Your Project

Using this rubric for formative assessment

Rubrics are most valuable when used formatively — before and during work, not just for final grading. Used this way, the rubric gives students specific, targeted feedback on their strengths and weaknesses while there is still time to act on it. It also supports peer feedback and self-reflection, which are both key to developing students' ownership of their learning.

A particularly effective strategy is to assess a sample argument together as a class before students begin their own projects. Choose a model argument — the model template from the Argument lesson works well — and ask students to apply the rubric to it. Where does the sample fall on each criterion? What would need to change to move it from approaches expectations to meets expectations? Students often find it much easier to see the distinction in someone else's work, and the discussion builds a shared understanding of what the descriptors actually mean in practice.

Once students have drafts, the rubric can structure peer feedback: each student applies the rubric to a partner's draft and writes specific suggestions for improvement. Students can also self-assess before a final submission, annotating their own argument to show where they believe they meet each criterion. Pay attention to patterns in these assessments — if many students are strong on evidence but weak on explanation, that signals a need to revisit the claim-evidence-explanation connection before the project is due.

Throughout this unit, you have been making observations about how diseases spread using computational models. Now it is your turn to design your own investigation, make a claim, and support it with evidence.

A strong argument has three parts: a claim, evidence, and an explanation of how the evidence supports the claim. Here is what each of those means in the context of your project.

Claims

A claim is a specific statement about how diseases work in the real world. Think about something that genuinely surprised you or made you curious while working with the models. What did you wonder about?

A good claim is specific. "Diseases spread" is not a claim — it is just a fact. A better claim might be: "Diseases spread much faster through populations where people have many connections, even if the infection rate stays the same." That is specific enough that you could actually test it with the model, and it says something meaningful about the real world.

Evidence

Evidence is what you actually saw in the model that supports your claim. Evidence needs to be specific enough that someone who was not watching over your shoulder could understand exactly what you saw. That means:

Include your model's parameter settings (infection rate, recovery rate, etc.)
Include a screenshot of the model or a plot of the population health data
Report specific numbers or patterns, not just general impressions

Think of evidence as the part of your argument where you show your work.

Explanation

The explanation is the most important part, and the part most often left out. It is the bridge between your evidence and your claim: why does what you saw in the model support what you are saying about the real world?

Your explanation should tell the reader:

What to look for in your evidence
Why that pattern supports your claim
How well the model represents the real world (remember: all models are simplified — what does this model leave out?)

Without an explanation, your reader is left to figure out the connection on their own. Your job is to make that connection clear.

Methods

Methods describe the choices you made in designing your investigation. A model does not collect evidence on its own — you had to make decisions about which model to use, how to set its parameters, and which runs to compare. Those decisions shape what the evidence can and cannot show, so your reader needs to understand them.

Strong methods explain:

Which model you used and why — Does this model capture the features relevant to your claim? For instance, if you are making a claim about population structure, you need a model that actually represents connections between individuals.
Why you chose your parameter settings — Were they based on real disease data? On a scenario you wanted to explore? On a baseline that makes comparisons meaningful?
Why you chose the cases you compared — What did you vary, and what did you hold constant? Changing one thing at a time is what lets you draw conclusions.

Methods are often left out of student arguments because they feel like background rather than evidence. But a claim supported by unexplained choices is much harder to evaluate or trust.

Rubric

Criterion	Inadequate	Approaches expectations	Meets expectations	Exceeds expectations
Claims	Does not approach expectations.	BUT: The claim may be too vague to test (e.g., "diseases are dangerous"), or it describes only what happens in the model without connecting to the real world, or it is more of a question than a statement.	Makes a specific, testable claim about how disease works in the real world. The claim is meaningful — it describes something that could matter for understanding a real outbreak — and it is something the model can actually help investigate.	AND: The claim is precise enough to be surprising or non-obvious. It makes a connection to a specific real-world disease or public health situation, and reflects genuine curiosity about how diseases work.
Methods	Does not approach expectations.	BUT: The methods may be incomplete or unexplained. The model choice may not be justified, parameter settings may be listed without explanation, or it may be unclear why those particular cases were compared.	Explains how the investigation was designed: which model was used and why it is appropriate for the claim, how parameter settings were chosen, and why the specific cases or contrasts were selected. A reader can understand not just what was done but why.	AND: The methods show careful experimental reasoning. The student explains what was held constant and what was varied, and reflects on how their design choices affect what conclusions can be drawn.
Evidence	Does not approach expectations.	BUT: The evidence may be incomplete, missing key parameter settings or visual data. It may only loosely connect to the specific claim, or describe model behavior in general terms rather than reporting specific results.	Provides specific evidence from the model that directly supports the claim. The evidence includes enough detail — parameter settings and a screenshot or plot — that a reader could understand exactly what happened in the model.	AND: Multiple pieces of evidence are provided, showing that the pattern holds across different conditions. The evidence is presented clearly so the reader can see what the claim is about.
Explanation	Does not approach expectations.	BUT: The connection between evidence and claim may be left implicit, so the reader has to infer it. The explanation may describe the evidence without saying what it means, or may not address how well the model represents the real world.	Clearly explains how the evidence supports the claim. The explanation tells the reader what to notice in the evidence and why it matters. It also reflects on what the model gets right and wrong about the real world.	AND: The explanation considers alternative interpretations or asks what would need to be true in the real world for the model to accurately represent it. It shows genuine critical thinking about the relationship between models and reality.