Global Change, vr and Learning

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VII. Learning, VR and Global Change

There are three parts to this section. In the first, we identify what students need to achieve cognitively in order to learn about global change. In the second, we summarize those attributes of VR that support learning. In the third section, we revisit the five “challenges” for learning about global change, outlined above, in order to illustrate how VR might be used to teach about complex science material.

Summary: Cognitive Requirements for Learning about Global Change

In order to learn complex content, as exemplified by global change, students need to achieve the following skills and abilities:

Fluency with symbol systems

Students need to learn to recognize objects that represent concrete objects in the real world. (This is not always as easy as it seems in VEs given the current low level resolution of most HMDs.) They must learn the metaphors used to represent abstract concepts and principles regardless of the medium through which they are learning. They must internalize the symbol system so that its interpretation and use are automatic and do not intrude on learning content.

Discrimination ability

Students must learn the fine discriminations of color, size and shape that characterize changes in the perceptible environment as the result of changes in global warming.

Concept recognition

Students must learn to correctly classify the phenomena they observe.

Application of principles

Students must learn, through guided induction, the processes that operate in the environment that reflect global change.

Accurate mental models

As a consequence, students should develop valid mental models that contain the concepts and principles of global change. These models may be idiosyncratic provided they remain within the bounds of “correct science”.


Students’ mental models should be contextualized so that they can be applied in settings other than the VE in which they were acquired. These settings may include the purely academic, when, for example, students are required to take exams or write essays about global change. They may also include the real world, where students find themselves having to explain to others about global change, to the purely practical, where the behavior of students vis-‡-vis the environment is appropriate and thoughtful, and where their criticism and direction of others’ behavior is likewise reflective of what they have learned.


Students need to be motivated to learn about global change and motivated to apply what they have learned.


Students must remember what they have learned.

Problem solving skill

Students must develop the ability to define the problems they need to solve, to gather data about global change from whatever environment they are working within, to interpret those data and to draw inferences that lead them to testable hypotheses, to test those hypotheses and determine their tenability.

Summary: Relevant attributes of VR

The attributes of VEs that are likely to foster the above cognitive requirements may be summarized as follows:

Data representation

VEs can represent data about any imaginable phenomenon in any imaginable format.

Social learning and collaboration

VEs are becoming multi-participant making it possible for discussion and interaction to take place inside the VE.

Immediacy of feedback

The consequences of student actions are immediately apparent.

Appropriateness of feedback

The feedback the student receives (none, knowledge of results, correction of errors) can be adapted to fit the students’ learning styles and current mastery of content.

User control over VE itself

Students may interactively modify the nature and behavior of VEs. They can even build their own VEs to embody their own understanding of particular phenomena.

Nature of interactions

Interactions between the user and the VE may vary. They can be direct, as when a student action directly impacts on some aspect of the VE. They can be mediated by other users, as when one student is delegated by a group to perform certain tasks within the VE. They can be mediated by language, where a student communicates indirectly with the VE. Or they can be mediated by an agent within the VE who acts on behalf of the student.

Multisensory experience

VEs provide visual, auditory and to some extent haptic and tactile experiences.


There are of course constraints that need to be borne in mind when making decisions about using VEs. A trade-off between these and the added value of VEs is an important action to take. These constraints include: Cost, difficulty of running immersive VEs on currently available equipment, difficulty of maintaining hardware and software, lack of curricular materials, and the high cost of developing materials.

Using VR to Learn about Global Change

Five “challenges” to learning about global change were presented in an earlier section on learning about global change. These were a sample of the kinds of issues that face students and teachers of this material. We return to these now and suggest how VR might be used to help students meet each challenge. Again, these suggestions are a small sample of potential ideas.

Conceptualization of relevant processes

Many of the processes at work within the phenomenon of global change cannot be sensed by humans. Teachers face significant problems about how to visualize these processes. Many of the cause-effect relationships are counter-intuitive.

VEs, whether immersive or not, are particularly effective at visualizing abstract processes. Once these processes are accessible to students’ senses, the necessary discriminations can be made, the concepts assigned to their appropriate categories and the principles that link them understood. The counter-intuitiveness of processes as observed without understanding can be mastered by iterative observation, hypothesis-building and testing hypotheses in a VE. All of the potential of immersive VEs for developing this scientific problem-solving skill and for applying it once developed come into play.
Importance of Scale

Global change operates locally, globally and at scales in between. Many students have difficulty understanding how global change’s local manifestations, like declines in salmon stock, may have global causes and impact far beyond the student’s local environment.

