Video Researcher

Research for transformative course videos: $ARGUMENTS

$ARGUMENTS may include:

• A research focus (e.g., completion rates, cohort vs self-paced, corporate training ROI, transfer gap)
• A specific video scene to update (e.g., S05, S06, Act 2)
• A narrative angle (e.g., why online courses fail, what makes cohorts work, the formation gap)
• Empty — run the full research sweep across all categories

Research Categories

Run web searches across these domains. Each category maps to scenes in the course explainer video library (_docs/videos/courses/_shared/scripts/course-explainer-scenes.md).

Category 1: Market Size & Scale (→ S05)

• Global e-learning market size (2025-2026 current, 2030+ projections)
• Corporate training spend globally
• Number of online learners worldwide
• MOOC enrollment numbers (Coursera, edX, Udemy totals)
• Growth rate and trajectory

Search queries:

• global e-learning market size 2025 2026 billion
• corporate training spending worldwide 2025 2026
• online learning market projections 2030
• MOOC enrollment statistics 2025 2026

Category 2: Completion & Dropout Rates (→ S06)

• MOOC completion rates (latest Harvard/MIT data or successors)
• Self-paced course completion rates
• Corporate e-learning completion rates
• Dropout timing (when do people quit?)
• Trend over time (is it getting worse?)

Search queries:

• MOOC completion rate 2024 2025 statistics
• online course dropout rate statistics
• online course completion rate trend getting worse
• when do online course learners drop out

Category 3: The Transfer Gap (→ S07)

• Percentage of learners who apply what they learned
• Corporate training transfer rates
• "Knowing-doing gap" research
• Kirkpatrick model Level 3/4 data
• Cost of non-transfer (wasted training spend)

Search queries:

• training transfer gap statistics percentage apply learning
• corporate training effectiveness percentage employees apply skills
• knowing doing gap online learning research
• Kirkpatrick level 3 transfer rate statistics

Category 4: Cohort & Community Effect (→ S08)

• Cohort-based course completion rates vs self-paced
• Community learning outcomes research
• Social learning effectiveness data
• Accountability and peer effects on completion
• Specific cohort programs with published data (altMBA, On Deck, Reforge, etc.)

Search queries:

• cohort based course completion rate vs self paced
• community based learning outcomes research 2024 2025
• social learning completion rates statistics
• altMBA completion rate cohort based learning

Category 5: Transformative Learning & Formation (→ S09-S12)

• Mezirow's transformative learning theory — empirical evidence
• Disorienting dilemma research
• Experiential learning outcomes vs passive
• Reflection in learning — impact data
• Community/communitas in education research

Search queries:

• transformative learning theory empirical evidence outcomes
• experiential learning vs passive lecture outcomes statistics
• reflection in learning impact effectiveness research
• disorienting dilemma transformative education research

Category 6: The Counter-Narrative (→ compelling contrasts)

• What top-performing courses do differently
• Engagement vs completion (the real metric?)
• Faith-based / formation-oriented programs with data
• Microlearning, spaced repetition, active recall — what actually works

Search queries:

• what makes online courses effective research 2025
• active learning vs passive learning completion outcomes
• spaced repetition learning effectiveness statistics
• faith based online education outcomes

Before Starting

1. Read _docs/videos/courses/_shared/scripts/course-explainer-scenes.md — understand the existing scene library and its current statistics
2. Read _docs/videos/style-guide/brand-tokens.md — understand the visual vocabulary for suggesting components
3. Note which statistics are already cited and their dates — flag anything outdated

Research Protocol

For each category:

1. Search — Run 2-4 web searches using the suggested queries (adapt based on what you find)
2. Verify — Cross-reference claims across multiple sources. Prefer:
   • Academic studies (Harvard, MIT, Stanford)
   • Industry reports (HolonIQ, Statista, Josh Bersin, Class Central)
   • Government data (NCES, UNESCO)
   • Named programs with published metrics
3. Extract — Pull the specific number, the source, the year, and the context
4. Flag confidence — Mark each finding as:
   • VERIFIED — multiple credible sources agree
   • REPORTED — single credible source, not independently confirmed
   • ESTIMATED — derived or extrapolated from partial data
   • OUTDATED — data is 3+ years old and may have shifted

Output Format

Part 1: Research Brief

Organize findings by category. For each finding:

### [Category Name] (→ Scene [ID])

#### [Finding headline]
- **Stat**: [The number or claim]
- **Source**: [Organization, report name, year]
- **URL**: [Link if available]
- **Confidence**: VERIFIED | REPORTED | ESTIMATED | OUTDATED
- **Context**: [1-2 sentences on what this means and any caveats]

Part 2: Scene Update Recommendations

For each existing scene (S05-S08 especially), recommend:

• Which stats to keep, update, or replace
• New stats that strengthen the narrative
• Suggested revised narration lines (keeping voice/tone consistent)
• Any new visual concepts the data suggests

Part 3: New Component Suggestions

Propose 3-5 new video components or scene variants that the research supports:

#### [Component Name]
- **Data point**: [The compelling stat]
- **Narrative hook**: [1-2 sentence script suggestion]
- **Visual concept**: [How this could be animated]
- **Best used in**: [Full explainer / social cut / ad / standalone]
- **Scene pairing**: [Which existing scene it strengthens or extends]

Part 4: Source Index

A clean table of all sources cited, with URLs, for fact-checking and attribution.

File Output

Save the complete research brief to: _docs/videos/courses/_shared/lab/[topic]-research-[YYYY-MM-DD].md

Create the lab/ directory if it doesn't exist.

If updating specific scene statistics, also note the recommended changes in: _docs/videos/courses/_shared/lab/scene-update-recommendations.md

Rules

• Never fabricate statistics. If you can't find a number, say so.
• Always cite sources with year. A stat without a date is useless.
• Prefer recent data (2023-2026). Flag anything older than 3 years.
• Cross-reference market size numbers — they vary wildly by source and definition.
• The narrative purpose matters: we're telling the story of why information alone doesn't transform, and why community-based formation does. Research should serve that arc.
• Don't just dump numbers — contextualize. "5% completion" means nothing without "out of every 100 people who sign up..."
• Keep the Movemental voice in mind when suggesting narration lines — measured, cinematic, warm. Not hype. Not startup pitch.