Raising the Bar-UDL and the potential for transforming learning environments: A case study of the first steps in introducing a UDL framework to teaching, learning and assessment on a departmental level at SETU

About this initiative

The proposed initiative is a continuation of a previously funded project from 2022-23 that utilises the Pearson Mastering Chemistry early alert function to identify those students who may be struggling academically and who are at risk of failing/non progression. Specifically the diagnostics within the Pearson Mastering Chemistry online tutorial system provides actionable insight based upon the student’s performance in a series of weekly tutorials. Once identified it is envisaged that each student will be offered extra support/tuition to assist their learning and overcome the difficulties they are experiencing with the theory content of year 1 chemistry modules. The funding from this call will again allow the extra support/tuition to be provided. The Pearson suite of online tutorials has been used in the Dept. of Science and Dept. of Land Science for several years and has been integral to the delivery of modules especially those at stage 1. The early alert diagnostic function, which is part of this online tutorial system, was identified as having potential to aid student retention as part of a previous SATLE funded project i.e. Pathways to Increase Retention Through Early At-risk Prediction Modelling. As an offshoot of this project it is clear that this initiative aligns with the call’s theme of digital transformation within the tertiary sector. The aforementioned online tutorial system has proved invaluable in the last number of years especially during the Covid-19 pandemic and this teaching and learning enhancement initiative will allow us to effectively maintain and build upon its most successful elements. SATLE funding from 2022-23 has allowed the team to get the initiative up and running and this current funding opportunity would allow us to continue our work and to build upon our learnings thus far.