Mining reality to explore the 21st century student experience

Abstract

Understanding student experience is a key aspect of higher education research. To date, the dominant methods for advancing this area have been the use of surveys and interviews, methods that typically rely on post-event recollections or perceptions, which can be incomplete and unreliable. Advances in mobile sensor technologies afford the opportunity to capture continuous, naturally-occurring student activity. In this thesis, I propose a new research approach for higher education that redefines student experience in terms of objective activity observation, rather than a construct of perception. I argue that novel, technologically driven research practices such as ‘Reality Mining’—continuous capture of digital data from wearable devices and the use of multi-modal datasets captured over prolonged periods, offer a deeper, more accurate representation of students’ lived experience.

To explore the potential of these new methods, I implemented and evaluated three approaches to gathering student activity and behaviour data. I collected data from 21 undergraduate health science students at the University of Otago, over the period of a single semester (approximately four months). The data captured included GPS trace data from a smartphone app to explore student spaces and movements; photo data from a wearable auto-camera (that takes a photo from the wearer’s point-of-view, every 30 seconds) to investigate student activities; and computer usage data captured via the RescueTime software to gain insight into students’ digital practices. I explored the findings of these three datasets, visualising the student experience in different ways to demonstrate different perspectives on student activity, and utilised a number of new analytical approaches (such as Computer Vision algorithms for automatically categorising photostream data) to make sense of the voluminous data generated. To help future researchers wanting to utilise similar techniques, I also outlined the limitations and challenges encountered in using these new methods/devices for research.

The findings of the three method explorations offer some insights into various aspects of the student experience, but serve mostly to highlight the idiographic nature of student life. The principal finding of this research is that these types of ‘student analytics’ are most readily useful to the students themselves, for highlighting their practices and informing self-improvement. I look at this aspect through the lens of a movement called the ‘Quantified Self’, which promotes the use of self-tracking technologies for personal development.

To conclude my thesis, I discuss broadly how these methods could feature in higher education research, for researchers, for the institution, and, most importantly, for the students themselves. To this end, I develop a conceptual framework derived from Tschumi’s (1976) Space-Event-Movement framework. At the same time, I also take a critical perspective about the role of these types of personal analytics in the future of higher education, and question how involved the institution should be in the capture and utilisation of these data. Ultimately, there is value in exploring these data capture methods further, but always keeping the ‘student’ placed squarely at the centre of the ‘student experience’.