Subgroup Discovery in MOOCs: A Big Data Application for Describing
  Different Types of Learners
J. M. Luna H. M. Fardoun F. Padillo C. Romero S. Ventura
The aim of this paper is to categorize and describe different types of
learners in massive open online courses (MOOCs) by means of a subgroup
discovery approach based on MapReduce. The final objective is to discover
IF-THEN rules that appear in different 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 considering
de-identified data from the first 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,
achieving a runtime that is almost constant for different courses.
Additionally, thanks to the final post-processing step, only interesting and
not-redundant rules are discovered, 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.