Foundations of dynamic learning analytics: Using university student data to increase retention

TLDR

Digitisation and e‑learning have generated vast learner‑behavior data, enabling analytics that can improve learning design, early warning, and student retention. The authors propose a foundational learning analytics model that uses stakeholder‑data interaction and visual analytics to foster shared inquiry and improve student retention. The model employs self‑organising maps and visual analytics to support personalized learning and services across higher‑education institutions.

Abstract

Abstract With digitisation and the rise of e‐learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper‐based course materials, registration, admissions and support services to the mobile, always‐on and always accessible data has driven demand for information and generated new forms of data observable through consumption behaviours. These changes have led to a plethora of data sets that store learning content and track user behaviours. Most recently, new data analytics approaches are creating new ways of understanding trends and behaviours in students that can be used to improve learning design, strengthen student retention, provide early warning signals concerning individual students and help to personalise the learner's experience. This paper proposes a foundational learning analytics model ( LAM ) for higher education that focuses on the dynamic interaction of stakeholders with their data supported by visual analytics, such as self‐organising maps, to generate conversations, shared inquiry and solution‐seeking. The model can be applied for other educational institutions interested in using learning analytics processes to support personalised learning and support services. Further work is testing its efficacy in increasing student retention rates.