adaptive recommendation system for mooc

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

Adaptive Recommendation System for MOOC

Naveen Bansal

M. Tech Projectunder the guidance of

Prof. Deepak B. Phatak

Computer Science & EngineeringIndian Institute of Technology, Bombay

Oct 31, 2013

Adaptive Recommendation System for MOOC 1

Outline

• Introduction to learning in MOOC system.

• Components of adaptive hypermedia• Learner modeling• Knowledge representation

• Proposed system

• Implementation status

• Plan for stage 2

• References

Outline Adaptive Recommendation System for MOOC 2

Outline

• Proposed system

• References

Outline

• Components of adaptive hypermedia

• Learner modeling• Knowledge representation

• Proposed system

• References

Outline

• Proposed system

• References

Outline

• Proposed system

• References

Outline

• Proposed system

• References

Outline

• Proposed system

• References

Outline

• Proposed system

• References

Introduction

Learning work-flow in MOOC system

Introduction Adaptive Recommendation System for MOOC 3

Introduction“Learning workflow in Improvised MOOC system”

Figure: Learning in Improvised MOOC systemIntroduction Adaptive Recommendation System for MOOC 4

Introduction

Personalized Feedback

• Participants need feedback about the concepts taught, before theend of a given week.

• Feedback should be personalized to his/her performance.

• Feedback should be in the form of recommended tasks.

• Recommended tasks must not be redundant.

• e.g Task - “You may not know how to calculate the greatest numberamong three numbers using ternary operator”

Introduction

Personalized Feedback

• Participants need feedback about the concepts taught, before theend of a given week.

• Feedback should be personalized to his/her performance.

• Feedback should be in the form of recommended tasks.

• Recommended tasks must not be redundant.

• e.g Task - “You may not know how to calculate the greatest numberamong three numbers using ternary operator”

Introduction“Adaptive Hypermedia”

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

Figure: classification of adaptation technologies [Bru94b]

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

Figure: classification of adaptation technologies [Bru94b]

Components of Adaptive Hypermedia System:

Figure: components of adaptive system [Bru94a]

Introduction

Some important conferences/workshops/journals

• UMAP: Conference on user modeling,Adaptation, andPersonalization (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 adaptablehypermedia (last-2011,USA)

• International workshop on Information Heterogeneity and fusion inrecommender system(last-2010,Spain)

Introduction

Some of the most active people working in this field

• Peter Brusilousky (school of information science, university ofPittsburgh, home- www.sis.pitt.edu/ peterb)

• Konstantina Chrysafiadi (Dept. of informatics, university ofPiraeus, [email protected])

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

• Alenka kavcic (Faculty of computer and Information science,University of Ljubljana)

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 Adaptive Recommendation System for MOOC 11

Learner Modeling

Classification of Learner models

Figure: classification of learner models [Bru01]

Learner Modeling

Existing Approaches for Learner Modeling

• Bayesion Belief Network

• Machine Learning techniques

• Neural network techniques

• Fuzzy clustering techniques

Learner Modeling

Bayesion Belief Network

Bayesian networks are directed graphs that represents a set of randomvariables and their conditional dependencies [Ngu08][GSMBFPK10].nodes represents random variables, edges represents conditionalprobabilities.

• 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

Machine Learning approach

• A user always leaves some patterns while interacting withHypermedia system.

• System Learns about users’ interests, habits and preferences, basedon 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 inadaptive systems.

Learner Modeling

Machine Learning approach in Adaptive Hypermedia

• Required large amount of data e.g. click streams, search logs etc[Bau96].

• Complexity of this approach is high.

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 “twophase learning algorithm”

• training phase exploits data from all the users to learn all types ofstereotypes.

• learning phase learns about a given learner on the basis of the datastored in the system.

Learner Modeling

Fuzzy Logic Method

• Fuzzy clustering

• Neuro Fuzzy representation of domain

• Clustering is the classification of a data point into differentstereotypic clusters.

• Hard clustering (non-fuzzy clustering)• Soft clustering (fuzzy clustering)

• A membership function is associated with every data point whichspecifies the degree of belonging of a particular data point to a givencluster.

• Fuzzy classification technique fuzzifies the classification of learnersin to different classes.

