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doi:10.1016/j.intcom.2005.10.007 
Copyright © 2005 Elsevier B.V. All rights reserved.
An interactive computer graphics interface for the introduction of fuzzy inference in environmental education
Received 23 November 2004;
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Abstract
Fuzzy logic is based on sets of rules that can be easily understood by the students, since they bear a close resemblance to natural language. The introduction of fuzzy logic, within the framework of Environmental Education, is considered to be necessary in order to provide an insight to the complex environmental interactions. Fuzzy inference is introduced in this paper as an extension of hypothetico-predictive argumentation and it allows the investigation of alternative hypotheses. This is achieved through the development of an interactive computer graphics environment that encompasses a set of fuzzy logic analysis tools and a fuzzy inference model of a lake. The fuzzy model guarantees the scientific integrity of the simulation results, and the graphical interface presents to the students only the comprehensible characteristics of the environmental stressors in the ecosystem of the lake. The proposed graphical interface was developed in successive design stages, with the active participation of the students. The results of the students' experimentation with the graphical interface indicate that their comprehension of the significant environmental problems of the lake is considerably improved and some misconceptions are resolved. Thus, it is considered valuable as an aid to environmental education.
Keywords: Interactive interface; Fuzzy logic; Environmental education
Article Outline
- 1. Introduction
- 2. Fuzzy inference and hypothetico-predictive reasoning
- 3. Fuzzy modelling of the ecosystem of lake Koronia
- 4. Interactive graphical environments and learning
- 5. Design methodology
- 5.1. First stage: paper drawing prototypes
- 5.2. Second stage: simple computer prototypes
- 5.3. Third stage: graphical interface design
- 5.4. Participants
- 6. Implementation
- 7. Results of the students' experimentation and analysis
- 7.1. Increasing the impact of stressors
- 7.2. Restoring actions
- 7.3. Experimenting with actual data
- 7.4. Understanding fuzzy rules
- 7.5. Overall improvement
- 8. Conclusions
- Appendix A. Appendix
- A.1. Category 1
- A.2. Category 2
- A.3. Category 3
- A.4. Category 4
- A.5. Category 5
- References
