Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Adaptive Recommendation System for MOOC Naveen Bansal M. Tech Project under the guidance of Prof. Deepak B. Phatak Computer Science & Engineering Indian Institute of Technology, Bombay

Oct 31, 2013

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system.

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system. • Components of adaptive hypermedia

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system. • Components of adaptive hypermedia • Learner modeling

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system. • Components of adaptive hypermedia • Learner modeling • Knowledge representation

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system. • Components of adaptive hypermedia • Learner modeling • Knowledge representation • Proposed system

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system. • Components of adaptive hypermedia • Learner modeling • Knowledge representation • Proposed system • Implementation status

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system. • Components of adaptive hypermedia • Learner modeling • Knowledge representation • Proposed system • Implementation status • Plan for stage 2

Adaptive Recommendation System for MOOC

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Outline

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Outline • Introduction to learning in MOOC system. • Components of adaptive hypermedia • Learner modeling • Knowledge representation • Proposed system • Implementation status • Plan for stage 2 • References

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Introduction Learning work-flow in MOOC system

Introduction

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Introduction “Learning workflow in Improvised MOOC system”

Introduction

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Introduction “Adaptive Hypermedia”

Adaptive Hypermedia: Collection of all techniques which can be used to enable adaptation in a web based application [Bru01].

Introduction

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Introduction “Adaptive Hypermedia”

Adaptive Hypermedia: Collection of all techniques which can be used to enable adaptation in a web based application [Bru01].

Figure: classification of adaptation technologies [Bru94b]

Introduction

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Introduction “Adaptive Hypermedia”

Introduction

Components of Adaptive System:

Figure: components of adaptive system [Bru94a]

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Introduction

Some important conferences/workshops/journals • UMAP: Conference on user modeling,Adaptation, and

Personalization (last-2012,Canada) • Hypertext: ACM conference hypertext and social media

(last-2011,Netherlands) • PING: Workshop on personalization in e-learning (last-2007,Greece) • UM: International conference on User Modeling (last-2003,USA) • International workshop on authoring of adaptive and adaptable

hypermedia (last-2011,USA) • International workshop on Information Heterogeneity and fusion in

recommender system(last-2010,Spain)

Introduction

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Introduction

Some of the most active people working in this field • Peter Brusilousky (school of information science, university of

Pittsburgh, home- www.sis.pitt.edu/ peterb) • Konstantina Chrysafiadi (Dept. of informatics, university of

Piraeus, [email protected]) • Maria virou(Dept. of informatics, university of Piraeus,

[email protected]) • Alenka kavcic (Faculty of computer and Information science,

University of Ljubljana)

Introduction

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Learner Modeling

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

What is Learner Modeling?

Learner Modeling

• System’s understanding about the user

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

What is Learner Modeling?

Learner Modeling

• System’s understanding about the user • Learner profile v/s Learner model

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

What is Learner Modeling?

Learner Modeling

• System’s understanding about the user • Learner profile v/s Learner model • Two types of information is stored in Learner model

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

What is Learner Modeling? • System’s understanding about the user • Learner profile v/s Learner model • Two types of information is stored in Learner model

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Classification of Learner models

Figure: classification of learner models [Bru01]

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Existing Approaches for Learner Modeling • Bayesion Belief Network • Machine Learning techniques • Neural network techniques • Fuzzy clustering techniques • Neuro Fuzzy techniques

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Bayesion Belief Network Bayesian networks are directed graphs that represents a set of random variables and their conditional dependencies [Ngu08][GSMBFPK10]. nodes represents random variables, edges represents conditional probabilities. • Knowledge: set of learning objectives • Actions: depends on learning objective • Passive • Individual active • Collective active • Evidence: • Utility: U = w1 .E1 + w2 .E2 + w3 .E3 + w4 .E4 ........ + wn .En

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Machine Learning approach

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Machine Learning approach

Learner Modeling

• A user always leaves some patterns while interacting with

Hypermedia system.

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Machine Learning approach

Learner Modeling

• A user always leaves some patterns while interacting with

Hypermedia system. • System Learns about users’ interests, habits and preferences, based

on interaction.

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Machine Learning approach

Learner Modeling

• A user always leaves some patterns while interacting with

Hypermedia system. • System Learns about users’ interests, habits and preferences, based

on interaction. • Constructs a behavior oriented learner model.

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Machine Learning approach

Learner Modeling

• A user always leaves some patterns while interacting with

Hypermedia system. • System Learns about users’ interests, habits and preferences, based

on interaction. • Constructs a behavior oriented learner model. • Used extensively in e-commerce to understand the behavior of users.

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Machine Learning approach

Learner Modeling

• A user always leaves some patterns while interacting with

Hypermedia system. • System Learns about users’ interests, habits and preferences, based

on interaction. • Constructs a behavior oriented learner model. • Used extensively in e-commerce to understand the behavior of users. • can also be used for learner classification and plan recognition in

adaptive systems.

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Limitations of Machine Learning approach • Required large amount of data e.g. click streams, search logs etc

[Bau96]. • Complexity of this approach is high (it can be managed but we can

achieve same system with low complexity )

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Neural network Method • Infer a meaningful pattern from a set of large and imprecise data [Fin96]. • provides methods to model human behavior. • J. Beck and B. Woolf attempts to model the learner by a “two

phase learning algorithm” • training phase exploits data from all the users to learn all types of

stereotypes. • learning phase learns about a given learner on the basis of the data

stored in the system.

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Fuzzy Logic Method • Fuzzy clustering • Neuro Fuzzy representation of domain

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Fuzzy Logic Method • Fuzzy clustering • Neuro Fuzzy representation of domain • Clustering is the classification of a data point into different

stereotypic clusters. • Hard clustering (non-fuzzy clustering) • Soft clustering (fuzzy clustering)

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Fuzzy Logic Method • Fuzzy clustering • Neuro Fuzzy representation of domain • Clustering is the classification of a data point into different

stereotypic clusters. • Hard clustering (non-fuzzy clustering) • Soft clustering (fuzzy clustering)

• A membership function is associated with every data point which

specifies the degree of belonging of a particular data point to a given cluster.

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Fuzzy Logic Method • Fuzzy clustering • Neuro Fuzzy representation of domain • Clustering is the classification of a data point into different

stereotypic clusters. • Hard clustering (non-fuzzy clustering) • Soft clustering (fuzzy clustering)

• A membership function is associated with every data point which

specifies the degree of belonging of a particular data point to a given cluster. • Fuzzy classification technique fuzzifies the classification of learners

in to different classes.

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Comparative Analysis Parameters for comparison

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Comparative Analysis Parameters for comparison • Computational complexity

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Comparative Analysis Parameters for comparison • Computational complexity • Dynamic modeling

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Comparative Analysis Parameters for comparison • Computational complexity • Dynamic modeling • Labeled/Unlabeled

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Comparative Analysis Parameters for comparison

Learner Modeling

• Computational complexity • Dynamic modeling • Labeled/Unlabeled • Size of training data

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Comparative Analysis Parameters for comparison

Learner Modeling

• Computational complexity • Dynamic modeling • Labeled/Unlabeled • Size of training data • Uncertainty

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Comparative Analysis Parameters for comparison

Learner Modeling

• Computational complexity • Dynamic modeling • Labeled/Unlabeled • Size of training data • Uncertainty • Noisy data

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling Comparative Analysis Technique

Fuzzy Logic Neural Networks Fuzzy clustering NeuroFuzzy

Compl- Dynamic Labeled/ exity modelUnlaing beled Med Yes N/A High Yes Both

Size of training data N/A High

Uncertanity Yes Yes

High/ Med High

No

Both

Med/High

Yes

Yes

Labeled

Med/High

Yes

Table: Characteristics of different techniques for user modeling [KY95]

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Inferences Task Prediction Recommendation Classification

Needed NeuroFuzzy NeuroFuzzy Fuzzy Logic Neuro Fuzzy

Filtering

Fuzzy Logic

Not Needed Neural Networks Neural Networks, Fuzzy clustering Neural Networks Fuzzy Clustering Neural Networks

Table: Techniques recommended for adaptation tasks [FMMCM04]

Learner Modeling

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Learner Modeling

Available adaptive systems and their charaterstics Adaptive System ELM-ART AHA! Hyperadapter Netcoach Interbook

based on knowledge YES YES YES YES YES

based on preferences YES YES -

Table: Adaptive systems based on learners characterstics [GYZ11]

Learner Modeling

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Knowledge Representation

Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Knowledge Representation • To achieve adaptation, the domain knowledge should be represented

in a manner that there should be a mapping between the adaptive system and the human expert [SKPR05].

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Knowledge Representation • To achieve adaptation, the domain knowledge should be represented

in a manner that there should be a mapping between the adaptive system and the human expert [SKPR05]. • knowledge representation must be specific to the learner modeling

technique

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Knowledge Representation • To achieve adaptation, the domain knowledge should be represented

in a manner that there should be a mapping between the adaptive system and the human expert [SKPR05]. • knowledge representation must be specific to the learner modeling

technique • knowledge can be represented in various ways

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Knowledge Representation • To achieve adaptation, the domain knowledge should be represented

in a manner that there should be a mapping between the adaptive system and the human expert [SKPR05]. • knowledge representation must be specific to the learner modeling

technique • knowledge can be represented in various ways • Initial thinking about the system • Hierarchical knowledge representation • Goal oriented connectionist knowledge representation • Fuzzy cognitive Maps(FCM)

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Initial thinking about the system • Domain is the set of concepts

Di = {c1 , c2 , c3 ......cn }

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Initial thinking about the system • Domain is the set of concepts

Di = {c1 , c2 , c3 ......cn } • Recommendation will be based on the score/concept.

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Initial thinking about the system • Domain is the set of concepts

Di = {c1 , c2 , c3 ......cn } • Recommendation will be based on the score/concept. • Initialization System will be initialized on the basis of quiz in which

there is a one to one or many relationship among the concepts and questions

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Limitations • concepts are dependent c1 = “Find the sum in a for loop” c2 = “Find the average in a for loop”

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Limitations • concepts are dependent c1 = “Find the sum in a for loop” c2 = “Find the average in a for loop” • Recommendations should be such that : • Weak learner gets incremental suggestions based on the complexity. • System should infer/skip some concepts for smart learners.

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Hierarchical knowledge representation M. Siddappa and A. S. Manjunath tried to conduct a course on “Computer Architecture” based on hierarchical representation of domain .

Figure: hierarchy based representation of domain [SM07]

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Advantage • gives the order of presentation of concepts in increasing order of

complexity • only part − of /pre − requisite relationship can be represented

efficiently

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation

Advantage • gives the order of presentation of concepts in increasing order of

complexity • only part − of /pre − requisite relationship can be represented

efficiently Limitations • other types of relationships such as related relationships among the concepts cannot be represented. • Increasing the knowledge levels of one concept can only affect the

knowledge levels of concepts in hierarchy [Mae94].

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation “Fuzzy Cognitive Maps (FCM)”

Fuzzy Cognitive Maps • In all the previous approaches, if the knowledge level of a particular

concept reaches above a threshold value then the concept will be considered as learned. • Knowledge level is not discrete e.g. “He is intelligent”,“He knows

this concept 50%” • Fuzzy logic fuzzifies the classification of users’ knowledge levels in

classes • Each point in the Fuzzy logic is associated with a membership

function which tells the degree of membership of this point to the set [Fin96].

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation “Fuzzy Cognitive Maps (FCM)”

Fuzzy Cognitive Maps • A = φ : ∀xX : µA (x) = 0 • Represent the domain in the form of graph where each node

represents a domain concept [Kav04]. • The relationship between two nodes can be of two types • essential pre-requisites(E) • supportive pre-requisites(S) • R =E ∪S

E ∩S =φ • E ⊆C ×C

µE : C n × C → [0, 1] o µE (ci ,cj ) E= : (c , c )C i j (ci ,cj )

Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation “Fuzzy Cognitive Maps (FCM)”

Fuzzy Cognitive Maps • Domain can be represented by a triplet :

GD = (C .E .S) C: set of concept nodes, E: set of essential prerequisites, S: set of supportive prerequisite • Each node is associated with a knowledge level ‘l’. • Membership functions are defined which value of fuzzifies the

classification of learner on the basis of ‘l’. • Learner knowledge about a particular concept c is therefore

expressed by providing the values of three fuzzy sets [SKPR05] (µU , µK , µL ) µU + µK + µL = 1 µU > 0 =⇒ µL = 0 µL > 0 =⇒ µU = 0 Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Knowledge Representation “Fuzzy Cognitive Maps (FCM)”

Figure: Membership Functions for fuzzy sets of unknown (CU),known(CK), and learned (CL) [VS01] Knowledge Representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC

Requirements:

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC

Requirements: • Adaptive

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC

Requirements: • Adaptive • Dynamic updation

Proposed system

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC

Requirements: • Adaptive • Dynamic updation • low computational complexity

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC

Requirements: • Adaptive • Dynamic updation • low computational complexity • Domain independent (e.g. should not need any expert to assign

weightage scores on the edges)

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC

Input to the system

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC “4-layered architecture”

Proposed system

Figure: A 4-layered architecture for Knowledge representation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC Relationship among the concept modules

Proposed system

• hierarchical relationship • non-hierarchical relationship

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Recommendation system in MOOC

Example Module id C1 C2 C3 C4 C5 C6

Concept if-else statement switch statement for loop while loop do-while loop Using conditional statements with iterative statements Table: An example lecture

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Example Concept id C1 C1 C1 C1

Objective id objp obj1 obj2 obj3

C1

obj4

C2 C2 C2 C2

objp obj1 obj2 obj3

C2

obj4

Proposed system

Description syntax of if-else statement check a given number is positive or negative check a given number is even or odd given two numbers, check which one is greater given three numbers, check which one is greater syntax of switch statement check a given number is positive or negative check a given number is even or odd given two numbers, check which one is greater given three numbers, check which one is greater

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Example Concept id C3 C3 C3 C3 C3 C3 C4 C4 C4 C4 C4 C4

Proposed system

Objective id objp obj1 obj2 obj3 obj4 obj5 objp obj1 obj2 obj3 obj4 obj5

Description syntax of the for statement print the first n integers in a for loop counting in a for loop calculating sum in a for loop calculating average in a for loop calculating max/min in a for loop syntax of the while statement print the first n integers in a while loop counting in a while loop calculating sum in a while loop calculating average in a while loop calculating max/min in a while loop

Table: An example of concept to objective table Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Example

Proposed system

C5 C5 C5 C5 C5 C5 C6

objp obj1 obj2 obj3 obj4 obj5 obj1

C6

obj2

C6

obj3

C6

obj4

syntax of the do-while statement print the first n integers in a do-while loop counting in a do-while loop calculating sum in a do-while loop calculating average in a do-while loop calculating max/min in a do-while loop print all the even numbers between 1 to 100 using print all the odd numbers divisible by 7, between 1 to 100 print all the numbers divisible by 3 or 5, between 1 to 100 print all the odd numbers divisible 3 or 5 but not 3 and 5, between 1 to 100

Table: An example of concept to objective table Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC

Proposed system

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC

Proposed system

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC

Proposed system

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC

Proposed system

Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC

State diagram of an objective

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Modeling learner • Each learner’s knowledge for a particular concept can be represented as four tuples (Un, Uk, K , L) .The membership functions for these fuzzy sets depends on knowledge level ‘x’. Membership functions for Un and Uk   1, µUn (x) = 1 − (x − 55)/5,   0,  (x − 55)/5,      1, µUK (x) = 1 − (x − 70)/5,    0,    0,

Proposed system

x ≤ 55 55 < x < 60 x ≥ 60 55 < x < 60 60 ≤ x ≤ 70 70 < x < 75 x < 55 x ≥ 75

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Modeling learner Membership functions for K and L   (x − 70)/5, 70 < x < 75      75 ≤ x ≤ 85 1, µK (x) = 1 − (x − 85)/5, 85 < x < 90   0, x ≤ 70    0, x ≥ 90   (x − 85)/5, 85 < x < 90  µL (x) = 1, 90 ≤ x ≤ 100   0, x ≤ 85

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC

Three steps to achieve adaptation • Initialization • Update the systems assumptions about the learner. • Prioritize the concept modules that needs to be recommended.

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Initialization • Initialization of the system refers to capturing initial data about the user e.g. user preferences, learning style, knowledge level about all the concept taught in the week.

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Initialization • Initialization of the system refers to capturing initial data about the user e.g. user preferences, learning style, knowledge level about all the concept taught in the week. Number of objectives achieved for Ci • Knowledge level(Ci ) = Total number of objectives in Ci

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Initialization • Initialization of the system refers to capturing initial data about the user e.g. user preferences, learning style, knowledge level about all the concept taught in the week. Number of objectives achieved for Ci • Knowledge level(Ci ) = Total number of objectives in Ci • Example

Figure: Initialization of concept nodes Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Prioritizing concept module- Approach 1 Select the concept module According to Topological sort.

Figure: Initialization of concept nodes

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Prioritizing concept module- Approach 1 Select the concept module According to Topological sort.

Figure: Initialization of concept nodes • Advantage- Select those concept modules which are affecting other

concept modules i.e. making them ready.

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Prioritizing concept module- Approach 1 Select the concept module According to Topological sort.

Figure: Initialization of concept nodes • Advantage- Select those concept modules which are affecting other

concept modules i.e. making them ready. • Limitation- Knowledge level of the concept as well as distance is

not considered i.e. we may select a concept which is already in learnt state but influencing other concept modules. Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Prioritizing concept module- Approach 2 Select the concept module having minimum distance.

Proposed system

Figure: Initialization of concept nodes

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Prioritizing concept module- Approach 2 Select the concept module having minimum distance.

Proposed system

Figure: Initialization of concept nodes • Advantage- Minimum number of concepts to be covered in order to

learn the concept.

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Prioritizing concept module- Approach 2 Select the concept module having minimum distance.

Proposed system

Figure: Initialization of concept nodes • Advantage- Minimum number of concepts to be covered in order to

learn the concept. • Limitation- Concept module having minimum distance may have

knowledge level as learned Adaptive Recommendation System for MOOC

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Proposed System for Adaptive Recommendations in MOOC Prioritizing concept module- Approach 3 unknown → unsatisfactoryKnown → known → learnt

Prioritizing concept module- Approach 4 unsatisfactoryKnown → known → learnt and then unknown

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Uniqueness of Proposed System How this approach is different then existing approaches

Proposed system

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Uniqueness of Proposed System How this approach is different then existing approaches

Proposed system

• Domain independent (Experts are not required for calculating the

dependency of one concept on other)

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Uniqueness of Proposed System How this approach is different then existing approaches

Proposed system

• Domain independent (Experts are not required for calculating the

dependency of one concept on other) • Data set is not required for learning

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Uniqueness of Proposed System How this approach is different then existing approaches

Proposed system

• Domain independent (Experts are not required for calculating the

dependency of one concept on other) • Data set is not required for learning • Low complexity and run time updation is possible

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Evaluation

Evaluation

All the possible approaches needs to be compared after implementation • In ITS community, the common practice of evaluation is Empirical

approaches. • The criteria for the evaluation of knowledge representation technique

is the mean number of times that a leaner is advised to read a domain concept, until is considered as learned. • There are statistical approaches e.g. “Independent sample T-test • Levene’s test is used according to which if the value of “Sig”

variable is higher than 0.05, then two variance are approximately equal otherwise the difference between the means are statistically significant.

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

User Interfaces

Implementation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

User Interfaces

Implementation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Use-case diagram

Implementation

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Plan for stage 2

Looking ahead

Goals: • Literature survey. • Find out the problems in the existing approaches.

Plan for Stage 2

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Plan for stage 2

Looking ahead

Goals: • Capture requirements. • SRS document. • Implementation plan

Plan for Stage 2

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Plan for stage 2

Looking ahead

Goals: • Implementation

Plan for Stage 2

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Plan for stage 2

Looking ahead

Goals: • Implementation

Plan for Stage 2

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Plan for stage 2

Looking ahead

Goals: • Make test plan. • Testing

Plan for Stage 2

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

Plan for stage 2

Looking ahead

Goals: • Resolve Issues. • Deployment • Write paper for UMAP 2014

Plan for Stage 2

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Outline Introduction Learner Modeling Knowledge Representation Proposed system Evaluation Implementation Plan for Stage 2

References I

Plan for Stage 2

Mathias Bauer. Machine learning for user modeling and plan recognition. In Proc. ICML’96 Workshop "Machine Learning meets Human Computer Interaction" 5–16, pages 5–16, 1996. Peter Brusilovsky. The construction and application of student models in intelligent tutoring systems. Computer and System Sciences International, 32(1):70–89, July 1994. Peter Brusilovsky. Student modelling and adaptivity in web based learning systems. Computer and System Sciences International, 32(1):7089, July 1994. Peter Brusilovsky. Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1-2):87–110, March 2001. Janet Finlay. Machine learning: a tool to support improved usability? In Proc. ICML’96 Workshop "Machine Learning meets Human-Computer Interaction, pages 17–28, 1996. Enrique Fras-Martnez, George Magoulas, Sherry Chen, and Robert Macredie. Recent soft computing approaches to user modeling in adaptive hypermedia. In PaulM.E. Bra and Wolfgang Nejdl, editors, Adaptive Hypermedia and Adaptive Web-Based Systems, volume 3137 of Lecture Notes in Computer Science, pages 104–114. Springer Berlin Heidelberg, 2004. Sergio Gutierrez-Santos, Jaime Mayor-Berzal, Carmen Fernandez-Panadero, and Carlos Delgado Kloos. Authoring of probabilistic sequencing in adaptive hypermedia with bayesian networks. 16(19):2801–2820, oct 2010.

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References II

Plan for Stage 2

M.A. Ghazal, M.M. Yusof, and N.A.M. Zin. Adaptive educational hypermedia system using cognitive style approach: Challenges and opportunities. In Electrical Engineering and Informatics (ICEEI), 2011 International Conference on, pages 1–6, 2011. A. Kavcic. Fuzzy user modeling for adaptation in educational hypermedia. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 34(4):439–449, 2004. George J. Klir and Bo Yuan. Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1995. Pattie Maes. Agents that reduce work and information overload. Commun. ACM, 37(7):30–40, July 1994. Viet Anh Nguyen. Constructing a bayesian belief network to generate learning path in adaptive hypermedia system. Computer Science and Cybernetics, 24(1):12–19, 2008. Wojciech Stach, Lukasz Kurgan, Witold Pedrycz, and Marek Reformat. Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 153(3):371 – 401, 2005. M. Siddappa and A. S. Manjunath. Knowledge representation using multilevel hierarchical model in intelligent tutoring system. In Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology, ACST’07, pages 323–329, Anaheim, CA, USA, 2007. ACTA Press.

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References III

Plan for Stage 2

S. Vrettos and A. Stafylopatis. A fuzzy rule-based agent for web retrieval-filtering. In Ning Zhong, Yiju Yao, Jiming Liu, and Setsuo Ohsuga, editors, Web Intelligence: Research and Development, volume 2198 of Lecture Notes in Computer Science, pages 448–453. Springer Berlin Heidelberg, 2001.

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