  Millions of people have enrolled and enrol (especially in the Covid-19
pandemic world) in MOOCs. However, the retention rate of learners is
notoriously low. The majority of the research work on this issue focuses on
predicting the dropout rate, but very few use explainable learning patterns as
part of this analysis. However, visual representation of learning patterns
could provide deeper insights into learners' behaviour across different
courses, whilst numerical analyses can -- and arguably, should -- be used to
confirm the latter. Thus, this paper proposes and compares different
granularity visualisations for learning patterns (based on clickstream data)
for both course completers and non-completers. In the large-scale MOOCs we
analysed, across various domains, our fine-grained, fish-eye visualisation
approach showed that non-completers are more likely to jump forward in their
learning sessions, often on a 'catch-up' path, whilst completers exhibit linear
behaviour. For coarser, bird-eye granularity visualisation, we observed
learners' transition between types of learning activity, obtaining typed
transition graphs. The results, backed up by statistical significance analysis
and machine learning, provide insights for course instructors to maintain
engagement of learners by adapting the course design to not just 'dry'
predicted values, but explainable, visually viable paths extracted.
