For those who would like to use this tutorial as a teaching aid to their class, we have a few recommendations to help the process go more smoothly, based on Human Factors research.
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1. Participation
The first recommendation is to give a percentage of the students’ participation grade as freebie simply for taking the course. We recommend 3-5%. This can be a great low stress way for students to bump up their grade a little, and gives immediate and tangible benefits to taking the course—delayed benefits and only having intrinsic rewards can prevent uptake on courses. If you would like to make sure they have taken the course, simply have them discuss one of the modules or write a short informal piece about something that they learned or found interesting.
2. Scheduling
Our second recommendation is around how this course should be completed. For those taking the course as a self-guided tutorial, whatever speed at which they can most comfortably absorb the information, is the best speed to do this course. However, our recommendation for teaching or taking this tutorial with others, is to set aside time to simply do 1 module a week, absorb the information in it and discuss that module, before the next one.
We have included detailed descriptions of each module, to make it easy to understand and choose which modules are relevant for each person/group’s specific needs.
3. Celebrate Failure
Our third recommendation is to emphasize confidence building and encourage mistakes and ‘failures’ as a learning opportunity. There are real concerns with funding and ability to conduct research if a project is seen as failing. This can lead to decision paralysis and end projects before they even begin due to wanting to do things correctly.
We have identified that when it comes to data sharing, these same concerns hold true. There is concern that data might be shared too far, or that they might make a mistake when anonymizing data, and so it is better to not start at all.
Our recommendation to combat this, is to emphasize that this program will help build their confidence to working towards best practices, and that it is an extremely low stakes course, a great way to learn about data without any worries about how things will turn out. If there are mistakes made, such as in making an account for something or any of the other activities, they should be celebrated as learning opportunities.
More broadly, moving attitudes away from a failure mindset and into a celebration of learning something new will help mitigate these issues on a broader scientific level.
4. Emphasize what is already known
Our final recommendation is based on making the skill gap seem small. If the gap between what students can already do and what they need to learn seems too large, then it will be intimidating and unfun.
This course is designed to be easy to take and understand. It builds on what students already know, and is sub-divided into easy to study module, so that they can choose which ones they feel most excited to learn. The modules introduce data management early, and then build on each skill that is needed to make data management plans function successfully.