Modelling Affective Learning Behaviours

A data driven learning analytics framework based on users' cognitive and affective learning behaviours

Degree type

PhD

Closing date

1 June 2025

Campus

Hobart

Citizenship requirement

Domestic

About the research project

Engagement can be influenced by different characteristics of individual learners and impacted by the design of the learning activities. This project aims to design and develop a model of student engagement with tertiary-education learning activities. This project focuses on the challenges of collecting, analysing, and interpreting data to improve learner engagement within tertiary subjects, particularly data available within a learning management system that students use throughout a course. A greater understanding of student behaviour with different learning activities may identify mechanisms to improve academic performance.

Engagement can be measured by considering the behavioural, emotional, and cognitive factors that impact the student learning process. This study will also explore the opportunities of advanced multimodal data collection (eg analysing facial expression and/or behaviour patterns during interactions with a learning activity) and the mechanisms to incorporate those collection methods within a learning management system.
This study will investigate what factors lead to learner disengagement with various learning activities and the identification of personalised intervention strategies to improve student performance.

References:
*Grawemeyer, B., Mavrikis, M., Holmes, W., Gutiérrez-Santos, S., Wiedmann, M., & Rummel, N. (2017). Affective learning: Improving engagement and enhancing learning with affect-aware feedback. User Modeling and User-Adapted Interaction, 27, 119-158.
*Schneider, S., Beege, M., Nebel, S., Schnaubert, L., & Rey, G. D. (2022). The cognitive-affective-social theory of learning in digital environments (CASTLE). Educational Psychology Review, 34(1), 1-38.
*Lim, J., & Richardson, J. C. (2021). Predictive effects of undergraduate students' perceptions of social, cognitive, and teaching presence on affective learning outcomes according to disciplines. Computers & Education, 161, 104063.
*Dorji, P. (2021). Affective Domain: The Uncharted Area of Teaching and Learning in Tertiary Education. Asian Research Journal of Arts & Social Sciences, 13(1), 51-65.

Primary Supervisor

Meet Dr Soonja Yeom

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 (e.g., ICT, educational theory, AI theory)
  • Programming skills with understanding in data analytics and machine learning
  • Quantitative research skills

Additional desirable selection criteria specific to this project

  • Prior research experience

Application process

  1. Select your project, and check that you meet the eligibility and selection criteria, including citizenship;
  2. Contact Dr Soonja Yeom to discuss your suitability and the project's requirements; and
  3. 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.
  4. 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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