Purpose & Goals
- Define what successful research‑in‑the‑course looks like for YOUR class.
- Map course learning outcomes to Research Experience Level (REL – see table below) competencies (for a first CURE we are looking at Level 1) and to concrete artifacts (spreadsheet, graph, poster).
- Decide grade weight (recommended 15-20% of final grade) and equity/access principles.
What Faculty Should Do (Step‑by‑Step)
- Write 3–5 measurable learning outcomes (e.g., ‘Students compute mean/SD and explain the pattern in a paragraph’).
- Choose a target REL (1 for most first implementations; 2 for advanced students).
- List expected deliverables: variables sheet, cleaned dataset, 1–2 graphs, 250‑word abstract, poster template.
- Add research milestones into your syllabus calendar with due dates and brief descriptions.
- Identify RAs/peer mentors and define their roles (data steward, reminders, peer feedback).
Concrete Examples
- Biology: ‘By Week 12 students will compute and interpret average blink rate change after 20 minutes of screen exposure.’
- Chemistry: ‘Students will create a neighborhood PM2.5 map using class measurements + public API data.’
- Sociology: ‘Students will code 50 media items for gender portrayals and graph proportions by category.’
- Speech Science: ‘Students will identify clinical and non-clinical voices based on measurements of acoustic properties visualized using the Praat software.’
Troubleshooting & Tips
- If outcomes feel vague, force an artifact: ‘What file will I grade?’ ‘What decision will students justify?’
- If grade weight is low, students under‑prioritize; raise to ≥15% and tie to make‑up/extra‑credit options.
- Equity: ensure no student must disclose personal/health data; offer alternative datasets.
Targeted Resources
- INTREPID Commons → Project Overview page.
- Workshop #1 slides: project goals & structure.
- CUREnet ‘defining outcomes’ guides.
Research Experience Levels (REL) — Quick Map
| Level | Competencies | Typical Milestones |
|---|---|---|
| 1 | Ask a researchable question; form a hypothesis; collect small dataset; compute descriptive stats; interpret tables/graphs; reflect on process. | Steps 1–7: topic → question → data collection → entry → descriptive analysis → visualization → poster draft |
| 2 | Select appropriate analysis; apply ethics/IRB; information & technological literacy; present to external audience. | Step 5 extended (e.g., t‑test/ANOVA/regression or coding); Step 7 polished poster; Step 8 badge & external presentation. |
| 3 | Independent design choices; reproducible workflows; manuscript‑quality products; mentor peers; transition to URE. | Advanced analysis (Python/R), preregistration, replication; lead section of class database; submit abstract to conference; join K‑CORE/CRSP. |



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