πŸ“‹ Instructor Cheat Sheet β€” Session 8: Capstone Research Project

Data Science for Young Minds Β· Grade 5 Β· Ages 10–11 Β· 60 minutes
Session 8 of 8 Capstone Independent Research Full Data Cycle Celebration Session
⏱️ Session Agenda
TimeBlockWhat You DoStudent Activity
0–5LaunchCelebrate completing 7 sessions. Explain: today students are real data scientists completing a full research project.Receive worksheet; read through entire project
5–12Stage 1: QuestionHelp students choose a testable research question. Circulate and approve questions before they move on.Write research question and hypothesis (Part 1)
12–20Stage 2: PlanGuide methodology planning β€” what data, how to collect, potential bias. Emphasize sample size.Complete methodology plan (Part 2)
20–38Stage 3: CollectStudents conduct their chosen data collection (survey, observation, or experiment). Circulate to assist.Collect data in data table (Part 3)
38–50Stage 4: AnalyzeGuide students to calculate percentages, draw graphs, and look for patterns. Prompt analysis questions.Complete graphs and analysis writing (Parts 4–5)
50–57Stage 5: Share3–4 volunteers share their research with the class (30 seconds each).Brief presentations; class asks one question each
57–60CelebrationAcknowledge growth across 8 sessions. Hand out completion certificates if available.Complete course reflection sentence
πŸ“‹ Five Research Project Stages β€” Instructor Guide
Stage 1: Ask

Goal: Form a specific, testable question and hypothesis.

Watch for: Questions that are too broad ("Do people like food?") or untestable ("Is the universe infinite?"). Guide toward "What percent of students in this class…?" or "Is there a relationship between X and Y in our class data?"

Approve before moving on: Question must be answerable with data they can actually collect in class today.

Stage 2: Plan

Goal: Decide what data to collect, how, from whom, and acknowledge limitations.

Watch for: Students who want to survey the whole school (not feasible). Redirect to class survey, personal experiment, or 5-minute observation.

Key prompt: "What could make your sample biased? How will you address it?"

Stage 3: Collect

Goal: Record real data systematically in the data table.

Watch for: Students recording results before they finish collecting, or changing their question mid-collection.

Key prompt: "Fill in every cell. If a value is missing, write 'N/A' β€” don't skip it."

Stage 4: Analyze

Goal: Calculate totals and percentages, draw 2 graphs, identify patterns, write C-E-R conclusion.

Watch for: Students who state their hypothesis was "proved" β€” prompt them to say "supported" or "not supported." Data can never fully prove anything.

Key prompt: "What pattern do you see? Is your hypothesis supported by the data?"

Stage 5: Reflect

Goal: Identify limitations, apply ethics check, think about next steps.

Watch for: Students who think their study is perfect. Every study has limitations β€” help them find them.

Key prompt: "What would you do differently if you had more time, a bigger sample, or better tools?"

πŸ’‘ Suggested Research Questions

Good starter questions for this class:

  • What percent of classmates prefer mornings vs evenings?
  • Is there a correlation between hours of sleep and mood rating today?
  • What is the most common way students got to school today?
  • How many data-generating devices does each student have at home?
  • Is there a relationship between favorite subject and favorite type of data display?
  • What percent of classmates have read a full app privacy policy?
  • How many steps did students take on a school day vs a weekend day?
  • Do students who eat breakfast report higher energy levels?

Remind students: correlation β‰  causation even in their own data!

⭐ ND-Friendly Tips
Capstone Accommodations
  • Provide a list of pre-approved research questions students can choose from rather than generating their own
  • Allow pairs or small groups for data collection if individual work is overwhelming
  • The data table has flexible rows β€” students with limited stamina can collect fewer data points (minimum 8)
  • Graph spaces are intentionally blank β€” provide graph paper or pre-drawn axes for students who need structure
  • C-E-R conclusion can be written as bullet points rather than flowing prose for students who find writing difficult
  • Mini-presentation is optional β€” written worksheet alone is sufficient for assessment
  • Celebrate process over outcome β€” a "wrong" hypothesis that is well-investigated is excellent science
πŸ“Š Simplified Assessment Rubric
Criterion3 β€” Strong2 β€” Developing1 β€” Beginning
Research Question & Hypothesis Question is specific and testable; hypothesis uses "I predict... because..." format with clear reasoning Question is testable but somewhat vague; hypothesis present but reasoning is thin Question is too broad or untestable; hypothesis missing or just a guess
Data Collection Data table complete with 10+ entries; method clearly described; potential bias identified Data table mostly complete (7–9 entries); method described; bias not addressed Data table incomplete (fewer than 7 entries); method unclear
Analysis & Graphs Both graphs drawn and labeled correctly; percentages calculated accurately; clear pattern identified At least one graph drawn; percentages attempted; pattern partially identified Graphs missing or unlabeled; percentages not calculated; no pattern identified
C-E-R Conclusion Claim clearly states whether hypothesis was supported; evidence cites specific data; reasoning explains connection Claim and evidence present but reasoning is weak; hypothesis result mentioned Conclusion is vague; no data cited; C-E-R structure not used
Reflection & Ethics At least 2 meaningful limitations identified; ethics check thoughtfully completed; next steps proposed 1–2 limitations identified; ethics check attempted; next steps vague No limitations identified; ethics check skipped; no next steps
🧰 Supplies Needed
Printed worksheets (1 per student) Rulers Colored pencils Calculators (optional) Graph paper (optional) Timer Completion certificates (optional)

Percentage Formula Reminder (post on board):

% = (count Γ· total) Γ— 100

πŸŽ“ Course Wrap-Up Points
  • They have now completed the full data science process: Ask β†’ Plan β†’ Collect β†’ Analyze β†’ Share
  • They can now spot misleading graphs, question sample sizes, identify correlation vs causation, and think about data ethics
  • These are skills professional data scientists use every day
  • Data is everywhere β€” they are now equipped to read it critically
  • Encourage them to keep asking "How do they know that?" about any statistic they encounter