Type

Conference Proceedings

Authors

Alan F. Smeaton
Philip Scanlon

Subjects

Computer Science

Topics
student technology social network analysis computer networks online systems student behaviour environment machine learning

Using WiFi technology to identify student activity within a bounded environment (2017)

Abstract We use 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 student use of the Eduroam WiFi platform within our campus from smartphones, tablets and laptops. The advantage of this data-set is that it captures the personal interactions each student has with the IT systems. Data-sets 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 data-set. We achieve this through identifying student location and those who share that location with them and cross-referencing this with the scheduled University timetable.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Status = In Press
Ireland -> Dublin City University -> Subject = Computer Science: Computer networks
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Alan F. Smeaton, Philip Scanlon

Experts in our system

1
Alan F. Smeaton
Dublin City University
Total Publications: 492
 
2
Philip Scanlon
Dublin City University
Total Publications: 7