Exciting opportunities to work on a new Discovery Project: Enhancing learner feedback literacy using AI-powered feedback analytics!
Project description:
The ability to understand and act on feedback is one of the most powerful drivers of learning success. Despite decades of research affirming the value of feedback, it remains underutilised and inconsistently effective. A key reason for this gap is the learner’s limited capacity to engage with feedback productively, a skill known as feedback literacy. Current approaches to studying learners’ engagement with feedback have relied heavily on self-reported data. While informative, self-reported data can be susceptible to bias, poor memory, and incorrect self-assessment. This project will complement this self-reported measurement of feedback literacy by tracking students’ real-time interactions with feedback and providing personalised support using feedback analytics powered by AI. This project proposes to harness the power of AI and feedback analytics to address the long-standing issue of feedback traceability and improve support for feedback literacy in higher education, guided by three core research questions:
RQ1: What actions and processes observed through diverse learners’ trace data measure their feedback literacy?
RQ2: How can we personalise support for feedback literacy through capturing real-time trace data about learners’ engagement with feedback and communicating the insights effectively to learners?
RQ3: How does learner feedback literacy develop when interacting with feedback provided by humans versus AI?
We offer two fully-funded PhD scholarships ($36,063 AUD stipend allowance per annum for up to 3.5 years, plus $4,000 travel allowance).
Successful applicants will be supervised by Dr Yi-Shan Tsai and A/Prof Roberto-Martinez Maldonado. They will work with the larger project team including Dr Sadia Nawaz, Prof Michael Phillips, Prof Danijela Gasevic, Prof Phillip Dawson, A/Prof Linda Corrin, Asst Prof Yizhou Fan, Adj/Prof Rafael Ferreira Leite de Mello, and A/Prof Lan Yang. They will be based at the Centre of Learning Analytics at Monash.
The PhD 1 project will focus on advancing analytics-based measurement of feedback literacy, and the PhD 2 project will focus on innovating feedback analytics design using theory-driven and user-centred approaches. Apart from interest in applying theories to understand learning data and passion for solving educational challenges, we would like to see applicants with relevant research skills described below.
Required knowledge
- A Masters degree, Honours distinction or equivalent with at least above-average grades in computer science, mathematics, statistics, educational technology, or equivalent.
- Analytical, creative and innovative approach to solving problems
- Strong interest in designing and conducting quantitative, qualitative or mixed-method studies
- Strong programming skills in at least one relevant language (e.g. C/C++, .NET, Java, Python, R, etc.)
- Experience with data mining, data analytics or business intelligence tools (e.g. Weka, ProM, RapidMiner). Visualisation tools are a bonus.
- Human-centred design
It is advantageous if you can evidence:
- Experience in designing and conducting quantitative, qualitative or mixed-method studies
- Familiarity with educational theory, instructional design, learning sciences or human-computer interaction
- Peer-reviewed publications
- Design of user-centred software