Simulations that allow easy transition from local to global points of view can make it easier for them to understand the relationships among local and global phenomena. The ability of VEs to allow students to instantaneously change points of view may be useful here.
Natural Variability, Human Impact and Global Change

Global climate has changed over the earth’s history before humans had any impact on the environment. Today, there is evidence of human impact. Students need to understand the extent to which global change is a natural phenomenon and the extent to which it is affected by human activity.

In a VE, students can make observations and measurements related to global change at any point in history. They can travel forward in time to see what the future holds with or without changing the amount of pollution humans put into the atmosphere. With a reasonably accurate database, they can also travel back in time to observe and measure climatic fluctuations throughout earth’s history. They will observe radical changes in climate during the ice ages and shifts in composition of the earth’s atmosphere during the planet’s early evolution and at subsequent periods. From these observations made in the future and in the past, they will be able at least to reason about the relative contributions of natural cycles and human impact to global change without necessarily “coming up with the right answers”.
Predicting Global Change

We have previously talked about students making predictions about where global change will lead us and testing those predictions in VEs. We now emphasize the difficulties environmental scientists themselves have in making these predictions. The difficulty arises from the complexity and incompleteness of the numerical models scientists use for this purpose and the difficulty that non-specialists have understanding them.

The issue of prediction from models is an important part of the environmental education curriculum. Having students use modeling software to build their own models from theory and data, to build simulations that run the models and make predications, and to build their own VEs that allow easier interaction with and observation of the models’ effects will help students develop an appreciation of the difficulty of predicting global change.
Common Misconceptions

Students come to science classes with preconceptions about global change. These are often misconceptions derived from at best oversimplified and at worst incorrect accounts of the phenomena in the popular media. Provided misconceptions make intuitive sense – they mostly do --, then students’ mental models for global change will be robust and resistance to change even in the face of compelling evidence.

Working in VEs allows students to develop ownership of what they study. The evidence they derive from observation and problem-solving in the VE is theirs, not evidence provided by a teacher, a textbook or an expert. It is therefore more likely to be believed. The ability to manipulate VEs in radical ways also allows students to gather information in “what if” scenarios that are beyond the power of scientists to explore in the real world. As we saw above, VEs can still be understandable and useable even when they do not oversimplify the content. It is therefore likely that misconceptions about global change can be replaced by accurate understanding by learning in VEs.
Personal Points of View

Finally, science education aims at students taking personal responsibility for their actions on the environment. Students who understand the critical problems facing our planet are sincerely distressed by them. Using simulations so that students may see the future consequences of their actions or of their inaction can help them become more responsible citizens. Doing so within a compelling virtual environment will likely heighten their motivation to act responsibly.

VIII. Recommendations for Research

In this section, we provide a list of research questions, hypotheses and related issues that arose from the two workshops. Since the preceding sections of the report provide a framework and rationale for these, and since they are a distillation from a significant amount of debate, we present them without discussion.

We assumed that a basic goal of science education is to develop students’ appreciation of science in all its complexity and to increase students’ ability to use the scientific method. The following research questions address this goal.

Research questions

A question whose answer must preempt the answers to all of the following questions is: “What is wrong with the way students learn about global change and science generally using the methods and technologies we currently have?” If it turns out that in answering this question we discover that we are doing perfectly well now, then it will be difficult to justify investment in VR.

On the assumption that there is still room for improving science education, we suggest the following research questions.

Can We Increase Motivation?

Does VR help increase students’ motivation to learn in the short and long term? Why or why not? Is VR measurably better than current technologies and strategies for increasing motivation?

What is the relationship of engagement to motivation? How do VR effects and the technical means to achieve them relate to engagement? What is the combined effect? Does VR create a better level of engagement than other methods? How do these apply to different topics?

Can We Help Students Make Sense of Real World?

What aspects of the “real world” do we want students to comprehend? Does VR facilitate the acquisition of knowledge (facts, concepts, principles, etc.) better than current methods?

What is the measurable benefit of making the invisible visible, the impossible possible and the abstract concrete?
Does VR address phenomena that are inherently 3D better than current methods?
How closely do the models have to resemble reality and behave according to students’ expectations in order for learning to take place?
Does VR help students to build trust in their observations and reasoning and to overcome misconceptions better than current methods?

Does VR Help Students Build Mental Models?

How does VR facilitate the building of mental models? Is VR more intuitive for developing understanding than other methods?

Does VR help integrate the sciences with other disciplines and encourage collaboration better than current methods?
Does VR increase metacognitive ability?

Can VR Develop Ability to Synthesize, Abstract, Predict and Transfer?

Does VR help students improve the use of their mental models to synthesize their existing knowledge, abstract to similar situations, predict outcomes and hence transfer their skills and knowledge to a similar situation or domain?

