candace thille: the science of learning, big data, technology, and transformations in education
TRANSCRIPT
Candace Thille February 26, 2015
TE C H N O L O G Y , A N D B I G D ATA I N H I G H E R ED U C AT I O N
The Science of Learning:
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 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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What are the Affordances of the Technology?
Access/ Convenience
SimulaAon/ PersonalizaAon
ConnecAon/ Crowdsourcing
OLI: Design learning activities that collect data to make the learning process visible and provide feedback for continuous improvement
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
Web-‐Based Learning and Research
Environments
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
Results
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
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
Learner Diversity
Mindset Stereotype threat
Social Psychological Science
Explanatory and Predictive Models
Testable Hypotheses
Relationships in Data
Theory Driven Cognitive Science Engagement Neuroscience Identity/mindset Metacognition Social Context
Data Driven Data Mining Machine Learning Artificial Intelligence Natural Language Statistical Modeling Network Analysis
New Open Learning Analytics System LMS
semantic tagging
learner event handling
instructor dashboard
External to LMS
skills mapping service
data visualization
service outcome analytics service
adaptive & personalized
learning
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.
DataShop: Pittsburgh Science of Learning Center
IKIT: University of Toronto
Virtuous Cycle of Continuous Improvement
Design learning
experience
Collect Data
Analyze Data
Refine Learning Theory/ models
Improvement in post secondary education will require converting teaching from a solo sport to a community-based research activity.
• Herbert Simon 1991
“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?
