Grading details
Overview
The team project is submitted as a reproducible, end-to-end workflow on GitHub, featuring a well-structured and fully automated pipeline for data exploration & cleaning, analysis, and deployment.
Points can be obtained for three assessment criteria:
- GitHub repository (30% of the final grade)
- Data preparation and analysis (45% of the final grade)
- Quality of the source code and degree of automation (25% of the final grade)
The grading rubric can be viewed/downloaded here.
Weights for each component of the grading rubric are indicated in brackets (e.g., 5%). In calculating your final grade, percentages are converted to grade points on a ten-point scale (e.g., 5% make up 0.5 grade points on a 10-point scale), weighted by the following percentages:
- High proficiency and/or exceeds expectations (grade points x 100%)
- Adequate proficiency (grade points x 80%)
- Some proficiency (grade points x 60%)
- Insufficient proficiency (grade points x 30%)
- No proficiency (grade points x 0%)
Example
A student team has shown adequate proficiency in writing source code and automating the project. This part of the team project counts towards 25% of the team project’s final grade. In grade points, this equals 2.5 points on a 10-point scale. The points are weighted with 80% for adequate proficiency. The grade for this part of the data package equals 2.5 x 80% = 2.00.
Disclaimer: The same grading rubric will be used to provide preliminary feedback during the coaching sessions. For these sessions, the sub-criteria are simplified to “Very good”, “Sufficient”, and “Needs improvement” serving as preliminary feedback to indicate whether the project is on track. The detailed sub-criteria outlined above, however, will be applied for the final grade calculation.
Tips for the project
Structure your README.md
The readme is not a copy of your way of deployment (e.g., your report), but links to it. Therefore, only summarize the main results in your readme (e.g., in bullet point format), or include screenshots (e.g., to your app, to your “best” figure).
More information on how a proper readme should look like can be found at Tilburg Science Hub.
Final “housekeeping” checks to conduct before the deadline
Ever considered your project may work exclusively on your computer, but not on somebody else’s? It could just be you forgot to tell your users to install a particular package!
To avoid such common “replication traps”, please check out your repository on somebody else’s computer, go through the installation instructions, and try to run the entire project.
More valuable tips and tricks for submitting a clean & working repository are documented in our checklist for “housekeeping”.