A novel approach for analyzing student interaction with educational systems

The data in higher educational institutions come from the interaction of students with the various online systems, such as learning management, registration, advising and email. Research in the field of educational data mining is concerned with the collection and analysis of such data to discover new insights about student behavior, learning style and success factors. Since different departments in an institution manage different IT systems, collecting data from all of these departments requires collaboration. The data has to be extracted from many systems, which uses different data formats. Therefore, a typical research work in this field analyzes data extracted from one system. This paper, on the other hand, analyzes the network traffic that students generate while on-campus. This approach provides us with a better view of the student interaction with the educational systems, compared to the single view achieved by analyzing data from one system. We anonymize student personally identifiable information to protect student privacy. Further, we propose the use of fog computing to enhance student privacy and reduce network load.