The ability of researchers to identify the type of activities and levels of interaction among students on a campus is important to research in Learning Analytics and in particular, anthropological studies which explore interactions among students. Historically the collection of base data in such studies has in the main been through observation, questionnaires or a combination of both. This work utilises the unique digital footprints created by student interactions with online systems within a University environment to measure student behaviour and correlate it with exam performance. The specific digital footprint we use is a students connections to the Eduroam WiFi platform within a campus. The advantage of this data-set is that it captures the personal interactions each student has with the IT systems. Datasets of this type are usually structured, complete and traceable. We will present findings that illustrate that the behaviour of students can be contextualised within the academic environment by mining this dataset. We achieve this through identifying student location and those who share that location with them and cross-referencing this with the scheduled University timetable. Our work uses the digital footprint to identify student location and thus co-location of students. From this co-location analyses we infer peer groupings and levels of interaction. This can be used for identifying peers in a University community and for identifying popular locations for different students and their peer groups to meet. This thesis examines the data collection process we followed and our data-mining process. Using the spatio-temporal data derived from the WiFi system we mined the data to produce actionable knowledge for use in the learning process. This research contains min- imal Personal Data and no Sensitive Personal Data as defined by the DCU Data Protection Policy (Version 2.0). All data has been anonymised, stored and used in conformance with the Universitys Personal Data Security Schedule (PDSS).