Will technology change the way we teach and learn? Join Professor Thille for an engaging discussion on technology and the science of learning. She’ll share what we’ve learned from open online courses and what this means for higher education.

1. Candace Thille
February 26, 2015
TE C H N O L O G Y , A N D BI G DATA I N HI G H E R ED U C AT I O N
The Science of Learning:

2. Goal of the Science
of Learning:
To Describe, Explain and Predict Student Learning

3. Pasteur’s Quadrant
Stokes argues basic/applied goals need not trade off
Low
Emphasis
on
Applied
Work
High
Emphasis
on
Applied
Work
High
Emphasis
on
Basic
Science
How
to
translate
to
real
world?
(Bohr)
(Pasteur)
Low
Emphasis
on
Basic
Science
What
principle
can
be
derived?
(Edison)
J
X

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00101010100010101000101010100101000000101001010101110101000101010100010
101000101010100
What are the Affordances of the
Technology?
Access/
Convenience
SimulaAon/
PersonalizaAon
ConnecAon/
Crowdsourcing

5. OLI: Design learning
activities that collect data to
make the learning process
visible and provide feedback
for continuous improvement

6. Feedback to the Learner

7. Goal directed practice & targeted feedback
enhances the quality of students’ learning

8. Feedback to the Instructor

13. Feedback to the Design Team

14. Team Based Design & Development

15. Feedback to the Learning
Researchers

16. Methods
Randomized
Trials
Classroom
Studies

17. Best of Both Worlds
§ Combine theory and
practice to develop
effective educational
technology and refine
learning theory
Web-­‐Based
Learning
and
Research
Environments

18. Pasteur’s Quadrant
Stokes argues basic/applied goals need not trade off
Low
Emphasis
on
Applied
Work
High
Emphasis
on
Applied
Work
High
Emphasis
on
Basic
Science
How
to
translate
to
real
world?
(Bohr)
(Pasteur)
Low
Emphasis
on
Basic
Science
What
principle
can
be
derived?
(Edison)
X

19. Results

20. Some Limits In OLI v1.0:
• Creative Commons License but on a closed system.
• Limited authoring or adaptation tools
• Limited visual representations for data driven
improvement
• Learning Science = Cognitive Science
• Focus on individual learner
• Primarily STEM courses

21. Cognitive Science
▪ OLIv1
▪ guided learning
▪ complex cognitive tasks
▪ targeted hints & feedback

22. ▪ background knowledge
▪ relevant skills
▪ future goals
Learner Diversity

23. ▪ background knowledge
▪ relevant skills
▪ future goals
▪ attributions—how
learners explain the
causes of
experiences
Learner Diversity

24. Mindset
Stereotype threat
Social Psychological Science

25. Explanatory and Predictive Models
Testable Hypotheses
Relationships in Data
Theory Driven
Cognitive Science
Engagement NeuroscienceIdentity/mindset
Metacognition Social Context
Data Driven
Data Mining
Machine Learning Artificial IntelligenceNatural Language
Statistical Modeling Network Analysis

26. New Open Learning Analytics System
LMS
semantictagging
learnereventhandling
instructor
dashboard
External to LMS
skillsmapping
service
data
visualization
service
outcome
analytics
service
adaptive&
personalized
learning

27. Open Data and Data Formats
Share Alike and Share Data
(This doesn’t exist, but we think it should.)
Build and promote communities of research.

28. DataShop: Pittsburgh Science of Learning Center
IKIT: University of Toronto

29. Virtuous Cycle of Continuous Improvement
Design
learning
experience
Collect
Data
Analyze
Data
Refine
Learning
Theory/
models

30. Improvement in post secondary
education will require converting
teaching from a solo sport to a
community-based research activity.
• Herbert Simon 1991

31. “Without a complete
revolution…in our
approach to teaching…
we cannot go beyond
(current levels) of
productivity” William Baumol, 1967
Our
Message:
Such
a
revolu<on
is
possible
happening
Our
Ques<on:
Who
will
lead
it?

32. cthille@stanford.edu