Fostering conceptual and cognitive change in learners can be difficult. Students often come to a learning situation with robust, implicit understandings of the material under study. One explanation for the implicit nature of these understandings is a lack of metaknowledge about the knowledge to be acquired. Helping learners create metaknowledge may free paths to conceptual change. This paper proposes the use of fuzzy cognitive maps [FCMs] as a tool for creating metaknowledge and exploring hidden implications of a learner’s understanding. Two specific educational applications of FCMs are explored in detail and recommendations are included for further investigations within educational contexts.

1. EXIMUS 2013
Rule Based Fuzzy Cognitive Mapping:
Applications in Education
Presented By,
Naveen H
Najashree
USN
4SF10EC062
4SF10EC061
Sahyadri college of engineering and management.

2. CONTENTS
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Abstract
Introduction
Fuzzy Logic
Fuzzy cognitive maps
Fuzzy and crisp sets
Concept mapping
Causality and FCMs
Conclusion
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

3. ABSTRACT

Fostering conceptual and cognitive change.

Creating metaknowledge and exploring hidden
implications of a learner’s understanding.

‘‘So far as the laws of mathematics refer to
reality, they are not certain. And so far as they
are certain, they do not refer to reality.’’
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

4. INTRODUCTION
 Fuzzy
Cognitive Mapping(FCM) is a tool for
formalizing understandings of conceptual
and casual relationships.
 FCMs
in educational organization.
 FCM
combine the strength of cognitive
maps with fuzzy logic.
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

5. FUZZY LOGIC

Embeds human knowledge into working algorithms.

highly complex systems whose behaviors are not
well understood.

If-Then rules are the examples of structured
knowledge.

Important tool in generic decision making.
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

6. FUZZY SETS

Fuzzy Sets can represent the degree to which a
quality is possessed.

Fuzzy Sets (Simple Fuzzy Variables) have values in
the range of [0,1]
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

7. FUZZY COGNITIVE MAPS
•
Fuzzy logic is a system for representing
uncertainty, or possibility.
EXAMPLES OF FCMS:
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

8. FUZZY LOGIC SYSTEM

Fuzzification: transforming crisp values into
linguistic terms of fuzzy sets.

Inference mechanism: Interprets the values in the
input vector and, based on user-defined rules,
assigns values to the output vector. Usually based
on if-then rules.

De-Fuzzification: conversion of fuzzy output to
crisp
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

9. FUZZY AND CRISP SETS
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

10. CONCEPT MAPPING

Graphical representations of a specified conceptual
domain.

Include some aspect of subjectivity.

Representation of the perceptions and beliefs of a
decision maker

Represent a form of distributed intelligence.
 structure activity
 save mental work
 avoid error.
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

11. FUZZY COGNITIVE MAP

12. FUZZY LINGUISTIC VARIABLES
Fuzzy Linguistic Variables are used to represent
qualities spanning a particular spectrum
Social :{police vigilance, theft } ;

Fuzzy cognitive mapping, Sahyadri college of engineering and management.

13. Police
vigilance
Theft
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

14. CLASSIFICATION OF FUZZY INFERENCE
METHODS
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

15. MAMDANI'S METHOD

Step 1: Evaluate the antecedent for each rule.

Step 2: Obtain each rule's conclusion.

Step 3: Aggregate conclusions.

Step 4: Defuzzification.
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

16. CAUSALITY AND FCMS

The relation between causes and effects

Fuzzy logic allows the representation of fuzzy concepts
and degree of causality.

FCMs are a tool for representing a dynamic process
and modeling the process in real-time.

FCMs is the temporal and causal nature.
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

17. ADVANTAGES
1.
FCMs capture more information in the
relationships between concepts.
2. FCMs are dynamic.
3. FCMs express hidden relationships.
4. FCMs are combinable.
5. FCMs are tunable.
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

18. CONCLUSION

Potential usefulness of fuzzy cognitive mapping
in educational organization settings.

Combining the capability of fuzzy logic to represent soft
knowledge domains with dynamic modeling.

Stimulates educational research to recognize the
unique applicability of fuzzy logic to our field.
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

19. REFERENCES
Jason R. Cole, Kay A. Persichitte, Fuzzy Cognitive
Mapping, University of Northern Colorado,
 Kosko B. Fuzzy cognitive maps. Int J Man-Mach
Stud 1986;24:65]75.
 Gardner H. The unschooled mind: How children
learn and how schools should teach.
 João Paulo Carvalho José A. B. Tomé Rule Based
Fuzzy Cognitive Maps and Fuzzy Cognitive Maps –
A Comparative StudyINESC - Instituto de
Engenharia de Sistemas e Computadores


20. Queries ?
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

21. Thank You
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

22. Backup
slides
Fuzzy cognitive mapping, Sahyadri college of engineering and management.

23. THE END