Digital dry lab assignment to teach data analysis and interpretation skills


The aim of this project was to replace a practical, microbiological experiment (total of 12h) with a run time of two weeks by a digital assignment to allow students to perform a digital preservation experiment that mimics real life, including data analysis. Most parts of the project were implemented as planned (first selecting gamma models and test parameters, setting up a database, writing a scenario for the students about the experiment, data analysis, setting up a preservation tool in Drupal / Labbuddy). However, in the end, we decided not to use this online platform but instead to make use of MS Excel. We want the students to be able to understand the kinetics behind microbial growth, how variables like pH, water activity and temperature affect microbial growth. For this, it is best if the students get to see, compose, edit, and change formulas and parameters. This was (at the time of developing) not yet possible in labbuddy, and would take many more developing hours. Instead, we experienced that the workaround of using an Excel workbook, worked perfectly fine. In addition, after the course, graduates will not have access to labbuddy, but will have access to Excel, which was another important reason to change the platform. The labbuddy developments have not been in vain; they will be and are used in tools for other groups (e.g. Food Chemistry). In addition, we did update and improve the question and explanation section of the labbuddy platform for this digital experiment.


The project has yielded a tool to simulate results of lab experiments and a framework to analyze and interpret the results.


We now have a preservation database containing datasets with (random) growth parameters for the different microorganisms, mimicking real life, we created a model that students can use to predict microbial growth, students are encouraged to compare their results to results obtained through online tools, and an assignment was created that guides the students to perform a digital preservation experiment and to analyze this data. This model can easily be adjusted and improved and allows the student to see and edit every (random) variable that affects growth and analyze results. An improved version of the model is used in the 2018 version of the course (better looking, more realistic model, more guidance on how to execute the experiment), more information on statistics (and how to use Excel for this) is included, all questions are now included in labbuddy (instead of a separate pdf). 


For more information or recommendations, please contact Martine Reij and/or Tjakko Abee.


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