Publications on ePortfolio: Archives of the Research Landscape (PEARL)

A preliminary evaluation of data-informed mentoring at an Australian medical school


Bate, F., Fyfe, S., Griffiths, D., Russell, K., Skinner, C., & Tor, E. (2021). A preliminary evaluation of data-informed mentoring at an Australian medical school. Asia Pacific Scholar, 6(1), 60–69.


Introduction: In 2017, the School of Medicine of the University of Notre Dame Australia implemented a data-informed mentoring program as part of a more substantial shift towards programmatic assessment. Data-informed mentoring, in an educational context, can be challenging with boundaries between mentor, coach and assessor roles sometimes blurred. Mentors may be required to concurrently develop trust relationships, guide learning and development, and assess student performance. The place of data-informed mentoring within an overall assessment design can also be ambiguous. This paper is a preliminary evaluation study of the implementation of data informed mentoring at a medical school, focusing specifically on how students and staff reacted and responded to the initiative. Methods: Action research framed and guided the conduct of the research. Mixed methods, involving qualitative and quantitative tools, were used with data collected from students through questionnaires and mentors through focus groups. Results: Both students and mentors appreciated data-informed mentoring and indications are that it is an effective augmentation to the School’s educational program, serving as a useful step towards the implementation of programmatic assessment. Conclusion: Although data-informed mentoring is valued by students and mentors, more work is required to: better integrate it with assessment policies and practices; stimulate students’ intrinsic motivation; improve task design and feedback processes; develop consistent learner-centred approaches to mentoring; and support data-informed mentoring with appropriate information and communications technologies. The initiative is described using an ecological model that may be useful to organisations considering data-informed mentoring.

Category: Empirical, Affective