Session Outline

Higher education is increasingly steered by dashboards: continuation, engagement, attainment, satisfaction, and “early alert” indicators. Yet the mechanisms that often determine student success such as, belonging, mattering, self-efficacy, and the quality of the student advisor relationship, are only partially visible in institutional datasets. This can produce a recurring failure mode; we can detect risk in metrics, but we lack sufficient context to interpret it fairly or respond in ways that are empowering, inclusive, and ethically proportionate.

This presentation proposes a practical synthesis to this problem; metrics in service of meaning. Introducing a human centred advising model where analytics are treated as hypothesis generating prompts rather than verdicts about students. The approach combines psychologically informed advising micro-skills (coaching questions, strengths-based reframing, small-steps action planning), belonging and mattering led touchpoints, and a transparent “sensemaking” method aligned with sector expectations for ethical use of learning analytics (purpose clarity, proportionality, and bias awareness). Using an applied case example from a high pressure studio based academic discipline, the paper demonstrates how the same dataset can lead to deficit responses, for example “fix the student”, or to agency building advising, understanding the story, reducing friction, and building capability.

Learning Outcomes

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