Primary supervisor
Yi-Shan TsaiResearch area
Education and AnalyticsStudent satisfaction with feedback is consistently low in higher education, and there is a lack of understanding regarding how students interpret and interact with feedback. Beyond the formal education context, feedback continues to play an important role in shaping individuals' performance in workplace and lifelong learning.
Learning analytics promises to enhance feedback practice by providing real-time data and insights into learning behaviour and outcomes, so as to inform educational interventions. However, the feedback loop remains open without an understanding of how recipients of feedback make use of the received feedback or the #sustainability of such feedback practice. As technology-mediated feedback (e.g., AI-mediated feedback) becomes an integral part of learning, there is growing urgency in developing ‘feedback literacy’ among learners and understanding how technology shapes feedback pedagogy. These issues also apply in the workpace where the teacher-to-student and peer-to-peer feedback processes transition to employer-to-employee and colleague-to-colleague.
I am seeking PhD students who are interested in taking inter-disciplinary approaches to exploring the issues above. Example questions to investigate are as follows:
- How do feedback recipients make sense of technology-mediated/ AI-mediated/ data-based feedback and act on it?
- How can we enhance digital feedback literacy among feedback recipients and providers through the development of feedback tools?
- How can we enhance the understanding of engagement with feedback using trace data?
- How can the understanding of feedback engagement inform teaching in educational settings or training in workplace?
- How can we improve the communication of 'feedback analytics' using storytelling elements (e.g., data storytelling, data comics, etc.)?
Required knowledge
- Experience in designing and conducting quantitative, qualitative or mixed-method studies
- Interest in educational research, design-focused research, or ethnographic research.
- Skills in one or more of the following areas including software development, data mining, data analytics, experimental design, and qualitative research methods.
- A Master’s degree (research-based), Honours distinction or equivalent with at least above-average grades in computer science, education, psychology, or relevant fields.
- IELTS 7.0 (overall) and good written and spoken communication skills.
