Data connections: a model for responding to student learning needs

Abstract

The collection, collation and cognition of student learning data has become a well-established part of university central systems; however, stakeholders such as learning developers are not always able to access, interpret and act upon the data. Thus, there is a lost opportunity to leverage the potential to enhance and improve student learning through learning analytics (LA). Current practice often consists of parallel academic silos which have limited learning data connectivity. This evidence is typically optimised for reporting metrics to external regulatory bodies, such as the Office for Students’ (OfS) B3 conditions relating to student retention, completion and continuation.

The rich potential within the learning analytics data unlocks new and more timely opportunities for early interventions by learning developers to enhance the course curriculum and increase inclusivity for learners. The seamless integration and collaboration between academic and learning developers creates a coherent and ‘wrap-around’ range of learning opportunities and support materials that together facilitated an effective progression of learning and achievement. It builds upon the well-established body of research relating to student transitions, which prioritised the importance and value of the early identification of the key characteristics of prior learning experiences and settings.

Learning developers make a significant contribution to the student learning experience, and emerging technologies such as learning analytics (LA) provide the potential for earlier interventions for a broader range of students.