  In this paper, we describe data mining techniques used to extract frequent
learning pathways from a large educational dataset. These pathways were
extracted as a directed graph that encoded student learning processes. Our
dataset contains more than 800 million interactions of over 3 million
anonymized students in an online learning platform. Performing process mining
on large and complex datasets regularly yields incomprehensible process models.
Although, if we cluster data and obtain groups following similar processes, we
can greatly improve process mining results. To this end, we developed a
sequence clustering algorithm that let us group students who followed similar
learning pathways. To extract frequent learning pathways from these clusters of
data, we developed a graph-based process discovery algorithm that revealed to
us the sequences of learning activities that many students followed. These
sequences represented highways of student learning.
