Degree type
PhD
Closing date
1 June 2025
Campus
Hobart
Citizenship requirement
Domestic
About the research project
Learning activities with constructive and relevant feedback are critical to achieving learning outcomes. Due to large class sizes academics are moving towards automated assessment to improve the timeliness of feedback to the students. Unfortunately automated feedback is often limited to meerly be an assessment of whether a student's answer is right or wrong, with limited guidance as to where the student is doing well and where there might be areas for improvement. Learning Management Systems(LMS) have their own proprietary methods for learning evaluation and feedback, with limited external module support. Incorporating automated assessment tools within an LMS, particularly one that provides meaningful feedback raises a number of issues particularly around privacy and security. LMS are subject to security issues that arise from protocol, application and host-based operating systems vulnerabilities which consequently can engender privacy and authenticity concerns for the learners involved. This project aims to design, develop and evaluate automated and personalised secure adaptive feedback tools for tertiary-education learning activities which enhance students learning experiences with improved learning outcomes whilst ensuring privacy and authenticity are preserved when integrated with an LMS.
Alam, A. (2022). Cloud-based e-learning: scaffolding the environment for adaptive e-learning ecosystem based on cloud computing infrastructure. In Computer Communication, Networking and IoT: Proceedings of 5th ICICC 2021, Volume 2 (pp. 1-9). Singapore: Springer Nature Singapore.
Panadero, E., & Lipnevich, A. A. (2022). A review of feedback models and typologies: Towards an integrative model of feedback elements. Educational Research Review, 35, 100416.
Mondal, A., Maity, S., Mazumdar, N., & Sau, S. (2022, August). Security and Privacy Preserving Online Student Feedback Management System. In 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS) (pp. 379-386). IEEE.
Sabic, A., & Azemovic, J. (2010, June). Model of efficient Assessment System with accent on privacy, security and integration with E-University components. In 2010 2nd International Conference on Education Technology and Computer (Vol. 3, pp. V3-128). IEEE.
Suhail, A. H., Guangul, F. M., & Nazeer, A. (2024). Evolution of Assessment and Feedback Methods in Higher Education. In Utilizing AI for Assessment, Grading, and Feedback in Higher Education (pp. 57-84). IGI Global.
Primary Supervisor
Funding
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 $33,511 per annum (2025 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.
Other funding opportunities and fees
For further information regarding other scholarships on offer, and the various fees of undertaking a research degree, please visit Scholarships and fees.
Eligibility
Applicants should review the Higher Degree by Research minimum entry requirements.
Ensure your eligibility for the scholarship round by referring to our Key Dates.
Additional eligibility criteria specific to this project/scholarship:
- Applications are open to Domestic/ International/ Onshore applicants.
- Applications are open to applications from ICT/Computer Science discipline background only.
- English language score must be above minimum entry requirements for this project.
Selection Criteria
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:
- Demonstrated capacity in critical thinking.
- Background in subject areas relevant to the project (i.e. ICT, AI theory, Cyber security).
- Programming skills with understanding in data analytics and machine learning.
- Quantitative research skills.
Additional desirable selection criteria specific to this project
- Prior experiences in research.
Application process
- Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
- Contact Dr Soonja Yeom to discuss your suitability and the project's requirements; and
- In your application:
- 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.
- Submit a signed supervisory support form, a CV including contact details of 2 referees and your project research proposal.
- Apply prior to 1 June 2025.
Full details of the application process can be found under the 'How to apply' section at Research degrees.
Following the 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.
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