Teaching Innovation Awards

Postgraduate research skills training and in-person lab practice are integral to doctoral training programmes. The impossibility of accessing lab facilities during the COVID-19 pandemic, and the reopening of these labs with restrictions, meant most research training could not be provided in person. This took a serious toll on postgraduate research student (PGR) motivation, undermining their self-confidence in lab practice.

To solve this problem, the School of Pharmacy developed two learning resources - “PGR Biosciences Lab Shots” and “PGR Chemistry Lab Shots” – using LearnSci LabSims. Accessible everywhere, these resources allowed students to safely prepare for the lab and regain confidence.

900 LearnSci activities were undertaken in the first nine months of implementation among a community of 179 PGRs. The impact of this work has been recognised across Schools of the University. After sharing their impact in the Academic Community of Practice run by the Faculty of Science Digital Learning Team, other Schools expressed an interest in adopting the resources.

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Pre-COVID-19, students collected data in the lab and processed it in a follow-up session using a Smart Worksheet. Assessment took the form of an end-of-module exam, where calculations were carried out using previously unseen data.

With the shift to online learning, this exam changed to an open book, non-vigilated 24-hour format. High-stakes, time-pressured online assessments are an ideal environment for student collusion, and the previous exam, with a single correct answer, was especially high risk for students checking answers with each other or working collaboratively. The Smart Worksheet continued to be a valuable tool even with no student-collected lab data. Historical datasets were provided which enabled the students to complete the formative work. For the end-of-module assessment, students downloaded the exam paper and their unique dataset, generated by an R script. All students used the correct dataset and there was no evidence of collusion. Alongside the students’ datasets, the R script generated an answer file for the marker containing all answers and workings, including plotted data.

Student performance showed no statistical difference to the previous two years, despite students having access to their notes and the internet, and less time pressure. The mean and median marks for the data section of the exam remained the same as the previous two years.

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