Can VR Increase Retention Rate

Does VR help students retain skills and knowledge better than current methods? Does VR provide improved methods for feedback?

More General Questions

What have we learned or can we learn from where VR has been successfully used elsewhere (e.g. in training for pilots, surgeons, in battlefield simulations; in providing entertainment; in doing 3D design)?

Given that the interface devices are still not very intuitive and sometimes even get in the way, how do we design a research program whose findings about learning are not likely to be confounded with interface issues?
What are the key problems in K-12 science education? How does VR help address those issues?

What is the best instructional role for augmented reality? What is the role of augmented reality in science education?

What content areas (or types of learning outcome) are best learned with VR?
Can we introduce relatively complex science topics earlier into the curriculum (at lower grade levels) if students learn about them in VR?
What are the misconceptions that VR is best used for correcting?
What are new misconceptions that VR is likely to create? How do we avoid creating them?
How does the level of immersion in a VE affect learning outcomes?
Do VEs best support part-to-whole or whole-to-part models of teaching?
What is the value of role-playing in VEs?
Will the affordance of anonymity and the use of character templates in VEs make it possible for students to overcome stereotypes?
Can VR be used to embed educational content in an enticing form within a narrative context?
When should VEs present concrete experiences and when abstractions?

Sample Testable Hypotheses (How we might answer the research questions.)
VR leads to greater improvement on divided attention tasks than single, forced attention tasks.
Students retain what they discover in VEs longer and better than they do discoveries made in other ways.
Exposure to VR improves students’ spatial reasoning and spatial orientation abilities.
Students constructing and inhabiting 3D immersive VEs representing global change acquire and retain greater understanding of the system parameters and are able to synthesize and predict future outcomes better than students who use the best non-immersive methods for world building and viewing.
A learner who experiences multiple levels of immersion will learn more.
A learner who experiences multiple levels of immersion will learn more about global-local interactions.
A learner will trust experiences in VEs sufficiently to modify misconceptions.
Interacting with other students in a VE will improve learning.
Student assessment of and performance in VR is positively related to complexity, fantasy and degree of realism.
Manipulating the system is more effective for learning than not manipulating the system.
Having a student participate actively in the behavior of a VE will lead to greater understanding of certain concepts and principles than having them observe the VE from within, which will in turn be more effective than having them observe from without.
Students who learn science in VEs will have a more positive attitude towards science and technology.

Independent Variables to Manipulate Across Technologies and Treatments

Population type (based on age, gender, ethnicity, mental and physical disabilities, etc.).

Levels of immersion and concomitant ability to manipulate space, time, scale, frames of reference (ego- vs. exocentric).
Extent of use of spatialized sound, force feedback, etc.
Extent of the ability to communicate, role-play.
Cohesiveness and obviousness of story-line.
Relative emphasis on entertainment value.
Sense of ownership operationally defined as building vs. visiting worlds.
Science expertise of the teacher.

Other issues affecting research and implementation of technology in schools

Time to learn and use technology effectively in classroom, to integrate curriculum to meet goals.

Cost to acquire, maintain, network and update systems.
Flexibility to meet different needs of teachers in various disciplines and ability levels.
Workshop Attendees.

Dan Barstow

Janice DeCosmo

University of Washington, WA Sea Grant
Chris Dede

National Science Foundation

Daniel Edelson

Northwestern University

Richard Edgerton

Seattle School District

Tom Furness

University of Washington, HITL

Ken Galluppi


Bob Gotwals

Shodor Educational Foundation

Bill Hastie

Washington State University, Olympia

Kathleen Heidenreich

Local Science Educator

Jeff Hendricks

Stanwood Middle School

Hunter Hoffman

University of Washington, HITL

Earl Hunt

University of Washington, Psychology

Ronald Kantor

University of Houston, Clear Lake

Walter Keenan

Bob Kozma

Stanford Research Institute
Lynn Liben

Penn State University

Chien Liang “Jonathan” Liu

Washington State University

Bowen Loftin

University of Houston

Miles Logsdon

University of Washington, Oceanography

Beverly Lynds

Patricia Morse

National Science Foundation
Michael Moshell

University of Central Florida

Joan Piper

Museum of Flight

Howard Rose

Firsthand LLC

Nora Sabelli

National Science Foundation

Perry Samson

University of Michigan

Tim Schmidt

Stanwood Middle School

Jim Slotta

UC Berkeley

John Smith

University of Washington, Education

Mike Spranger

University of Washington, WA Sea Grant

Mark Stoermer

University of Washington, APL

Trav Stratton

Pacific Northwest National Labs

Bill Winn

University of Washington

Raul Zaritsky

National Center for Supercomputing Applications, U. of Illinois

NSF Workshop Report 1/30/98

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