Learning about Causal Systems in Complex Domains
A Multidisciplinary Synthesis

Principal Investigators

 N. Hari Narayanan, Auburn University
Ashok Goel, Georgia Tech
Cindy Hmelo-Silver, Rutgers University
Sadhana Puntambekar, University of Wisconsin Madison
 

Summary
Advisory Board
PI Meetings
Reports
Bibliography
Workshop

Understanding causal systems is a significant aspect of learning in almost all disciplines of science, engineering and technology. However, this is a difficult task, one which often leads to misconceptions because many aspects of such systems are dynamic, emergent, invisible, and interdependent. Furthermore, the spatio-temporal structure of causal chains can be complex and confusing for learners. Not surprisingly, students find it hard to comprehend, explain, make predictions about, operate or troubleshoot systems in causal domains. Research on fundamental aspects of causal systems, mechanisms of causal understanding, ways to improve the learning and application of causal models remains fragmented, discipline-specific, and published in disparate forums. As a result, a global view of the state-of-the-art and research challenges of complex causal learning is currently unavailable. Addressing this lacuna is the main objective of this project.

As part of this NSF-funded multi-university project, we are 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 is an effort that draws from multiple disciplines, including but not limited to cognitive science, computer science, engineering education, psychology, and science education. Original objectives of the project are:

  • conducting a review of the state-of-the-art and producing a bibliography and synthesis report
  • organizing a workshop to discuss research issues and challenges, and how to bring research results into practice, and
  • developing and submitting research proposals, including but not limited to a possible SLC proposal, on various aspects of complex causal learning,

If your research is relevant, we invite you to contribute information to the review by sending one or more of the following to the contact listed below.

  • A brief description of research activities and results
  • A list of publications
  • Web sites where research is described and publications are listed
  • Electronic or hard copies of significant papers
All relevant research will be included in the survey, your contributions will be acknowledged, and you will be informed about the workshop as details are finalized.

The rest of this web site is currently unavailable due to updating in progress.

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Contact:
N. Hari Narayanan
Computer Science & Software Engineering
107 Dunstan Hall, Auburn University
Auburn, AL 36849-5347, USA
(T) +1 334 844-6312 (F) +1 334 844-6329 (E) naraynh AT auburn dot edu