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. Outline
 The Future of Learning
 Getting there
 Case Study: Intelligent tutoring system (2009-2013)
 Skipped technical discussions
 Guiding Principles
2

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. 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. 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. 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. 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. 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. 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

10. 10

11. 11

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. 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

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

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