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Student satisfaction fuzzy_logic

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Student Satisfaction using Fuzzy Logic in Matlab

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Page 1: Student satisfaction fuzzy_logic

Student Satisfaction

using Fuzzy LogicResult and Analysis

-AMIT SINGH DAHAL

-G5638545

Page 2: Student satisfaction fuzzy_logic

Overview:

Introduction

Fuzzy System

Fuzzy Model

Input and Output Parameters

Rules

Conclusion and Analysis

Page 3: Student satisfaction fuzzy_logic

Introduction:

Objective:

-To know the Quality and Consistency of the University

-Hopefully improve in future based on the student’s evaluation

-student’s satisfaction on 7 Items:

- Education Experience

-Teachers

-Services and Facilities

-Service Staff

-Student Life

-Reputation of Faculty & University

-Student Engagement

Page 4: Student satisfaction fuzzy_logic

Introduction:

-ask questionnaires on these 7 Items

-Each Items comprises of some questions

-Each questions has a rating of 0-10

-Students gives rating based on their experience and thinking

-After getting the ratings in each Item, we use the aggregation technique

to get the crisp value for each Items and convert it out of 100

- Now, after getting the rating in 100, then our next step is to use fuzzy

logic and implement, since the values differ from each students

Page 5: Student satisfaction fuzzy_logic

Fuzzy System:

Applying membership

function in fuzzification

Create and use fuzzy rule

sets

Use one of the method(Ex:

Centroid Method) for

defuzification

Page 6: Student satisfaction fuzzy_logic

Fuzzy Model:

Used the Fuzzy ToolBox

provided by Matlab

7.12.0.635

Mamdani Fuzzy Inference

System for fuzzification

Triangular membership for

Input and Output

parameters

Centroid Method used for

deffuzification

Page 7: Student satisfaction fuzzy_logic

Input and Output Parameter:

Page 8: Student satisfaction fuzzy_logic

Input Parameter:

Page 9: Student satisfaction fuzzy_logic

Input Parameter:

Page 10: Student satisfaction fuzzy_logic

Output Parameter:

Page 11: Student satisfaction fuzzy_logic

Output Parameter:

Page 12: Student satisfaction fuzzy_logic

Rules:

our input variable is 7

and output variable

one with three

linguistic values each

altogether 3^7 i.e.

2187 rules

filtered and randomly

selected 63 rules

Page 13: Student satisfaction fuzzy_logic

Conclusion and Analysis:

Page 14: Student satisfaction fuzzy_logic

Conclusion and Analysis:

Gathered ratings from around 180 students from 1st to 4th year ICT

The overall of satisfaction got from the ICT student is 71.4

The satisfaction is more than average

When simply calculating statistically, we get 72.76, hence we can

conclude that Fuzzy Logic can be implemented to such satisfactory

results

As the satisfaction is over average, the services and other facilities

do not need to be upgraded for some years

But we should note, the output results directly depends on the rules

hence, many analysis should be done while defining rules

Page 15: Student satisfaction fuzzy_logic

THANK YOU…

ANY QUESTIONS???