CRM? What CRM? How course teams and academic advisors can work together to use local data to improve the student experience?

Session Outline

Student attrition rates, wellbeing, attainment and progression are all hot topics with the Higher Education (HE) sector (Taylor &Harris-Evans, 2018, Meehan & Howells, 2019, Barr & Jackson, 2018). Debates around the importance of transition into HE during periods of uncertainty in a young person life (Maunder et al, 2012) are common place amongst course teams.

Daniel (2015) states that Higher Education Institutions (HEI's) are operating in a complex environment and some universities are turning to CRM systems to help monitor their students. This is a fantastic development and a really useful tool, however, HEI's are at the early stages of this innovation and rolling out such systems can take a lot of time and embedding. Data mining can be used in HE to improve student lifecycle, performance and student retention (Goyal & Vohra, 2012). Course teams and academic advisors hold a huge amount of localised data and knowledge about students based on attendance, engagement, grades etc. If that data can be used and interegated, it can supply HE staff with the information to design better tools and interventions to support our students.

This interactive workshop will explore how the BSc (Hons) Sport Business Management course team at Sheffield Hallam Unviersity and the academic advisors (also part of the course team) have used localised data about students (in the abscene of a CRM system) to plan interventions to improve the student experience. The workshop will present the current system that is used by the team but will also aim to stimulate new ideas that can be used to enhance the programme further, the attendees can take away the ideas and implement them as they see fit. The workshop will provide attendees with a series of templates used during the project that can be amended and used within their own institutions.

This project is part of an ongoing piece of work that is looking to predict student drop out / progression using a series of touchpoints with academic advisors and course teams.