Rationale and Goals

Programming for statistical analysis is essential for life science students because it enables accurate interpretation of complex biological data, supports evidence-based conclusions, and fosters reproducible research. At the University of Glasgow, we teach R/RStudio to ~2,200 students across years 1–5. However, our students often struggle in the early years because programming and statistical theory are both unfamiliar, creating a steep learning curve. Large cohorts (200–700 students) compound this challenge, limiting opportunities for individual feedback.

Our goal was to create customised Smart Worksheets to enhance learning and confidence with R and statistical analysis while scaling effectively across large Year 2 courses. The three Smart Worksheets in this application are the first LearnSci resources designed to support R/RStudio use for statistical analysis, providing real-time feedback on coding errors and accuracy of commands, tests, and figures.

Implementation

We converted existing activities into Smart Worksheets aligned with course outcomes and assessments. In BIOL2044 (Microbiology & Immunology, 320 students), Smart Worksheets were used summatively with unique datasets to assess students' understanding of data/statistical analysis in their lab reports. From a staff perspective, completing/correcting the data analysis section (30% of the grade) before writing the rest of the report allowed students to decouple complex analysis from interpretation and narrative, improving these latter aspects even for those who struggled with coding.

For BIOL2039 (Fundamental Biology, 650 students) and BIOL2041 (Animal Biology, 210 students), we implemented formative customisable Smart Worksheets mirroring previous data labs. Real-time feedback addressed a major limitation in large labs (4 staff for 110 students), where individual support and feedback is limited.

Impact

From 249 survey responses, 82% rated 8–10/10 when asked if they would recommend Smart Worksheets (mean 8.6/10). Students reported an increased confidence of +0.54 for general data/statistical analysis (2.61–3.15) and +0.65 for using R (2.55–3.20) on a 1–4 scale, with the strongest gains among less-experienced students. Importantly, 89% agreed/strongly agreed that the Smart Worksheets supported a deeper understanding of R.

Qualitative feedback highlighted that students valued real-time feedback, step-by-step structure, and support for interpreting outputs rather than remembering code. Students reported deeper, more active learning and skills development: “Allowed me to think for myself more, instead of just being told what to do. It extended my knowledge of what the data actually tells me” and “The worksheet made you think more rather than just copying.” Confidence was summarised by: “It really upped my confidence in knowing that I was moving forward correctly, both in continuing to code and in writing my lab report”. Engagement and motivation were also noted: “Fun and interactive...they help me understand” and “The points also added a motivational element to the activities”.

Wider Applications

We are expanding Smart Worksheets into Year 1 and additional Year 2 courses. They have reduced staff time for R drop-ins and lab report marking, and unexpectedly supported students with social anxiety: “It made me feel more confident as I didn’t have to ask anyone for help, which I find hard to do in social settings.” One student summed up the impact: “Probably the most helpful thing I’ve had at uni so far”.