Early success indicators at a South African university: A data-centric approach to understanding and promoting student success behaviours

Abstract

Over the past two decades, the field of analytics for student success has evolved significantly, especially with the emergence of learning analytics as a formal research domain, pioneered by Siemens and colleagues in 2013. However, the use of data to inform decision-making concerning planning, intervention, and teaching and learning practices is not a new concept. Despite this, many institutional strategies to support student success remain siloed and often narrowly focused on specific barriers, rather than adopting a holistic view. This article introduces the concept of ‘early success indicators’ – a framework designed to integrate the diverse elements of institutional support into a cohesive, data-informed model. Unlike traditional approaches that emphasize deficit-oriented terminology such as ‘at-risk indicators’ or ‘early warning systems’, this approach adopts a strengths-based perspective. We ask, “What conditions must be in place for students to reach their full potential?” To answer this, we map key moments across the student lifecycle – from pre-registration through to final assessments – identifying relevant data points and support mechanisms. This work is grounded in the insights of Academic Success Coaches (also known as Faculty Student Advisors), institutional leaders, and data scientists, enabling a multi-stakeholder, interdisciplinary approach to student success. Our findings underscore the importance of institutional collaboration, a culture of data transparency, responsive support systems, and timely, targeted interventions. This conceptual article contributes to the shift from reactive to proactive student success support strategies, aiming to enhance throughput rates and overall student outcomes.