Advising at Scale: A Framework for Multi-Tier Student Success Interventions in Higher Education

Student success remains a central imperative for higher education institutions globally, yet traditional advising approaches struggle with the twin challenges of scale and specificity. This paper proposes a framework distinguished by its two-dimensional scale: vertical, spanning institutional role-player hierarchies from executives to students, and horizontal, enabling personalized reach across large populations through data-driven automation. A central attribute is a correlation-based needs assessment, foregrounding student voice, systematically linking self-reported challenges with academic outcomes to inform intervention selection and tailoring. Grounded in Improvement Science methodology, the framework creates closed-loop systems where effectiveness is continuously evaluated and refined through iterative cycles. Drawing on Student Integration Theory, Expectancy-Value Theory, and Self-Determination Theory, deployed as evaluative lenses for assessing platform design choices, we articulate how the framework addresses conditions for student persistence while respecting student agency. Contributions include an explicit theorisation of two-dimensional advising scale that builds on and extends existing advising frameworks, novel integration of Improvement Science with automated advising systems, and the use of motivation and persistence theory as evaluative lenses for assessing platform design choices.