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Module 1: Main Goals

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

LevelCompetenciesTypical Milestones
1Ask 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
2Select 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.
3Independent 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.