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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 Learni...
Goal of the Science
of Learning:
To Describe, Explain and Predict Student Learning
Pasteur’s Quadrant
Stokes argues basic/applied goals need not trade off
Low	
  Emphasis	
  on	
  
Applied	
  Work	
  
High...
01010101110101000101010100010101000101010100101000000101010101011101010
00101010100010101000101010100101000000101001010101...
OLI: Design learning
activities that collect data to
make the learning process
visible and provide feedback
for continuous...
Feedback to the Learner
Goal directed practice & targeted feedback
enhances the quality of students’ learning
Feedback to the Instructor
Feedback to the Design Team
Team Based Design & Development
Feedback to the Learning
Researchers
Methods
Randomized	
  Trials	
   Classroom	
  Studies	
  
Best of Both Worlds
§ Combine theory and
practice to develop
effective educational
technology and refine
learning theory
...
Pasteur’s Quadrant
Stokes argues basic/applied goals need not trade off
Low	
  Emphasis	
  on	
  
Applied	
  Work	
  
High...
Results
Some Limits In OLI v1.0:
•  Creative Commons License but on a closed system.
•  Limited authoring or adaptation tools 
•  ...
Cognitive Science
▪ OLIv1
▪ guided learning
▪ complex cognitive tasks
▪ targeted hints & feedback
▪ background knowledge
▪ relevant skills
▪ future goals
Learner Diversity
▪ background knowledge
▪ relevant skills
▪ future goals
▪ attributions—how
learners explain the
causes of
experiences
Lear...
Mindset
Stereotype threat
Social Psychological Science
Explanatory and Predictive Models
Testable Hypotheses
Relationships in Data
Theory Driven
Cognitive Science
Engagement Neu...
New Open Learning Analytics System
LMS
semantictagging
learnereventhandling
instructor
dashboard
External to LMS
skillsmap...
Open Data and Data Formats
Share Alike and Share Data
(This doesn’t exist, but we think it should.)
Build and promote comm...
DataShop: Pittsburgh Science of Learning Center
IKIT: University of Toronto
Virtuous Cycle of Continuous Improvement
Design	
  
learning	
  
experience	
  
Collect	
  Data	
  
Analyze	
  
Data	
  	
...
Improvement in post secondary
education will require converting
teaching from a solo sport to a
community-based research a...
“Without a complete
revolution…in our
approach to teaching…
we cannot go beyond
(current levels) of
productivity” William ...
cthille@stanford.edu
 Candace Thille: The Science of Learning,  Big Data, Technology, and Transformations in Education
 Candace Thille: The Science of Learning,  Big Data, Technology, and Transformations in Education
 Candace Thille: The Science of Learning,  Big Data, Technology, and Transformations in Education
 Candace Thille: The Science of Learning,  Big Data, Technology, and Transformations in Education
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Candace Thille: The Science of Learning, Big Data, Technology, and Transformations in Education

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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.

Published in: Education
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Candace Thille: The Science of Learning, Big Data, Technology, and Transformations in Education

  1. 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. 2. Goal of the Science of Learning: To Describe, Explain and Predict Student Learning
  3. 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  
  4. 4. 01010101110101000101010100010101000101010100101000000101010101011101010 00101010100010101000101010100101000000101001010101110101000101010100010 101000101010100   What are the Affordances of the Technology? Access/   Convenience   SimulaAon/   PersonalizaAon   ConnecAon/   Crowdsourcing  
  5. 5. OLI: Design learning activities that collect data to make the learning process visible and provide feedback for continuous improvement
  6. 6. Feedback to the Learner
  7. 7. Goal directed practice & targeted feedback enhances the quality of students’ learning
  8. 8. Feedback to the Instructor
  9. 9. Feedback to the Design Team
  10. 10. Team Based Design & Development
  11. 11. Feedback to the Learning Researchers
  12. 12. Methods Randomized  Trials   Classroom  Studies  
  13. 13. Best of Both Worlds § Combine theory and practice to develop effective educational technology and refine learning theory Web-­‐Based   Learning  and   Research   Environments  
  14. 14. 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  
  15. 15. Results
  16. 16. 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
  17. 17. Cognitive Science ▪ OLIv1 ▪ guided learning ▪ complex cognitive tasks ▪ targeted hints & feedback
  18. 18. ▪ background knowledge ▪ relevant skills ▪ future goals Learner Diversity
  19. 19. ▪ background knowledge ▪ relevant skills ▪ future goals ▪ attributions—how learners explain the causes of experiences Learner Diversity
  20. 20. Mindset Stereotype threat Social Psychological Science
  21. 21. 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
  22. 22. New Open Learning Analytics System LMS semantictagging learnereventhandling instructor dashboard External to LMS skillsmapping service data visualization service outcome analytics service adaptive& personalized learning
  23. 23. 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.
  24. 24. DataShop: Pittsburgh Science of Learning Center IKIT: University of Toronto
  25. 25. Virtuous Cycle of Continuous Improvement Design   learning   experience   Collect  Data   Analyze   Data     Refine   Learning   Theory/   models  
  26. 26. Improvement in post secondary education will require converting teaching from a solo sport to a community-based research activity. • Herbert Simon 1991
  27. 27. “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?  
  28. 28. cthille@stanford.edu

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