School of Chemistry Colloquium: Dr Alison Hill Unique data sets for robust and authentic online open book assessments

Online assessments where there is a clear ‘correct' answer are susceptible to students working together, sharing (or even selling) answers. The challenge we faced was to take a 1 hour, closed-book invigilated exam and turn it into a 24 hour, open book, non-invigilated exam. Our approach was to provide every student with their own data set based on a single historical student-generated data set. This enabled all students to have data of equivalent difficulty but with unique answers and different outcomes. Student performance was unchanged despite the very different examination conditions, however, marking times for the data section of the exam doubled. We have now developed code that allows us to mark this section of the exam automatically meaning that our method is now fully scalable. Our students have told us that personalised exams give them confidence their peers are not colluding/cheating as the incentive and opportunity to do so is significantly reduced. Since pioneering the use of unique data sets to personalise assessments, colleagues have developed their own approaches to individualise assessments. We believe that this approach can be used more widely to improve assessment robustness and authenticity. We have now extended this work to create a gamification session for our students which we use to consolidate learning in a fun and engaging way.