  We present a novel learning analytics approach, for analyzing the usage of
resources in MOOCs. Our target stakeholders are the course designers who aim to
evaluate their learning materials. In order to gain insight into the way
educational resources are used, we view dropout behaviour in an atypical
manner: Instead of using it as an indicator of failure, we use it as a mean to
compute other features. For this purpose, we developed a prototype, called
RUAF, that can be applied to the data format provided by FutureLearn. As a
proof of concept, we perform a study by applying this tool to the interaction
data of learners from four MOOCs. We also study the quality of our
computations, by comparing them to existing process mining approaches. We
present results that highlight patterns showing how learners use resources. We
also show examples of practical conclusions a course designer may benefit from.
