Outline of the talk

1. What I supposedly do during the day
2. A brief taxonomy and history of online educational models
3. Massive Open Online Courses (MOOCs)
4. JHU Biostat involvement in Coursera
5. Novel moving target directions of the field statistics
6. Data Science series
7. SWIRL

About these slides

• HTML5 using (customized) Google io2012 style
• Created using slidify
• Appear on github at (https://github.com/bcaffo/MOOCtalk) fork if you'd like
• Jointly written with my collaborators Jeff Leek and Roger Peng
• CC licensed by-nc-sa

SMART (www.smart-stats.org)

• Traditional in person teaching generally has characterists of: in person, lecture style, non-scalable, high cost, restricted access with a large amount of instructor interaction valued credentialing
• "Tranditional" online courses are online or blended online and in person and otherwise try to approximate traditional in person classes.
• Online interactive learning (OIL Code School, Code academy) primary characteristics are student-paced interactive learning
• Intelligent tutoring systems (swirl), like OILs just not necessarily online
• Khan Academy is online, interactive, scalable and low cost
• Other modalities : iTunes U, OpenCourseware, Udemy

Primary characteristics are open access, low cost, scalable, online

(every letter is negotiable, from Wikipedia citing Mathieu Plorde)

Several university/organization-specific sites, platforms and content delivery systems

• Stanford, CMU, Duke, Harvard, MIT, google ...
• Varying degrees of content/delivery
• EdX platform has been open sourced
• Google course builder (now contributing to EdX)
• Massive amount of development going into platforms and instruction sites/portals

You're on the internets

(Forums can be brutal)

Original three

• Brian Caffo, Roger Peng, Jeff Leek
• Run 09/2012, 09/2012, 01/2013
  

Since then

• Scott Zeger introduced class Cased Based Introduction to Statistics
• Brian introducted MBBC2
• Martin Lindquist introduced Statistical Analysis of fMRI Data
• John McGready introduced Statistical Reasoning for Public Health

• Students participate in MOOCs for a variety of reasons
• Numerous students sign up for a course, but do not actively participate
• Recent MBBC 1
  • 17K students
  • 10K accessed the course site ever
  • 7K watched any video ever
  • 2K submitted any quiz
  • 196 earned a SOA
  • 129 signature track
  • 128 posted to the forums

• Classes considered are MBBC1, MBBC2, CDA, DA, Case, StatReas, fMRI, Tools, Rprog, Clean
• A total of 776,691 students enrolled
  

• 19 class offerings

• Average of 40,878 students per class

• Minimum class size of 6,742 for class MBBC2 offering 2

• Maximum class size of 101,747 for class DA offering 1

• Verification based on typing patterns (highly unique according to Coursera)
  • Secondary verification via webcams
• Coursera gives student aid
• Revenue distribution via agreement between Coursera and partner institution
• 155 signature track for 20k students in MBBC1 V4
• Much higher retention and completion rates for signature track
• Between 1% and 12% Signature track rate for classes (depending on a variety of factors)
• All of our classes are $49 for sig track

Case studies

Codirected and taught by Roger Peng, Jeff Leek and Brian Caffo

• Everything done on Coursera
• All programming in R
• All lecture notes done in Slidify (common theme)
• All content open source
• Version control through git and github
• (Students will learn and use git)
• RStudio as an IDE
• knitr for reproducible documents and report writing