Designing engaging online labs



A number of core ILOs were not covered by the limited range of physical labs were were able to run within the scope of the restrictions during 2020-21.

Online labs have the potential to be unengaging, particularly if they remove interaction with staff, or are too sanitised and scripted.


Students carried out an online lab where they collected some data (counting parasites on a video), then attended a live online session with staff to learn how to carry out the numerical analysis.

What was done?

Where we could not fit in enough repeats to run a lab face-to-face, we tried to make online labs as interactive as possible.

One example that worked very well involved students carrying out an online lab where they had to collect some data themselves (counting parasites on a video) and after they completed this they attended a live online session.

In this session, the students were able to ask questions for clarity on anything they had carried out in the lab. Further, combining the data they collected with some collected previously, we ran a live data analysis with the students. They welcomed seeing how data was taken from a spreadsheet, sorted appropriately, stripped of anomalous data, uploaded to RStudio, and the analysis run.


For Students

  • making mistakes and having to problem solve.
  • Students got to see the real time process of uploading and analysing data rather than working through a ‘recipe-like’ worksheet. They were highly engaged and asked questions throughout.
  • Students had to add data they had collected giving them ownership of the lab.
  • Could take less set up time compared to creating online resources as an alternative.

Evaluation and student feedback

Our mixed online-asynchronous lab and online-live data analysis was very well received by students. As the analysis was done in real-time, mistakes were made in writing and editing scripts for the data analysis software, and things did not run slickly - as is often the realistic case when entering and running data analysis.

The students really appreciated seeing the mistakes and also the problem-solving required to determine whether the results were skewed (i.e. anomalous data outwith what was naturally possible), or whether the data analysis script was wrong.

They found that the live process humanised data analysis, which students often find intimidating. It showed that these errors and problem-solving opportunities are normal in data analysis processes, no matter how experienced you are.