0. A Cognitive Perspective on Integrated STEM (iSTEM) Learning
Eli M. Silk (University of Michigan)
Christian D. Schunn (University of Pittsburgh)
September 27, 2011
Presentation of a draft commissioned report for the National Academy of Engineering and National Research Council’s committee on determining the value of integrated K-12 STEM (iSTEM) education in the United States.

1. Cognitive Perspective on Learning?
Find learning limited by constraints on the mind
Knowledge helps, especially organized knowledge
Tradeoffs between situated and general knowledge
Value mechanisms explaining what is difficult
Factors that affect how learner encodes (interprets, stores) experiences
Factors that affect how learner retrieves (connects, applies) experiences
Focus on measures of understanding and learning
Controlled experimental contrasts
Rich descriptions of students’ progression of ideas
Outside our scope but interesting: interest, self-efficacy, value
Too much text?
iSTEM - 9/27/11
Silk & Schunn

2. iSTEM Empirical Patterns
Overall small positive learning benefit of iSTEM (d~=.3)
Deep level conceptual change is rare
(vs. how-to knowledge)
Some asymmetry between disciplines
(e.g., Science d > math d)
iSTEM - 9/27/11
Silk & Schunn

3. Useful Theoretical Frameworks
Knowledge Integration Perspective
Knowledge as a “repertoire of ideas”
Learning as connecting, sorting, reprioritizing
Easy to add normative ideas, hard to reorganize around them
Structure Mapping Theory
Knowledge as objects, features, relations
Learning as attending to deep structure
Surface features compete with deep features
iSTEM - 9/27/11
Silk & Schunn

4. Implications 1
Various organizations of past meta-analyses may not be the best ways to assess iSTEM efforts
May be large variation dependent upon details of implementation
What instructional scaffolds are in place for …
… connecting disciplinary ideas with experience-based ideas?
… attending to the deep structure of richly-contextualized problems?
iSTEM - 9/27/11
Silk & Schunn

5. Implications 2 Suggestions Outcomes measures (DVs) Predictors (IVs):
(more consistently) Separate measures for learning each discipline
Measure basic knowledge plus deep concepts and transfer
Predictors (IVs):
Level of integration between experience and discipline
0 – normative ideas only
1 – evoke experience-based ideas
2 – evoke and reinterpret experience-based ideas using normative, disciplinary ideas
Role of abstract and concrete details (cross-disciplinary?)
0 – abstract ideas + practice
1 – concrete problems w/ heavy detail
2 – concrete problems w/ light detail
3 – moves back and forth between abstract and concrete
iSTEM - 9/27/11
Silk & Schunn

6. Eli M. Silk esilk@pitt.edu Christian D. Schunn schunn@pitt.edu
Thank You
Eli M. Silk
Christian D. Schunn

7. Backup Slides

8. iSTEM - 9/27/11
Silk & Schunn