Dealing with many of the world’s most pressing problems requires an ability to understand and reason about causal complexity. For example, understanding climate change involves reasoning about non-obvious causes, spatial gaps, temporal delays, cyclic causality, and distributed causality where the agency/intentionality of one’s actions are on a different level than those of the emergent outcome. In the Understandings of Consequence Lab, we study causal cognition and learning in a complex world.
We pursue the following kinds of questions:
- What are the inherent default patterns or assumptions that we make as human beings that influence how we reason about complexity in our world?
- What are the characteristics of human cognition that lead to these default patterns?
- In what ways do these patterns help or hurt us when reasoning about causal complexity?
- How can we help people to use their cognition well in reasoning about causal complexity?
- How can we educate tomorrow’s learners to reason well about a complex world and to be able to solve the difficult problems that they will face?
Our work has deep implications for policy and practice. We work with experts in the sciences and beyond to impact policy. We also collaborate with teachers to develop curriculum and approaches to teaching the next generation to reason well about causal complexity.
The following projects are part of the Causal Learning corpus of work:
CAREER: Learning About Complex Causality in the Classroom (07/01/09-12/31/15), NSF#0845632, PI: Tina Grotzer
In the past decade, there has been a growing interest in how children reason about the nature of causality which suggests that children are capable of understanding complex causality to a greater extent than earlier research suggested. Yet paradoxically, students’ misconceptions in science have been linked to students’ difficulties reasoning about complex causal forms. This project looks at what students are capable of learning over time in contexts designed to support learning about causal complexity. This research project studied student learning of three complex causal concepts that are important to many science concepts—distributed causality, probabilistic causality, and action at a distance across multiple sessions over the course of a school year. It included two research phases: 1) Microgenetic studies of causal learning over time in supported contexts and; 2) Learning about the nature of causality in curriculum contexts. We developed and tested instructional scaffolds that showed promsing results in helping students learn the causal concepts.
Learning to RECAST Students’ Causal Assumptions in Science Through Interactive, Multimedia Professional Development Tools
(07/01/05- 4/30/11), NSF #ESI-0455664, PI: Tina Grotzer
Understanding the nature of causality is critical to learning a range of science concepts from “everyday science” to the science of complexity. Our earlier research established that students hold default assumptions about the nature of causality that hinder their science learning and that curriculum designed to restructure students’ causal assumptions while learning the science leads to deeper understanding. Here, the UC team and the Science Media Group (SMG) of the Harvard-Smithsonian Center for Astrophysics collaborated in a five-year iterative design process to create on-line professional development tools to help teachers use the Causal Patterns in Science curriculum and pedagogy well. The tools guide middle school physics and biology teachers in assessing the structure of their students’ scientific explanations and in developing curriculum to restructure or RECAST students’ understandings to fit with scientifically accepted explanations. The resulting website is available at:http://www,pz.harvard.edu/ucp/causalpatternsinscience.Click here for a summary of research findings.
Extending the Understandings of Consequence Project: Investigating Transfer and Persistence
(07/01/01 -06/30/06) NSF #REC-0106988, Co-PIs: Tina Grotzer and David Perkins
Scientifically accepted explanations require students to structure knowledge in ways that often contradict their expectations about the nature of how causes and effects behave. Such explanations can involve: causal mechanisms that are inferred or abstract; causal patterns that extend beyond linear and unidirectional to cyclic, reciprocal, and non-sequential; correspondences between causes and effects that are in various respects probabilistic; and causal agents that are decentralized and involve aspects of emergence. These are ways of thinking and abstractions students typically are not familiar with. “The Understandings of Consequence Project” revealed that students and scientists’ explanations tend to have very different types of causal structures at the core. The project demonstrated that impacting students’ assumptions about the nature of causality is a promising approach for helping students restructure their knowledge and achieve scientific understandings. In this project, we assessed the transfer of understanding of causal forms to topics with isomorphic and non-isomorphic causal forms and to science learning more generally. Persistence of learning was examined later in the same school year and again two years later. The findings resulted in revisions to the Understandings of Consequence curriculum units. Click here for a summary of research findings.
The Challenge of Developing Systems Thinkers: How Misconceptions about Complex Causality Contribute to Fundamental Problems in Scientific Learning (07/01/98- 06/30/02) NSF #REC-9725502, Co-PIs: Tina Grotzer and David Perkins
Learning to understand and analyze the systems concepts present in many different scientific phenomena entails interpreting a variety of types of complex causal relationships. Yet students tend to hold preconceptions and misconceptions about causality that hinder learning and systematically generate misconceptions in science content. In this project we studied the assumptions that students brought to their learning in ecosystems, density, air pressure and other concepts. We considered mismatches between students' and scientific models to identify and examine points of difficulty. We worked with teachers to develop and assess intervention materials designed to help students beyond these points of difficulty. This work resulted in a list of default assumptions, a taxonomy of causal models, and an approach to teaching the underlying causality called RECASTing (which includes RECAST activities and discussions.) It also led to the initial versions of the Causal Patterns in Science Curriculum. Click here for a summary of research findings.
The work on this site has been supported by the National Science Foundation. All opinions, findings, conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the National Science Foundation.
The images below are from the June 27, 2019 IAP Workshop where participants brainstormed examples of each of the forms and features of causal complexity as outlined in the chart in the book, Learning Causality in a Complex World: Understandings of Consequence: