Identifying student groups through their wifi digital footprint.

Research in the field of peer influence is often based on the data collected through direct observation, interviews and surveys. These methods can be prone to bias and errors induced through human errors caused by question or answer interpretation or interviewee recall of historical events. My research utilises a unique form of data collection in the learning analytics domain. The data source is the anonymised digital footprint generated through the interaction of students wifi enabled devices and the universities wifi platforms. This datasets are collected without the intervention of researchers. It is hypothesized that the analysis of wifi logs can identify: student location patterns, duration of visits to each location and more importantly those who colocation regularly at these location. This research will carry out a longitudinal study to identify friendships and the correlation between friends academic results