18 July 2022
About the research project
Higher education is facing a difficult future. The current pandemic has significantly impacted upon international enrolments, budgets are being cut and the impact of rapid online teaching responses have all taken their toll. Academics are increasingly having to adapt and teach students from a broader range of sources that are too frequently ill prepared for University study. The entry requirements for some disciplines have also reduced over the past two decades leading to more ‘remedial’ teaching and support systems to facilitate student success. These entry requirements are perceived as being more informed by budget and enrolment targets (and considering attrition) than ensuring quality of outcomes, but there is minimal literature to suggest an alternative, tested model for selecting based on success potential. This project will develop a model for selecting applicants based on their likelihood of success in their field of study. The outcomes will potentially revolutionise the higher education selection process and ensure that new students are in a study path that will result in a good outcome.
This project takes a data driven approach to identifying success factors for university study and provide a prediction tool by which all new applicants can be assessed. Foundation work done by the Principal Supervisor at Flinders university focussed on success factors for online learning. (Work on success modelling for applicants was conducted but is unable to be shared due to confidentiality.) Previous work demonstrated that it is possible to predict the success potential of a student based on education history, teaching delivery and other personal characteristics. The work proposed for this PhD project would take that learning and develop a model by which applicants could be evaluated and success factors determined to help guide admissions and potentially provide a mechanism by which applicants could be counselled on learning and study pathways that would present the highest potential for individual success.
The project would utilise a number of computer science skills including analytics, machine learning, programming, security, human factors, and deep data interrogation.
Primary SupervisorMeet Prof Anna Shillabeer
Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:
- a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 3.5 years
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Applicants should review the Higher Degree by Research minimum entry requirements.
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- Experience in statistical analysis tools and technologies and well developed statistics skills are required
There is a three-step application process:
- Select your project, and check you meet the eligibility and selection criteria;
- Contact the Primary Supervisor, Prof Anna Shillabeer to discuss your suitability and the project's requirements; and
- Submit an application by the closing date listed above.
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.
Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.