In this blog, TIGER Chair Rachel O'Callaghan reviews the TIGER event on 'Calculating Feedback for the Masses', which was delivered by Lecturer in Biochemistry and Academic Lead for E-Learning Dr Mark Roberts.
Mark Roberts likes data and delivered a hugely interesting talk for the TIGER seminar series on 25 October titled ‘Calculating Feedback for the Masses.’ He teaches on a 2nd year practical module that consistently receives high feedback scores so was cautious about making any big changes to the module but there were areas where he felt things could be improved.
He started by thinking carefully about what we really want students to do with data - what are the important skills we want them to develop? Mark believes we should teach students more about data handling and focus our learning outcomes on improving data analysis and interpretation – I wholeheartedly agree! This is not an easy task, especially when you want to provide valuable feedback along the way and there are limited options to achieving this goal – we can use model data or demonstrators; both have advantages and disadvantages.
One significant benefit of using model data is that students generate the same answers so it is somewhat easier to mark electronically but this doesn’t demonstrate real science. Ideally, we want students to use real data that they generate themselves in lab classes. The big challenge with this approach is that the marking becomes a mammoth task due to the variety of answers generated. Mark was looking for ‘a solution that would measure if the student had accomplished right transformation of the data’, this way, real data could be used and the marking would be more manageable.
He secured funding from the E-Learning production scheme project and employed a student studying computer science at Queen Mary, who utilised Excel to try and create a workable solution – which they did! An excel based system that uses macros was created to achieve the following:
Essentially there is a single excel worksheet that the students download from QMPlus, they enter the results, perform their data analysis and the best bit, it is marked and feedback generated automatically! The worksheet also produces a summary of their results, the students are asked to include this in a short discussion of their results that is marked by demonstrators using a rubric – although the data calculations are important, this is equally valuable as an exercise as it explores their interpretation of the data.
Although there is a lot of work required to set this up for a module, the benefits far outweigh the effort and there are numerous applications. I was hugely impressed by this example of teaching practice, I think it is brilliant - I am very interested in considering how this approach may be employed in my own modules with a practical element, watch this space!
To find out more, please go to tinyurl.com/sbcsexcel18 (you will need a QMUL login to access the files).
Rachel O'Callaghan, TIGER Chair