Teaching Innovation Awards Winner
University of Exeter
Games and gamified learning can directly contribute to education, improve student motivation and engagement, and help social cohesion within groups of learners. For both educators and students, educative play can bring joy to our studies. During the COVID-19 pandemic, we noted that many students were compartmentalising their learning and lacked a context of their studies across a module and the course. We developed a gamified session that required students to integrate ideas from across our “Analytical Techniques in Biochemistry” module. In this session, students make deductions based on multiple experiments, and work in a team to solve a mystery. The session covers High-performance Liquid Chromatography, Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis, Western Blot, and Mass Spectrometry. It is timetabled at the end of the module to reinforce learning.
We developed the session in the style of a murder mystery, following the popular games “Mafia” and “Among Us”. In our session, the students take the role of external observers rather than participants, and cannot be “eliminated” from the game. This makes the session scalable to larger classes with minimal alteration. We developed an original game fiction, based around fictional creatures living in our department (“Lab Sprites”), to give the game more character and allow us to adapt the setting to our educational needs. Students are provided with a game briefing that outlines the scenario of an Impostor who has infiltrated the Lab Sprites and is attempting to kill them off and provides important data underlying the experiments that students will interpret; and a video demonstrating the game. We run the game interactively using the online platform Mural, which allows students to interact with each other, view the data, and vote on the likely Impostor anonymously at the end of each cycle.
To support the game, we wrote code to generate mysteries according to our needs, allowing varying levels of difficulty. This code produces original and unique data/images for HPLC, SDS-PAGE, and Western Blot experiments based on published data. All our code is publicly available on GitHub and CodeOcean. Students must use all the data knowing that we have embedded errors into the unique datasets and images to distract them from correctly identifying the Impostor. We adopted this strategy to help our students interpret non-perfect data, a skill they will need to succeed in their capstone research projects where data may be contradictory, incomplete, or confusing.
Our students reported that they gain increased confidence in the techniques in the game, or an understanding that they need to review their learning in these techniques. Students reported that they like to see realistic looking data, including imperfections, and that they want a session that is challenging without being too difficult to complete. This module-ending synoptic session enables our students to revise and link their learning across the module.
We have made our code freely available to encourage and support others to bring gamification into their practice. The code for each technique can be used more widely for digital learning of HPLC and SDS PAGE.