Making a meaningful
difference: Leveraging data
to improve learning for most
of people most of the time
LASI 2014 Keynote,...
Outline
 The Future of Learning
 Getting there
 Case Study: Intelligent tutoring system (2009-2013)
 Skipped technical...
The Future of Learning
 Recognizing and promoting excellence in teaching and
learning
 Non-intrusive model to recognize ...
The Future of Learning
 Blurring the boundary between teachers and
learners
 Learner promoted to become tutors and conte...
Getting There
 Achievements
 Knowledge models portable, sharable, transparent to
students
 Integrate with learning syst...
Getting There
 Relationships
 Detecting features that predict effective
teacher/learner or peer tutor/mentee relationshi...
Case Study
 CAREER: Educational Data Mining for Student
Support in Interactive Learning Environments
 NSF-IIS (2009-2013...
Technical Methodology
 Data-derived model tracer
 Graph answer of students
 Calculate transition probabilities
 Reward...
What types of tutors?
 General: Problem solving
 Maths. (Algebra, Geometry, Logic, Induction)
 Science (Chemistry and P...
10
11
How to Generate a Hint Sequence
 To generate hints
 Suggest the next state with the highest value
 Generate hints from ...
How Often Will a Hint Be Available?
 Experiment of four semesters of past data
 523 valid student attempts
 381 (73%) w...
14
How to Determinate Master Learning?
 Assume that students who complete a system
have mastered it
 Break down the system ...
16
Guiding Principles
 Respect
 Personalized models and adaptive contents
 “People can offer to the learning environment a...
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Tiffany Barnes "Making a meaningful difference: Leveraging data to improve learning for most of people most of the time"

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Transcript of "Tiffany Barnes "Making a meaningful difference: Leveraging data to improve learning for most of people most of the time""

  1. 1. Making a meaningful difference: Leveraging data to improve learning for most of people most of the time LASI 2014 Keynote, Dr.Tiffany Barnes, NCSU (Presenter: Dr. Chi-Un Lei, HKU) 1
  2. 2. Outline  The Future of Learning  Getting there  Case Study: Intelligent tutoring system (2009-2013)  Skipped technical discussions  Guiding Principles 2
  3. 3. The Future of Learning  Recognizing and promoting excellence in teaching and learning  Non-intrusive model to recognize mastery, commitment, engagement, mentoring and teaching potential in learners  Combined with detectors that recognize student needs  Real time support for effective culture  Identified when/where potential collaborators are working on similar tasks and pairs them according to maximum likelihood of a beneficial peer learning relationship  Hints on how the interaction can be most helpful
  4. 4. The Future of Learning  Blurring the boundary between teachers and learners  Learner promoted to become tutors and content creators  Knowledge modeling to constantly maintain flow during learning while detecting the needs of learning 4
  5. 5. Getting There  Achievements  Knowledge models portable, sharable, transparent to students  Integrate with learning systems like those with games  Detectors constantly updating achievements  Diverse learning environments  Forum, wikis, labs, assessments, tutorials, readings  EDM models informed from all
  6. 6. Getting There  Relationships  Detecting features that predict effective teacher/learner or peer tutor/mentee relationships  Providing scaffolds to continually support these  Focused around learning activities of current interest to users  But allowing for off-task activities that strengthen relationships and recognize that learners and teachers are people 6
  7. 7. Case Study  CAREER: Educational Data Mining for Student Support in Interactive Learning Environments  NSF-IIS (2009-2013)  Intelligent tutoring system  Use student data to construct models that represent student solutions  Trace student behavior in the model  Provide feedback and hints based on past records from students 7
  8. 8. Technical Methodology  Data-derived model tracer  Graph answer of students  Calculate transition probabilities  Reward good solutions and penalize errors  What does this give us?  Likely paths students take  A value for each state - This value is important  Use to provide help in the form of hints 8
  9. 9. What types of tutors?  General: Problem solving  Maths. (Algebra, Geometry, Logic, Induction)  Science (Chemistry and Physics)  Language and reading  Field test: Over 200 students per year  Discrete Math  Logic and Algorithms  Students have difficulty developing strategies to solve proofs 9
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  12. 12. How to Generate a Hint Sequence  To generate hints  Suggest the next state with the highest value  Generate hints from the state features of that state  To create a hint sequence  Indicate a goal expression to derive  Indicate the statements that should be used  Indicate the rule to apply next 12
  13. 13. How Often Will a Hint Be Available?  Experiment of four semesters of past data  523 valid student attempts  381 (73%) were complete, 142 (27%) were partially complete  Over all steps, hints only available for 45% of moves  However, 90% of Hint Requests were successful  More problems were completed with hints 13
  14. 14. 14
  15. 15. How to Determinate Master Learning?  Assume that students who complete a system have mastered it  Break down the system data into intervals  Model learning at the end of each interval  Compare new students to exemplar model to determine mastery 15
  16. 16. 16
  17. 17. Guiding Principles  Respect  Personalized models and adaptive contents  “People can offer to the learning environment and to one another”  Beneficence  Look for practical effect sizes  Move towards standardized data models and methods  Maximize potential of research to result in positive changes in educational systems  Justice  Consider equality in developing and deploying systems  Many ways to demonstrate (and measure) proficiency/mastery

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