Primary supervisor

Yi-Shan Tsai

Feedback is crucial to learning success; yet, higher education continues to struggle with effective feedback processes. It is important to recognise that feedback as a process requires both teachers and students to take active roles and work as partners. However, one challenge to facilitate a two-way process of feedback is the difficulty to track feedback impact on learning, particularly how students interact with feedback. This project seeks to address this problem by taking a human-centred approach by involving students and teachers to design and implement a learning analytics-based feedback tool to collect and analyse data generated when students make sense of the feedback that they receive. 

 

    Student cohort

    Single Semester
    Double Semester

    Aim/outline

    The project has multiple objectives and students may work towards one or more objectives depending on whether they are undertaking a single-semester or double-semester thesis.

    Objective 1: Understanding students’ experience with feedback (How do students interact with feedback? What does this tell us about their feedback literacy? What are the implications for teaching?)

    Objective 2: Understanding teachers’ experience with feedback (How do teachers facilitate a feedback process? How can data analytics support a two-way/ dialogic feedback process? What are the implications for professional development?)

    Objective 3: Explore ways to improve the effectiveness of data-driven feedback (How can we co-design solutions with students and/or teachers? How can we apply storytelling & visualisation principles to enhance the presentation of data? What are the implications for the development of relevant literacies, e.g., digital literacy, data literacy, and AI literacy? How do feedback agents including students, teachers, peers, and AI , interact with each other to facilitate an effective feedback process?)

    Approach: Lab studies, case studies, workshops, surveys, and interviews. Potential data to collect may include interview recordings, ThinkAloud data, eye-tracking data, log & trace data, among others.

    Required knowledge

    Depending on the objective(s) above that students are interested in, one or more prior knowledge and experience from the following list would be expected:

    • Knowledge in Data Science
    • Ability to programme in R or Python
    • Experience in user-centred design, human-centred interaction, or educational research in general.
    • Experience in interview design and analysis
    • Experience in survey design and analysis
    • Experience in eye-tracking study set-up and data analysis
    • Experience in trace data mining and analysis