Dr Senorita John

Doctor of Philosophy (PhD)

Mining reality to explore the 21st century student experience
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.

Dr Kwong Nui Sim

Doctor of Philosophy (PhD)

An investigation into the way PhD students utilise ICT to support their doctoral research process
This study thus examines the degree to which PhD students use ICT to support their doctoral research in their daily academic practices. In order to better understand the role of ICT among PhD students in an uncontrived context, the study adopted the interpretive, naturalist enquiry and analysis approach proposed by Guba and Lincoln, from social constructivist perspectives. This approach underpinned the decision to select a small number of participants from within a particular context to investigate their understandings of their experiences and use of ICT to support their research, in light of the adopted socio-technical framework. Three data sources were used in this study. Computer activity data was extracted from the computer devices of nine full time PhD students who self-reported as being skilled computer users. The second data source consisted of drawings gathered from the same group of participants about their doctoral research process involving the use of ICT. The third dataset represented photographs of this cohort of participants’ work areas as well as individual and group discussion sessions about the participants’ ICT use in this process.

Kait O’Callahan

Master of Higher Education (MHEd)

Exploring the Impact of Sleep on Performance and Wellbeing in Medical Imaging Students.
It is well accepted that university students are likely to experience sub-optimal sleep. Likewise, healthcare workers adhere to shift patterns and long hours that are known to negatively impact sleep quantity and quality. Medical imaging students straddle both worlds, yet research on medical imaging professionals is in short supply. In this study, the degree to which sleep has an impact on medical imaging student behaviours pertaining to wellbeing and performance was explored. The reasons for sleep habits were investigated, as well as whether learning about their individual sleep behaviours helped students make positive changes regarding their sleep.

Taylor Wilson

Master of Higher Education (MHEd)

An exploration of learning and performance in critical care simulation training through the use of data sensors.
The aim of this study was to observe and capture naturalistic behaviour of 10 intensivists involved in a live surgical environment. A multi-modal data-centric design was used that combined video, audio, biometric data and participant self-reports, as well as objective performance data, utilising the Non-technical Skills for Surgeons (NOTSS) scale and the revised Objective Structured Assessment of Technical Skills (OSATS) scale. The four dimensions of learning (the social, cognitive, psychomotor and affective) were used as a framework for analysing behaviour.

Junn Yeong Ng & Grace Ho

Exploring the merits of physiological, psychological and environmental data in determining factors associated with stress in oral surgery tutors.
Aim: To trial a novel multimodal approach for the collection of physiological, contextual and perception data in order to identify potential stressors in Oral Surgery tutors.
Methods: Wearable biometric sensors (wristbands) and clip-on cameras were worn by participants during teaching clinics to capture physiological and contextual data over sixty hours of clinical teaching sessions. Electrodermal activation measured by the biometrics wristbands was used as a proxy for measuring sympathetic nervous system activity (stress measure) with photographic images employed to identify essential contextual experiences (stressors). Both biometric and contextual data were combined in interactive graphs and examined with participants during regular meetings.
Results: Three experienced Oral Surgery tutors directly involved in hands-on clinical supervision of undergraduate Bachelor of Dental Surgery students volunteered for the study. The mean EDA values for each tutor over all sessions in microSiemens (μS) were 7.93 μS, 3.95 μS and 1.61 μS. Tutor A had 9 sessions of high cognitive load, which represented 45% of measured sessions. Tutor B had 8 sessions of high cognitive load, which represented 50% of measured sessions. Tutor C had 8 sessions of high cognitive load, which represented 50% of measured sessions. Sessions were determined as displaying high cognitive load when the mean EDA of the session exceeded the individual mean for each tutor.
Conclusion: Participating oral surgery tutors had a high SNS load in around half of their sessions, suggesting a high overall stress load during clinical teaching sessions.