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National Science Foundation Award #0350342

SLC Catalyst: Learning About Causal Systems in Complex Domains

Investigator(s):

James Cross (PI) ; Ashok Goel (Co-PI) ; Cindy Hmelo-Silver (Co-PI) ; Teresa Hubscher-Younger (Co-PI) ; Sadhana Puntambekar (Co-PI)

Sponsor:

Auburn University, AL 36849 3348444438

Start Date/Expiration Date

2003-10-01 to 2006-03-31 (amended 2004-12-23)

Awarded Amount to Date:

$193,916

Abstract: Understanding causal systems is a significant aspect of learning in almost all disciplines of science, engineering and technology. However, understanding causal systems is difficult, and often leads to misconceptions because many aspects of such systems are dynamic, invisible, and interdependent. Complex causal domains can be characterized in a domain-independent and general manner in terms of structure and causal propagation of component behaviors in spatial and temporal dimensions. The need to comprehend and integrate structure and causal propagation in multiple dimensions may be the source of many of the difficulties that learners face in understanding, explaining, making predictions about, operating or troubleshooting systems in causal domains. Clearly, research under the rubric "Science of Learning" ought to address the topic of complex causal learning. At present, research on fundamental aspects of causal systems, mechanisms of causal understanding, ways to improve the learning and application of causal models, etc. remains fragmented, discipline-specific, and published in disparate forums. As a result, a global view of the state-of-research and research avenues to advance our understanding of learning about complex causal systems is simply not available. Therefore, we propose to undertake a research synthesis effort. Its goals are twofold: developing a prospective synthesis of the state-of-research on complex causal learning across disciplines, and identifying gaps in the knowledge base that past research has built up. This will be an effort that draws from multiple disciplines, including but not limited to cognitive science, computer science, engineering education, machine learning, psychology, and science education. The outcomes of this catalyst project will be a workshop on complex causal learning and a comprehensive research report on the topic. The intellectual merit of this project lies in its development and dissemination of a comprehensive research report on the current knowledge base on complex causal learning, a topic of interest and importance to K-16 education and learning research in several scientific and technical disciplines. Such a reference resource is at present not available to researchers and educators. The broad impacts of this project will be in raising awareness and focusing the attention of the learning research community on the topic of complex causal learning, and in cross-disciplinary learning research that the dissemination of our research report can potentially encourage. The results of this project, especially the research challenges it identifies, are expected to form the basis of a future proposal for a Science of Learning Center on complex causal learning.

NSF Org:

SBE - Directorate for Social, Behavioral & Economic Sciences

Award Number:

0350342

Award Instrument:

Standard Grant

Program Manager:

Kenneth C. Whang
SBE Directorate for Social, Behavioral & Economic Sciences
SBE Directorate for Social, Behavioral & Economic Sciences

NSF Program(s):

SCIENCE OF LEARN CTR-CATALYSTS

Field Application(s):

Program Reference Code(s):

EXP PROG TO STIM COMP RES, 9150
UNASSIGNED, 0000

Program Element Code(s):

7277