The flipped classroom has been increasingly employed as a pedagogical strategy in the higher education classroom. This approach commonly involves pre-class learning activities that are delivered online through learning management systems that collect learning analytics data on student access patterns. This study sought to utilize learning analytics data to understand student learning behavior in a flipped classroom. The data analyzed three key parameters; the number of online study sessions for each individual student, the size of the sessions (number of topics covered), and the first time they accessed their materials relative to the relevant class date. The relationship between these parameters and academic performance was also explored. The study revealed patterns of student access changed throughout the course period, and most students did access their study materials before the relevant classroom session. Using k-means clustering as the algorithm, consistent early access to learning materials was associated with improved academic performance in this context. Insights derived from this study informed iterative improvements to the learning design of the course. Similar analyses could be applied to other higher education learning contexts as a feedback tool for educators seeking to improve the online learning experience of their students.