integrates for the first time, to the best of the authors’ knowledge, a set of modules, designed and implemented in a distributed fashion, to compute key performance indicators for distance learning institutions. It can be used to analyze the dropouts and the outcomes of students and, therefore, to evaluate the performances of universities, which can, in turn, propose effective improvements toward enhancing the overall e-learning scenario.
Keywords
Acknowledgements
The authors would like to thank Antonio Enrico Buonocore for carefully proofreading the paper.
Citation
Pecori, R., Suraci, V. and Ducange, P. (2019), "Efficient computation of key performance indicators in a distance learning university", Information Discovery and Delivery, Vol. 47 No. 2, pp. 96-105. https://doi.org/10.1108/IDD-09-2018-0050
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited