Download - Adaptive E - Learning Website
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Adaptive e-Learning Web Site
Created by:
Abhinav Singh10103500Faizan Akhtar 10103597Priyam Awasthy10103635
Submitted to:
Mr. Kishore KumarMs. Arti Jain
Supervisor:Ms. Mukta Goel
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What is Artificial Intelligence??
It is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
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Why Adaptive Learning ???
What is it that you expect from a system which offers you the opportunity to learn?
Course Material
MAY BE…But is that all? What is it you are actually looking for?
Something which can guide you along your whole path. Take decisions for you. A system which interacts with you.
BUT most importantly you are looking a system which can understand you, and will change for you.
And that’s why we need an Adaptive System. A Perfect System.
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What is Fuzzy logic??
Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based
Fuzzy logic includes 0 and 1 as extreme cases of truth (or "the state of matters" or "fact") but also includes the various states of truth in between so that, for example, the result of a comparison between two things could be not “cold" or "hot" but ".38 of hotness."
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Currently all available teaching systems work on precise evaluation.
Computer can Evaluate and Manipulate Precise values while humans evolution is affected by uncertainty, vagueness & judgments.
Adaptive Learning Systems
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Human thinking process Perfect+ = Online
Computer evaluation power Tutor
We propose web based tutoring model which is combination of
Intuitionistic fuzzy set theory + mamdani algorithm
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Flow of Control1. IQ TEST
2. CALCULATION SCORE FUNCTION
3. MAMDANI CALCULATIONS
4. CATEGORIZATION OF USER
5. COURSE DISPLAY
6. TOPIC TEST
7. CALCULATION OF SCORE FUNCTION
8. MAMDANI ALGORITHM FOR NEW FUZZY VALUE
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ALGORITHMS
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Intuitionistic Fuzzy
X: set of questions.
Ta: degree of true.
Fa: degree of false.
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Intuitionistic Fuzzy
S(a) : Score function
Ci: percentage of true.
Wi: percentage of false.
Ti: Ci/100
Fi: Wi/100
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Intuitionistic Fuzzy
Intuitionistic Fuzzy Weighted Averaging (IFWA):
Every question has specific initial fuzzy value QF, true weight Tw, false weight & Fw.
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Mamdani Model
Fuzzification of Inputs.
Rules Evaluation
Defuzzification
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Mamdani Model
Fuzzification: Process of making Crisp value Fuzzy
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Mamdani Model Rules Evaluation:
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Mamdani Model
Defuzzification: Weightage Average Model
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MAMDANI STEPSStep 1. Evaluate the antecedent for each ruleCalculate degree for each crisp input
Step 2. Obtain each rule's conclusion
OR = min(d1,d2) AND = max(d1,d2)
Step 3. Aggregate conclusions
In this step we combine the outputs obtained for each rule in step 2 (obtain conclusion) into a single fuzzy set, using a fuzzy aggregation operator.
Step 4. DefuzzificationCaculating centroid
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Time and Score Functions
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The Good, The Average, The Poor
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QuestionData
Sample Questions and their Fuzzy Values
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Our Grouping
0-.34 -> POOR
.35-.75 -> AVERAGE
.76-1 -> GOOD
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Our Model D
ATA
BA
SE
User Level Analysis
User Progress Analysis
Theory Generation ‘n’ Presentation Curriculum Sequencing Adaptive Navigation Support Adaptive Presentation Problem Solving Support
Main System A.I.
Update D
atabase
FORUM
IQ TEST
Main Test
TEST
Theory
Practice
Report
User
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A look at The System
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A look at The System
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Test Report
A look at The System
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A look at The System
ProgressReport
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A look at The System
ProgressReport
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FORUM
A look at The System
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PracticeCompiler
A look at The System
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THEORY DATA
A look at The System
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A look at The System
THEORY DATA
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A look at The System
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THANK - YOU