Subgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners
Autor
Luna, J.M.
Fardoun, H.M.
Padillo, Francisco
Romero Morales, C.
Ventura Soto, S.
Editor
Taylor & FrancisFecha
2019Materia
Subgroup discoveryBig data
MOOCs
Types of learners
Categorizing students
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The aim of this paper is to categorize and describe di erent types of learners in mas-
sive open online courses (MOOCs) by means of a subgroup discovery approach based
on MapReduce. The nal objective is to discover IF-THEN rules that appear in dif-
ferent MOOCs. The proposed subgroup discovery approach, which is an extension
of the well-known FP-Growth algorithm, considers emerging parallel methodologies
like MapReduce to be able to cope with extremely large datasets. As an additional
feature, the proposal includes a threshold value to denote the number of courses
that each discovered rule should satisfy. A post-processing step is also included so
redundant subgroups can be removed. The experimental stage is carried out by con-
sidering de-identi ed data from the rst year of 16 MITx and HarvardX courses on
the edX platform. Experimental results demonstrate that the proposed MapReduce
approach outperforms traditional sequential subgroup discovery approaches, achiev-
ing a runtime that is almost constant for di erent courses. Additionally, thanks to
the nal post-processing step, only interesting and not-redundant rules are discov-
ered, hence reducing the number of subgroups in one or two orders of magnitude.
Finally, the discovered subgroups are easily used by courses' instructors not only
for descriptive purposes but also for additional tasks such as recommendation or
personalization.