  Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of
students. The advent of complex "big data" from MOOC platforms is a challenging
yet rewarding opportunity to deeply understand how students are engaged in
MOOCs. Past research, looking mainly into overall behavior, may have missed
patterns related to student diversity. Using a large dataset from a MOOC
offered by FutureLearn, we delve into a new way of investigating hidden
patterns through both machine learning and statistical modelling. In this
paper, we report on clustering analysis of student activities and comparative
analysis on both behavioral patterns and demographical patterns between student
subpopulations in the MOOC. Our approach allows for a deeper understanding of
how MOOC students behave and achieve. Our findings may be used to design
adaptive strategies towards an enhanced MOOC experience
