School of Electronic Engineering and Computer Science

Student Behavioural Patterns within Academic Environments using Mobile Sensing

Supervisor: Dr Usman Naeem

Research group(s): Cognitive Science

Being able to understand how students feel within an academic environment can allow higher education institutions to be more proactive in order to ensure that adequate support is provided, which ensures that student expectations are being met. This is something that educational institutions around the world are paying a close attention to as the current higher education landscape is changing with students now looking for alternatives due to the rising costs associated with study on degree programmes. Existing studies have proved that it is possible to capture data from ubiquitous devices (e.g. mobile phones) that can give an indication of a student’s mental health and wellbeing during the lifetime of their course. The inference of this captured data can be very useful in recognising common patterns that can be utilised within the development of frameworks for designing behavioural monitoring tools for students. However, in order to develop a generic framework, we need to address the following questions: What behavioural characteristics do students from different parts of the world share? Does culture have an impact? Is there any correlation between a student’s academic behaviour and their social wellbeing within their country? This project will address these questions by developing a framework that can be deployed on mobile devices that is able to infer behavioural characteristics of students within a higher education context. This framework will be validated by a study that will be conducted at two international universities.