EcoMOD: Integrating Computational Thinking in Ecosystem Science Education via Modeling in Immersive Virtual Worlds (9/15/2016-8/31/19), NSF #DRL1639545, PIs: Chris Dede, Tina Grotzer and Karen Brennan.
The EcoMOD (Model/Modify, Observe, Design) project will explore the power of immersive virtual environments to support computational thinking and ecosystem science learning in elementary grades. Research shows that, with appropriate scaffolding, even young students can begin building complex causal concepts and understandings of systems dynamics. Developing more advanced scientific and computational thinking in later grades depends on creating a strong foundation in elementary school. However, important questions remain unanswered about how young learners think about models. EcoMOD engages learners in observation and exploration of a complex systems model based on a simulated forest building upon assets developed in an earlier project called EcoMUVE. EcoMOD’s learning goals, related to ecosystem science topics like food webs, will be taught using a systems perspective, and will shift the focus from comprehension of static representations to student interaction with dynamic computational models. Students will explore model elements through a programming sandbox, and will see the effects as they modify the properties and behaviors of the system through programming. EcoMOD will link multiple representations to help connect visual models to dynamic representations of ecosystem relationships. The curriculum will provide a highly supported, object-oriented programming environment customized to focus on ecosystems modeling and designed specifically for younger children.
EcoXPT: Affordances for Experimentation in an Immersive World to Support Learning of Ecosystems Science and Complex Causality (9/1/2014-8/31/17), NSF #DRL1416781, PIs: Tina Grotzer and Chris Dede
Understanding how ecosystems work is important for citizens in making decisions and for students who aspire to become scientists. It requires understanding of complex causality, possible unintended consequences, and the strengths and limitations of various investigative approaches. Ecosystem concepts are difficult to learn and to teach due to the amount of information, many interacting components, and non-linear patterns involved. They are particularly difficult to teach in classrooms because ecosystems involve complexities such as large-scale problems, populations of organisms, and change over extended time frames. Learning when and how ecosystem scientists employ different approaches can help learners understand the content and process of science, yet it remains challenging to meaningfully teach these concepts in schools. EcoXPT builds upon earlier work with EcoMUVE, but goes beyond observational inquiry to explore the diverse investigative strategies practiced in the field of ecosystems science, through adding tools modeled on modern approaches and integrated with iterative cycles of experimentation, reflection, and revision.
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.
EcoMOBILE: Blended Real and Virtual Immersive Experiences for Learning Complex Causality and Ecosystems Science (9/1/2011-8/31/15), NSF DrK121118530, Co-PIs: Chris Dede and Tina Grotzer
EcoMOBILE (Ecosystems Mobile Outdoor Blended Immersive Learning Environment) was an extension of an earlier project called EcoMUVE and the resulting curriculum, developed at the Harvard Graduate School of Education with funding from the Institute of Education Sciences. In EcoMUVE, students explore a virtual representation of a pond ecosystem. In EcoMobile, funded by the National Science Foundation and Qualcomm's Wireless Reach initiative, students use the EcoMUVE software and also extend their learning with mobile technologies through one or more field trips to a local pond environment. Two forms of technology for science education enhanced their experience in the real world.
Advancing Ecosystems Science Education via Situated Collaborative Learning in Multi-User Virtual Environments
(07/01/08- 06/30/11) IES#R305A080514, Co-PIs: Chris Dede and Tina Grotzer
Ecosystems science, an important strand of the life science content standards, requires an understanding of complex causal relationships. However, even after instruction, students often retain inaccurate interpretations about ecosystems’ structural patterns and systemic causality. With the research team of colleague Chris Dede, an expert in virtual worlds, we developed a Multi-User Virtual Environment (MUVE)-based ecosystems science curriculum called EcoMUVE to address these problems. EcoMUVE includes two ecosystems science curricular modules (pond and forest) for teaching various aspects of ecosystems science, These MUVE modules complement and extend the current curriculum of the Understandings of Consequence Project’s Causal Patterns in Ecosystems curriculum.
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.