The increasing prevalence and sophistication of automated tutoring systems necessitates the development of new methods for their evaluation and improvement. In particular, data-driven methods offer the opportunity to provide teachers with insight about student interactions with online systems, facilitating their improvement to maximise their educational value. In this paper, we present a new technique for analysing feedback in an automated programming tutor. Our method involves first clustering submitted programs with the same functionality together, then applying sequential pattern mining and graphically visualising student progress through an exercise. Using data from a beginner Python course, we demonstrate how this method can be applied to programming exercises to analyse student approaches, responses to feedback, areas of greatest difficulty and repetition of mistakes. This process could be used by teachers to more effectively understand student behaviour, allowing them to adapt both traditional and online teaching materials and feedback to optimise student experiences and outcomes.