Socratic Seminar Toolkit

ASTR 201 — Scholarly Engagement

Author

Instructor: Dr. Anna Rosen

Scholarly Engagement & Seminar Norms

What is “Scholarly Engagement” (10%)?

In this course, your engagement grade measures something real: whether you are practicing the skills scientists use to build understanding together.

You earn Scholarly Engagement credit through: - iClicker participation during in-class questions (often think–pair–share). - In-class group inquiry activities. - Socratic Seminars (a structured discussion where we interpret a shared “text,” often a figure, spectrum, short excerpt, or dataset).

This is not “points for talking.” It’s credit for doing the intellectual work of astronomy in community.

What “good engagement” looks like (examples)

During discussion, lab-style activities, or seminar, strong engagement sounds like:

  • “My claim is , because the plot shows .”
  • “I’m not fully sure, but I think ___ under the assumption that ___.”
  • “Can we check the axis / units / trend again? If that’s true, then ___.”
  • “An alternative explanation could be ___. What observation would separate them?”

You do not need to be loud to be engaged. You do need to be evidence-based and constructive.

Socratic Seminar norms (how we talk like scientists)

In seminar, our goal is shared inquiry, not performance.

1) Anchor claims in evidence.
If you make a claim, point to something specific: a line in the text, a feature in the figure, an axis label, a trend, a number.

2) Name assumptions out loud.
Astronomy is inference under constraints. Assumptions are part of the job, not something to hide.

3) Disagree with ideas, not people.
Use: “I interpret it differently because…” not “That’s wrong.”

4) Share the airtime.
If you’ve spoken a lot, practice listening. If you’ve been quiet, try one contribution: a question, a clarification, or one evidence-based claim.

5) Let uncertainty be normal (but not vague).
Uncertainty is fine. Vague claims are not. Try: “I’m ~70% confident because…”

Practical seminar roles (so everyone can contribute)

Depending on the day, you may be in an inner circle (speaking) or outer circle (observing + supporting).

Outer circle contributions that count as full engagement: - Track where the group used evidence well (and where we didn’t). - Notice assumptions that were stated (or missing). - Identify a moment when someone revised their thinking. - Offer one “what would we measure next?” question during the debrief.

What hurts your Scholarly Engagement grade

  • Side conversations during class or seminar
  • Phone use that distracts you or others
  • Dismissing classmates instead of engaging their reasoning
  • Speaking without evidence (repeatedly) after redirection

Academic integrity and AI tools

Your thinking matters here. Generative AI tools (e.g., ChatGPT, Copilot, Gemini, Claude) are prohibited for course-related assessments.
That includes any for-credit written seminar prep/reflections, if assigned. If you’re unsure whether something counts, ask before submitting.

Discourse Kit

Evidence & Reasoning Sentence Starters

Use these in Socratic Seminar, think–pair–share, and group inquiry activities. The goal is not fancy wording—it’s clear scientific thinking.

Claim (what you think is true)

  • “A conservative interpretation is that…”
  • “The figure suggests that…”
  • “My current best claim is…”

Evidence (what you’re pointing to)

  • “I’m basing that on ___ (axis/line/value/quote)…”
  • “In the region where ___, the trend shows…”
  • “The key detail is ___, which indicates…”

Reasoning (why the evidence supports the claim)

  • “That supports the claim because…”
  • “If ___ increases, then ___ should change because…”
  • “The physical story is: ___ → ___ → ___.”

Assumptions (what must be true)

  • “This depends on the assumption that…”
  • “We’re implicitly assuming ___ (calibration / geometry / equilibrium / negligible dust)…”
  • “If that assumption fails, the conclusion could change by…”

Alternative explanations (how to avoid tunnel vision)

  • “Another explanation consistent with the data is…”
  • “A competing model would predict…”
  • “These interpretations differ mainly in the assumption that…”

Uncertainty (allowed; vagueness is not)

  • “I’m about __% confident because…”
  • “The biggest uncertainty is…”
  • “I’m unsure whether ___ or ___, because the data don’t constrain…”

Discriminating tests (what would we measure next?)

  • “A measurement that would distinguish these is…”
  • “If we observe , it would support model A; if we observe , it would support model B.”
  • “The next-best observation would be ___ because it reduces the degeneracy between…”

Building on others (collaboration moves)

  • “I want to build on what ___ said by adding…”
  • “I agree with ___ under the condition that…”
  • “I interpret that differently because the evidence suggests…”

Common Astronomy Inference Pitfalls

Astronomy is inference under constraints. These pitfalls are normal—the goal is to notice them early and build guardrails.

Use this sheet during problem-solving and seminar.

1) Mixing up what’s measured vs what’s inferred

Guardrail: Write “Observable:” and “Inference:” separately.
Example: flux is measured; distance is inferred using a model.

