Using data more creatively to improve student experience

In the current environment, with particular scrutiny of the quality and impact of teaching within Higher Education, the need for evidence to support the effectiveness of academic practice has become increasingly apparent. Competition for students has resulted in greater emphasis on completion rates and the quality of the programmes. This presentation will explore the effectiveness of two sources of evidence currently used to quantify and enhance student experience and progression. It will outline how one University has developed strategies to make student data more accessible to staff on order to inform programme delivery and student support. The student experience is a complex, but not unfathomable, series of contextual variables. One method of unpicking and understanding these variables is Learning Analytics, which has been growing in popularity amongst UK institutions over the last decade. In general Learning Analytics is “the measurement, collection, analysis and reporting of data about the progress of learners and the contexts in which learning takes place” (Sclater, Mullan, & Peasgood, 2016, p.4) however its potential is far more exciting for the student journey. There is increasing focus on applying the outputs of Learning Analytics to future cohorts using feature-designed models and predictive analytics to create bespoke learning environments and experiences for students. This allows Universities to take an evidence-based ‘what works’ approach (Harrison and Waller, 2017, p. 81) to designing and resourcing student support and wellbeing programmes at the micro level (individual students), the meso level (programmes and departments) and the macro level (university wide). The lessons learned from a pilot study that provides relevant and timely student information to personal tutors will be shared during this session. Student satisfaction is an abstract concept, and the National Student Survey is a proxy measure, a barometer that focuses on only a part of this broader concept. The challenge in optimising student satisfaction is not merely the selection of the measures with which to assess this concept, but in the design of learning and teaching which delivers both a quality student experience and promotes optimum attainment. An evolutionary approach building on collaboration between strategic planners and academic staff has enabled educators to make increasingly intelligent use of this data.