  The Massive Open Online Course (MOOC) has expanded significantly in recent
years. With the widespread of MOOC, the opportunity to study the fascinating
courses for free has attracted numerous people of diverse educational
backgrounds all over the world. In the big data era, a key research topic for
MOOC is how to mine the needed courses in the massive course databases in cloud
for each individual student accurately and rapidly as the number of courses is
increasing fleetly. In this respect, the key challenge is how to realize
personalized course recommendation as well as to reduce the computing and
storage costs for the tremendous course data. In this paper, we propose a big
data-supported, context-aware online learning-based course recommender system
that could handle the dynamic and infinitely massive datasets, which recommends
courses by using personalized context information and historical statistics.
The context-awareness takes the personal preferences into consideration, making
the recommendation suitable for people with different backgrounds. Besides, the
algorithm achieves the sublinear regret performance, which means it can
gradually recommend the mostly preferred and matched courses to students. In
addition, our storage module is expanded to the distributed-connected storage
nodes, where the devised algorithm can handle massive course storage problems
from heterogeneous sources of course datasets. Comparing to existing
algorithms, our proposed algorithms achieve the linear time complexity and
space complexity. Experiment results verify the superiority of our algorithms
when comparing with existing ones in the MOOC big data setting.
