Global Change, vr and Learning

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Human influence on global change

It is important to understand the complexity and variability inherent in the natural system. Human influence on the system, then, may be understood as a mechanism that may drive the system to a new equilibrium state.

Humans as part of the natural system

Through natural life processes, humans participate in the biochemical cycles that constitute elements of the earth system. Other human activities also produce profound effects on the earth system. In particular, industrialization, agricultural practices and human population growth have significantly affected the earth.

Atmospheric composition and human influence

Industrialization and agricultural practices have caused significant changes in the concentrations of trace gases in the atmosphere. Some of these gases are contributors to the atmospheric greenhouse effect, which is a natural process that keeps our planet within the “habitable zone” for life. However, as the concentrations of these gases increase through human influence, the energy balance of the system is significantly affected, and many feedback mechanisms are set in motion.

Other gases and particulates produced by human activity affect the suitability of the lower atmosphere for sustaining plant and animal life (acid rain, ozone and particulate concentrations). They also affect the upper atmosphere’s ability to filter out solar ultraviolet radiation through depletion of stratospheric ozone and the energy balance of the earth system through increased reflection of solar radiation by microscopic particles.
Human impact on land cover

Human influence on land cover through clearing of forests, burning of agricultural debris, development of urban areas, re-routing of rivers, etc. has profoundly affected earth’s energy balance and availability of resources.

Population growth and natural resource consumption

Humans require natural resources to thrive. Population growth strains natural resources such as energy and water. Continued growth, and unequal consumption of resources affects the earth system and our quality of life.

Assessment of change and predictions for the future

How do we assess global change? This is a complex issue involving analysis of the aggregate of change at local and intermediate spatial scales, averaged over suitable time periods. It necessitates an understanding of the natural variability of the system. To analyze such a complex system, computer models are created to reflect current understanding of the relevant processes and to keep track of the effects of feedback mechanisms.

Natural variability of the system

To understand when global change is occurring, we must characterize the level of natural variability inherent in the system. An unusually hot summer on a hemispheric scale may be a natural departure from “normal” - several of these (how many?) in a row, however, may constitute the hemispheric response to global change.

Use of global models to analyze and predict change

Global atmosphere-ocean-land models are run on supercomputers to analyze past climates and to predict global change. These models represent the most sophisticated tools we have, however, they are approximations of reality and are limited by the current state of knowledge and availability of data. Analysis of model-produced data is also complex, since many feedback responses resulting from model iterations may not be well-understood even by the creators of the model representations.

Scientific predictions and societal decision-making

Global models are used to make the best predictions we can about global change. These results inform policy-makers and citizens about action that may be taken to alleviate/moderate predicted effects. However, no model is a perfect replica of reality, and decision-makers must recognize the evolutionary nature of scientific knowledge. Decision-makers strive to balance probable outcomes with the effort required for implementation of action plans.

IV. Attributes of Virtual Reality
Autonomy, Presence, Interaction
According to Zeltzer, instructional technologies may vary in the extent to which they are autonomous, induce presence, and allow meaningful interaction between user and system. Autonomy refers to the extent to which the VE is capable of performing its own actions without intervention from or attention to the user. A VE that rates high on this dimension would be dynamic, following its own paths to goals. The user’s intervention might or might not change the course of events. High-fidelity real-time simulators are examples of autonomous VEs. A simulated oil spill might move inexorably to a pollution disaster in spite of a student’s attempts to prevent it. The key to creating autonomy in a VE lies in the goodness of the computational models that create system’s actions and govern its behavior.
Presence is the experience the user has of being in a real place when visiting a virtual one. It depends on the effectiveness of the physical interface. For presence to be high, the interface must allow the user to interact with the VE in natural, intuitive ways. Any environment conveys to people who visit it ways of interacting with it that are intrinsic to it and therefore appear natural – its “affordances”. Thus, a virtual thermometer in a VE should be as “graspable”, manipulable and useful as a real one. And the ability to use the virtual thermometer should be intuitively evident to the user. This clearly requires a great deal of attention to and success in interface design. But when successful, presence is high and the sense that the user is interacting with a computer at all disappears. The interface itself vanishes.
The third dimension is interaction. While the physical interface and the presence it engenders may be improved by interactions involving the natural affordances of the VE, we are concerned here with the logical interface to the VE. This means the extent to which the VE responds correctly to the student’s actions. For example, a VE that simulates smog in a large city must correctly simulate the causes that the student may manipulate and the effects the student may observe. When the student enacts policy that requires cars to be non-polluting, the smog level must be seen to decrease.
Variation in a simulation’s autonomy, presence and interaction is likely to have a profound effect on both the information the VE conveys and what the student learns from it. Research into the effectiveness of VR and other learning environments for learning must pay careful attention to these three factors.

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