Efficient computation of key performance indicators in a distance learning university
| Published date | 20 May 2019 |
| DOI | https://doi.org/10.1108/IDD-09-2018-0050 |
| Date | 20 May 2019 |
| Pages | 96-105 |
| Author | Riccardo Pecori,Vincenzo Suraci,Pietro Ducange |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
Efficient computation of key performance
indicators in a distance learning university
Riccardo Pecori, Vincenzo Suraci and Pietro Ducange
SMARTEST Research Centre, eCampus University, Novedrate (CO), Italy
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 com pute 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 resu lted 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 Key performance indicators, E-Learning, Virtual learning environment, Learning analytics, MapReduce, Educational big data
Paper type Research paper
1. Introduction
In the last years, higher educational institutions have
significantly increased their usage of e-learning management
systems (eLMS) and virtual learning environments (VLEs)
(Bhuasiri et al.,2012). Many traditional leading universities
offer coursesto distant learners nowadays, becoming“blended”
universities and attracting a growing number of learners (Farid
et al.,2018). Blended learning implies both traditional lectures
and the usage of digital platforms, where didacticmaterials can
be uploadedand downloaded.
Another phenomenon, which has gained momentum in the
last years, is the development of full distance learning universities,
where teaching and learning are completely provided through a
VLE, featuring both video and audio lectures as well as
interactive and collaborative instruments such as concept maps,
ePortfolios, wikis, etc. (Tu et al.,2012).
This rise of e-learning was mainly due to the advancements in
ICT, but other reasons for that can be found in the requests from
students who cannot be categorized as traditional: workers,
impaired or injured, or living too far from the academic premises.
E-learning systems feature multiple advantages (Schneider
and Blikstein, 2015), but the most important is the huge
number of students that can be reached. This is possible thanks
to MOOC (Massive Open Online Course) platforms, such as
the ones of Khan Academy[1], EduOpen[2], MIT Open
CourseWare[3],Coursera[4], edX[5] and the like.
However, the effectiveness of an e-learning educational
approach needs to be evaluated considering the viewpoint of its
different stakeholders. To this aim, a very helpful aid can come
from the computation of summarizing key performance indicators
(KPIs), i.e. indices that can effectively convey the success and the
quality of particular degree courses with respect to national or
international benchmarks. Indeed, these are indices that are used
to evaluate also traditional in-presence universities, but in
e-learning universities, the data for calculating KPIs can be
accessed more easily. Moreover, KPIs, used in traditional
universities as well, can be enriched through the additional data
made available by the VLEs.
This can be done through Big Data mining and analytics
techniques capable to handle the large amount of educational
data, and to ease the development of new features allowing to
shorten response time or to reveal early dropouts (Knowles,
2015)(Ducangeet al.,2017).
This said, the contribution of this paper is the design,
implementation, and experimentation for the first time, to the
best of our knowledge, of a Distributed Learning Analytics
framework, based on the MapReduce paradigm, to compute
efficientlyKPIs by using educational Big Data (EBD) generated
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/2398-6247.htm
Information Discovery and Delivery
47/2 (2019) 96–105
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-09-2018-0050]
The authors would like to thank Antonio Enrico Buonocore for carefully
proofreading the paper.
Received 30 September 2018
Revised 9 December 2018
Accepted 20 December 2018
96
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