Quality Work: How enhancement led approaches to teaching data can reduce workload, improve outcomes, and deliver rainbows and kittens to all.

Abstract

Visit our app here: https://edin.ac/3PSpWhs Background Quality Assurance in Higher Education (HE) serves a range of purposes including the safeguarding of standards and supporting the development of staff and students. However, often Quality Assurance is seen as an onerous task, or even a barrier to innovation, such as when a Learning & Teaching Committee is viewed as an obstacle to the implementation of new teaching or assessment. In Scotland, HE Quality Assurance takes an ‘enhancement led’ approach where strong relationships between education providers, funding bodies and student organisations are encouraged, feedback is delivered to improve outcomes, and the sharing of good practice is encouraged. In the veterinary education sector, quality assurance is particularly important to support the delivery of day one competent veterinarians and meeting accreditation standards. Much of the enhancement led approach to Quality Assurance also sits in line with Open Science Framework approaches to data and methods, particularly in the review of data processing even where data itself is too sensitive to be shared. Summary of Work This talk will outline some of the open science approaches to data that R(D)SVS have developed in their Quality Assurance work. In line with open science approaches, it will share workflows and even utilities which can make Quality Assurance tasks in other schools easier and more comparable across schools. It will outline a potential strategy which interested parties can adopt to improve processes. Take Home Message Quality assurance need not strike boredom into the heart of educators. It can be made to work to our interests where we share good practice, particularly in reducing workload when good practice approaches are freely shared.

Event:

VetEd 2023

Location:

B2 ePosters Sustainability + Wellbeing