2) Confusing brightness with luminosity

  • Brightness (flux) depends on distance.
  • Luminosity is intrinsic power output.
    Guardrail: Ask: “Is this property distance-dependent?”

3) Treating a model assumption as a fact

Examples: circular orbits, equilibrium, “standard candle,” negligible dust.
Guardrail: Say: “This conclusion holds if ____.”

4) Over-claiming (data show X → therefore theory Y is true)

Data usually constrain a family of models.
Guardrail: Ask: “What else could explain this pattern?”

5) Ignoring selection effects (“what got into the dataset?”)

What you observe is shaped by detection limits and survey design.
Guardrail: Ask: “What might be missing, and why?”

6) Forgetting units or axis scaling (especially log axes)

A straight line on a log plot means something different than on a linear plot.
Guardrail: Always write the units and identify linear vs log.

7) Confusing correlation with causation

Two quantities can vary together due to a third variable or measurement bias.
Guardrail: Ask: “What mechanism connects them? What would break the trend?”

8) Treating uncertainty as a footnote

Uncertainty is part of the claim.
Guardrail: Try: “I’m ~__% confident because…” and name your biggest uncertainty.

9) Single-figure tunnel vision

A great plot can still be misleading without context (calibration, sample, method).
Guardrail: Ask: “What information is missing that could change interpretation?”

The most scientific question you can ask:
“What observation would discriminate between these explanations?”

Figure Kit

How to Read a Scientific Figure (Micro-Guide)

Name: ____________________________ Date: ____________________

Astronomy is a science of inference. A figure is not “the truth”—it’s a compact argument made out of data, axes, and assumptions.

Step 0: Identify the figure’s job (one sentence)

This figure is trying to show:
______________________________________________________________________________

Step 1: Read the axes like a scientist

  • What are the axes? (write the full variable names, not just symbols)
    x-axis: ________________________________ units: ________________
    y-axis: ________________________________ units: ________________

  • What is measured vs inferred?
    Measured (observables): __________________________________________________
    Inferred (model-dependent): ______________________________________________

  • What is the scale? (linear/log; important!)
    ☐ linear ☐ log ☐ mixed/other: ______________________

Step 2: Describe the pattern (before you interpret it)

Use literal description first.

  • Trend: ___________________________________________________________________
  • Scatter / uncertainty: ___________________________________________________
  • Outliers: ________________________________________________________________
  • Range / limits: __________________________________________________________

If error bars exist: what do they represent? ☐ measurement error ☐ intrinsic scatter ☐ not sure

Step 3: What claim does the figure support (conservatively)?

Conservative claim (supported by what’s shown):
______________________________________________________________________________
Evidence in the figure (point to a specific feature/value/region):
______________________________________________________________________________

Step 4: Name at least one assumption

Interpretation requires assumptions. Name one.

Assumption: _______________________________________________________________
If this assumption fails, the interpretation might change because:
______________________________________________________________________________

Step 5: Ask the “discriminating test” question

What new measurement would best reduce ambiguity?

Next measurement: _________________________________________________________
If we saw ________, it would strengthen the claim. If we saw ________, it would weaken it.

Quick checklist (for seminar)

☐ I can say what each axis means and its units.
☐ I separated description from interpretation.
☐ I named at least one assumption.
☐ I can propose a next measurement.

Socratic Seminar Prep — Half-Sheet

Name: ____________________________ Date: ____________________
Seminar topic / “text” (figure, excerpt, dataset): _______________________________________

1) My Claim (one sentence)

Write a specific claim that you think the “text” supports.

Claim:
______________________________________________________________________________
______________________________________________________________________________

2) Evidence (two concrete pieces)

Point to specific evidence: a quoted phrase, a trend, an axis label + value, a feature in a spectrum, etc.

Evidence #1 (what I’m pointing to):
______________________________________________________________________________
Why it supports my claim (one sentence):
______________________________________________________________________________

Evidence #2 (what I’m pointing to):
______________________________________________________________________________
Why it supports my claim (one sentence):
______________________________________________________________________________

3) Assumption (what must be true for my claim to hold)

Name at least one assumption your inference relies on. (Examples: equilibrium, calibration, geometry, negligible dust, “standard candle” validity, selection effects.)

Assumption:
______________________________________________________________________________
If this assumption fails, my claim would change like this:
______________________________________________________________________________

4) Uncertainty (optional but strongly encouraged)

Try a confidence estimate with a reason.

I am about _______% confident because
______________________________________________________________________________

5) Next Measurement (the discriminating test)

If you had one new observation/measurement you could make, what would best test your claim or distinguish between competing explanations?

Next measurement:
______________________________________________________________________________
What outcome would strengthen my claim?
______________________________________________________________________________
What outcome would weaken my claim?
______________________________________________________________________________

6) One Question I want to ask the group

Ask something that pushes thinking forward (not a yes/no question).

Question:
______________________________________________________________________________
______________________________________________________________________________