Working with Data: A Learning Sciences Perspective

1. Working with Data: A Learning Sciences Perspective Daniel C. Edelson WorldWatcher Project School of Education & Social Policy andComputer Science Department Northwestern University Supported by the National Science Foundation under grants no. REC-9730377, REC-9720663, ESI-9720687, and REC-0087751

2. Collaborators • Faculty: Louis Gomez, Roy Pea, Brian Reiser, Bruce Sherin • Research Scientists: Duane Griffin, Michael Taber, Susan Marshall • Graduate students: Douglas Gordin, Matthew Brown, Gabrielle Matese, Virginia Pitts • Curriculum development professionals: Kylene Chinsio, Adam Tarnoff, Michael Lach, Kathleen Schwille • Programmers: Eric Russell, Peter Moore, Brian Clark

3. The Big Disconnects in Education • Current methods do not match our goals • Current education emphasizes • Memorization of facts • Passive reception of information (listening, reading) • Practicing simple skills out of context • We want citizens that can • Perform complex tasks • Gather and synthesize information • Communicate with others

4. The Big Disconnects in Education (2) • Current methods do not match what people do in the real world: • For example, science is... • Asking questions. • Constructing explanations. • Collecting evidence. • Engaging in a dialogue (arguing, listening, asking). • Applying knowledge to meaningful problems.

5. The Big Disconnects in Education (3) • Current methods do not match what we know about learning: • Students must be engaged. • Content (facts) and Process (skills) learning must be integrated. • Students are not blank slates. Prior knowledge and outside influences must be accounted for. • Things learned divorced from meaningful context does not transfer to contexts where they are useful.

6. So, why do we want students to work with data? • Provide an authentic experience of science • Build skills (representative, quantitative, analytical) • Allow them to explore content phenomena in ways that… • Supplement other experiences of those phenomena • Allow them to explore phenomena on scales too large or too small to be experienced directly

7. The challenges of enabling students to work with data… • Availability (the easy one) • Accessibility for students and teachers • of tools • of data • Design of effective learning activities • There’s a learning sciences research and development program here: • Tool design • Data library design • Learning activity design 2:00

8. An approach to design of learning activities: Learning-for-Use • The Goal • help students to develop “useful” knowledge—knowledge that will be retrieved and applied when relevant in the future • Model of Learning • describes how useful knowledge can be developed. • based on research from cognitive science • Design Framework • provides guidelines for teachers and designers • fosters useful understanding

9. Learning-for-Use Model (from JRST 2001) • Underlying principles from cognitive science research (e.g., How People Learn): • Learning takes place through the construction and modification of knowledge structures. • Knowledge construction is a goal-directed process that is guided by a combination of conscious and unconscious understanding goals. • The circumstances in which knowledge is constructed and subsequently used determine its accessibility for future use. • Knowledge must be constructed in a form that supports use before it can be applied.

10. Three stages in Learning-for-Use • Motivate specific learning objectives …based on perceived need for and usefulness of knowledge or skills • Construct knowledge …from experience and instruction • Organize knowledge for use …for accessibility (retrieval) and usability (application)

11. Motivate

12. Construct Understanding

13. Organize for Use

14. What does LfU look like for each learning objective? Motivate Create Demand or Elicit Curiosity Reflect Balance of direct experience, indirect experience, modeling, instruction, and explanation Construct Reflect Organize Practice Apply Reflect

15. Motivate Motivate Motivate Construct Construct Organize Organize Construct Organize LfU for related learning objectives Aggregate objective Learning Objective 1 Learning Objective N Unit level Activity level

16. Motivate Motivate Construct Construct Organize Organize LfU for related learning objectives Learning Objective 1 Learning Objective 1(a) Motivate Learning Objective 1(b) = Construct = Organize

17. Where does working with data fit in? • Motivate • Elicit curiosity: observe surprising patterns in data (discrepant event) • Construct • Experience: Learn about phenomenon by seeing patterns in data • Organize • Apply: Use what has been learned to explain or predict patterns in data

18. Scenario-based inquiry learning • A specific form of Learning-for-Use A scenario (or project) provides the context that: • Creates demand • Provides opportunity to apply new knowledge A substantial portion of knowledge construction occurs through inquiry.

19. An example: Planetary Forecaster • 6-8 week middle school unit • Content objectives: relationship between physical geography and temperature • Latitude (curvature) • Time of year (tilt) • Land/Water (specific heat) • Elevation (pressure/density) • Process objectives: • data visualization and analysis • hypothesis formation and revision • argument from evidence

20. Create Demand: A Letter from the International Space Agency • Scientists have discovered a new planet that is very similar to Earth. • We want to plan a mission to colonize it… • Which portions of it would be habitable? • Students study relationship between physical geography and climate on Earth to forecast climate for Planet Y.

21. Structure of the project • Develop list of initial hypotheses from activity that elicits students’ prior conceptions. • Of those, investigate three factors: • Latitude • Land/Water • Elevation • Repeating sequence of: • Find pattern in Earth data • Investigate pattern in hands-on lab • Quantify pattern in Earth data • Apply it to “planetary forecast”

22. Curvature • Study Earth: • Average surface temperature • Incoming solar energy • Lab: • Penlight area on paper as angle changes

23. Tilt • Study Earth: • Seasonal temperatures • Seasonal insolation • Lab: • Penlights on tilted globes

24. Land/Water • Investigate Earth: • Land averages • Water averages …at same latitudes • Lab: Soil vs. H2O heating under shop lamp and cooling

25. Elevation • Investigating Earth: • Elevation and Temperature • Lab: • Rapid expansion (e.g., aerosol can)

26. Final Planetary Forecast July 8:00

27. Planetary Forecaster and Learning-for-Use • Motivate • Create demand based on planetary forecast scenario • Construct • Direct experience through inquiry activities • Explanation through instruction • Organize • Reflection through frequent discussions • Application in the context of planetary forecast • Use of data: To support inquiry activities and to support application task.

28. Take away • Learning Sciences research indicates need for complete learning process. • The current system tends to focus on knowledge construction to the detriment of other two steps. • Earth and Environmental Science problems can provide meaningful context for learning-for-use through scenario-based inquiry learning. • Activities that use data can contribute to all three stages of learning.

29. More info • The WorldWatcher Project — http://www.worldwatcher.org Supported in part by the National Science Foundation under grants no. RED-9453715 , ESI-9720687, DGE-9714534. • Affiliated with: • The Center for Learning Technologies in Urban Schools (LeTUS) — http://www.letus.org • The Center for Instructional Materials in Science (CIMS) http://www.sciencematerialscenter.org • Planetary Forecaster: http://www.letus.northwestern.edu/projects/pf