Grading Rubric¶
Welcome to the grading rubric for the Agent-Based Modeling & Social Theory course! Here’s how your grade will be determined, with a friendly visual to help you see the big picture at a glance.
Grading Breakdown¶
| Component | % of Grade | Details |
|---|---|---|
| Structured Reading Groups (SRG) | 35% | Prep Sheets: 8 graded at 3% each. Lowest grade dropped. |
| Labs | 25% | 5–6 short lab memos (1–2 pages). Each worth 3–4%. |
| Final Project | 30% | Proposal, schema, prototype, review, presentation, report. |
| Participation & Contribution | 10% | Seminar presence, peer feedback, teamwork evaluation. |
Detailed Breakdown¶
| Category | Component | Weight |
|---|---|---|
| SRG (35%) | Prep Sheets (8 @ 5% each, lowest dropped) | 35% |
| Labs (25%) | Lab Memos (5–6 @ 4–5% each) | 25% |
| Final Project (30%) | Proposal | 1% |
| Schema & Pseudocode | 4% | |
| Prototype Demo | 5% | |
| Design Review | 5% | |
| Final Presentation | 10% | |
| Final Report | 5% | |
| Participation (10%) | Seminar presence, peer feedback, teamwork | 10% |
If you have any questions about grading, please reach out to your instructors.
Classroom Policy¶
♿️ Disabilities & Accommodations¶
Calvin University is committed to access for all students.
If you have a documented disability, contact Student Success (Hiemenga Hall 227) to discuss accommodations.
For pregnancy-related accommodations, contact the Title IX Coordinator (Spoelhof University Center 364).
If you have an accommodation memo, please talk to me in the first two weeks of class.
🌍 Diversity and Inclusion¶
As your instructor, I am committed to creating a welcoming learning environment for ALL students. It is my desire that students from all backgrounds and perspectives be served well in this course. I believe the diversity of experience and perspective students bring to this class enriches us all. This means that we all (myself included) need to practice humility, grace, and a posture of learning from others in the classroom. If you notice someone who is behaving in a way that consistently violates this spirit of inclusion and respect, please let me know.
Concerns Statement¶
If you or someone you know in my class is hurt by something we say or do, please let us know so that we can learn from our mistakes and work towards a resolution with you. We realize that confronting a professor can be intimidating and awkward, so if you do not feel you can approach any of us directly, feel free to do so through another student or another professor or staff member. My hope is that any cause for concern in our course could be dealt with within the confines of our class. However, if you experience something in our course or another course at Calvin that is particularly egregious and you do not feel safe engaging with me/the professor directly or indirectly, you can submit a complaint using the “Comment on Faculty” form online: https://
🩺 Illness, Absences, and Make-Up Work¶
If you fall ill (physically or mentally) for an extended period of time, and you send me documentation from Student Life, Student Health Center, or the Center for Health and Wellness, then—and only then—I will consider allowing you to make up late assignments.
⏳ Incompletes & Late Work¶
An Incomplete (I) grade will be granted only in unusual circumstances, and only if those circumstances have been verified by the Student Life Office. Procrastination does not qualify as an unusual circumstance.
Honesty¶
In this course, you will engage in hands-on modeling, coding, and critical reflection about social systems. Academic honesty is essential—not just for your own learning, but for the integrity of our shared exploration. The guidelines below clarify what is expected regarding collaboration, originality, and attribution in this interdisciplinary setting.
Collaboration & Independent Work¶
Some assignments (such as labs or group projects) will involve collaboration, while others (such as essays or individual coding tasks) must be completed independently. The instructions for each assignment will specify what kind of collaboration, if any, is permitted.
When collaboration is allowed, you must still write up your own understanding and give credit to your collaborators.
For individual work, all writing, code, and analysis must be your own.
Plagiarism and Attribution¶
In this course, plagiarism includes copying code, text, analysis, or model structure from any source (including classmates, online repositories, or generative AI) without proper attribution.
If you use ideas, code, or text from any source—including books, articles, websites, or AI tools—you must clearly cite the source in your submission.
When in doubt, cite your source! Proper attribution is a sign of academic integrity and intellectual honesty.
Consider these rules of thumb:
If you found it efficient to use copy/paste or use a generative language model to create more than one or two lines of your application, you must document the original source of the code.
If the moment you figure out how to do something occurs while you are looking at a website or at the output of a generative language model, you should document that website.
Note that these rules of thumb apply to the code supplied in this course’s materials as well.
Examples¶
Acceptable:
Discussing general modeling strategies or social theory concepts with classmates.
Citing and building on published models, as long as you clearly reference the source and explain your own contributions.
Using generative AI or online resources for inspiration, provided you document exactly what was generated or copied and how you used it.
Unacceptable:
Submitting code, text, or analysis from another student or online source as your own, without attribution.
Copying large sections of code or text from generative AI or websites and failing to cite the source.
Allowing someone else to submit your work as their own.

Figure 1:ChatGPT in the wild (comicagile.net)
Detection & Consequences¶
All submissions may be checked for similarity using tools like MOSS or other plagiarism detection software.
If significant overlap is found between your work and another source (including classmates, online code, or AI-generated content), it will be treated as academic dishonesty.
Academic dishonesty may result in a failing grade for the assignment or the course, and will be reported according to Calvin’s Academic Honesty policy.
If you are ever unsure about what is allowed, ask your instructor before submitting your work.