Teaching statistics for the future: the MOOC revolution and beyond

Brian Caffo, PhD
09/20/2014

Outline of the talk

  1. Who am I?
  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 of statistics
  6. Data Science Specialization
  7. swirl

Slides

  • HTML5 using Rstudio presenter
  • 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

Core team

Drawing Drawing Drawing
Drawing Drawing Drawing

Plus generous contributions from the

My day job(s)

Connectomics

resting state fMRI

Drawing


Drawing

Educational systems

  • Online / in person / blended
  • Active/participatory/interactive learning
  • Scalable / non-scalable
  • Low cost / high cost / freemium
  • Student paced / teacher paced
  • Open / restricted access
  • Flipped / lecture style / blended
  • Open / closed source content
  • Instructor interaction
  • Credentialing
  • Funding model

Massive Open Online Courses

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

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

Drawing

Most visible MOOC instruction sites

Coursera platform, videos

Drawing

Example videos (on YouTube)

Important number: production time per hour of video time

Equipment

Coursera platform, quizzes

Drawing

Coursera platform, peer grading

Drawing

Coursera platform, forums

Main source for student interaction

(Forums can be brutal)

Drawing

Johns Hopkins Biostat Coursera classes

Original three

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

End of course enrollments

plot of chunk unnamed-chunk-1

End of course SOA

plot of chunk unnamed-chunk-2

Log10 enrollment over session, non DSS

plot of chunk unnamed-chunk-3

Log10 enrollment over session DSS

plot of chunk unnamed-chunk-4

Percent SOA over sessions non-DSS

plot of chunk unnamed-chunk-5

Percent SOA over session DSS

plot of chunk unnamed-chunk-6

Important consideration about completion rates

  • 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 ($6k in gross revenue)
    • 128 posted to the forums

Some summary statistics

  • A total of 1,656,654 enrollments
  • A total of 92,812 SOAs issued
  • 16 Unique classes
  • 56 class offerings
  • Average of 29,583 students per class.
  • Minimum class size of 6,139
  • Maximum class size of 101,747

Signature track

Drawing Drawing
  • 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
  • Much higher retention and completion rates for signature track

Case studies

Drawing

Case studies

Drawing

Case studies

Statistics, big data, data science

Drawing

Complimentary problems

Drawing

Johhs Hopkins Data Science Specialization

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

Drawing

Courses

Drawing

Specialization certificate

Drawing

Program format

  • 9 signature track courses
  • 1 capstone project course
  • Total cost (modular) $490
    • $49 per sig track for 10 classes
  • Each class is four weeks
  • Quizzes, in video quizzes and peer assessment projects
  • Run monthly after initial rollout
  • All content open source

Platform choices

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

Standard and non-standard stat content

  • Basic probability and math stat
  • Statistical inference
  • Regression and generalized linear models
  • Statistical machine learning
  • EDA
  • Machine learning
  • Training wheel data analysis
  • Reproducible research, report generation
  • Presentations
  • Interactive graphics
  • Data munging, obtaining data
  • Plotting software
  • Capstone project
  • Actual data analysis




A friendlier way to learn R...

Self-paced and interactive

Learn R and stats, together

swirl + Coursera

Get Coursera credit, automatically

Make your own interactive content

swirlstats.com