Learner Modeling

Fuzzy Logic Method

Learner Modeling

Fuzzy Logic Method

Learner Modeling

Fuzzy Logic Method

Learner Modeling

Comparative Analysis

Parameters for comparison

• Computational complexity

• Dynamic adaptability

• Labeled/Unlabeled

• Size of training data

• Uncertainty

• Noisy data

Learner Modeling

• Uncertainty

• Noisy data

Learner Modeling

• Uncertainty

• Noisy data

Learner Modeling

• Uncertainty

• Noisy data

Learner Modeling

• Uncertainty

• Noisy data

Learner Modeling

• Uncertainty

• Noisy data

Learner Modeling

• Uncertainty

• Noisy data

Learner Modeling

Technique Compl-exity

Dynamicadapt-ability

Labeled/Unla-beled

Size oftrainingdata

Fuzzy Logic Med Yes N/A N/ANeural Net-works

High Yes Both High

Fuzzy clus-tering

High/Med

No Both Med/High

Neuro-Fuzzy

High Yes Labeled Med/High

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

Learner Modeling

Inferences

Task Needed Not NeededPrediction NeuroFuzzy Neural NetworksRecommendation Fuzzy Logic Neural Networks,

Fuzzy clusteringClassification Neuro Fuzzy Neural Networks

Fuzzy ClusteringFiltering Fuzzy Logic Neural Networks

Table: Techniques recommended for adaptation tasks in adaptiveHypermedia(ITS)[FMMCM04]

Learner Modeling

Available adaptive systems and their charaterstics

Adaptive System based on knowl-edge

based on prefer-ences

ELM-ART YES -AHA! YES -Hyperadapter YES YESNetcoach YES YESInterbook YES -

Table: Adaptive systems based on learners characterstics [GYZ11]

Knowledge Representation

• To achieve adaptation, the domain knowledge should be representedin a manner that there should be a mapping between the adaptivesystem and the human expert [SKPR05].

• knowledge representation must be specific to the learner modelingtechnique

• knowledge can be represented in various ways• Hierarchical knowledge representation• Goal oriented connectionist knowledge representation• Fuzzy cognitive Maps(FCM)

Knowledge Representation Adaptive Recommendation System for MOOC 24

• knowledge can be represented in various ways

• Hierarchical knowledge representation• Goal oriented connectionist knowledge representation• Fuzzy cognitive Maps(FCM)

Initial thinking about the system

• Domain is the set of conceptsDi = {c1, c2, c3......cn}

• Recommendation will be based on the score/concept.

• Initialization System will be initialized on the basis of quiz in whichthere is a one to one or many relationship among the concepts andquestions

Limitations

• concepts are dependent/relatedc1 = “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.

Limitations

• concepts are dependent/relatedc1 = “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.

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]

Advantage

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

• only part − of /pre − requisite relationship can be representedefficiently

Limitations

• other types of relationships such as related relationships among theconcepts cannot be represented.

• Increasing the knowledge levels of one concept can only affect theknowledge levels of concepts in hierarchy [Mae94].

Advantage

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

• only part − of /pre − requisite relationship can be representedefficiently

Limitations

• other types of relationships such as related relationships among theconcepts cannot be represented.

• Increasing the knowledge levels of one concept can only affect theknowledge levels of concepts in hierarchy [Mae94].

Knowledge Representation“Fuzzy Cognitive Maps (FCM)”

Fuzzy Cognitive Maps

• In all the previous approaches, if the knowledge level of a particularconcept reaches above a threshold value then the concept will beconsidered as learned.

• Knowledge level is not discrete e.g. “He is intelligent”,“He knowsthis concept 50%”

• Fuzzy logic fuzzifies the classification of users’ knowledge levels inclasses

• Each point in the Fuzzy logic is associated with a membershipfunction which tells the degree of membership of this point to theset [Fin96].

• A = φ : ∀xεX : µA(x) = 0

• Represent the domain in the form of graph where each noderepresents 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 × CµE : C × C → [0, 1]

• Domain can be represented by a triplet :

GD = (C .E .S)

C: set of concept nodes, E: set of essential prerequisites, S: set ofsupportive prerequisite

• Each node is associated with a knowledge level ‘l’.

• Membership functions are defined which value of fuzzifies theclassification of learner on the basis of ‘l’.

• Learner knowledge about a particular concept c is thereforeexpressed by providing the values of three fuzzy sets [SKPR05](µU , µK , µL)µU + µK + uL = 1µU > 0 =⇒ µL = 0µL > 0 =⇒ µU = 0

Proposed System for Recommendation system in MOOC

Requirements:

• Adaptive

• Dynamic updatability

• low computational complexity

• Domain independent (e.g. should not need any expert to assignstrentgth of impact among the concepts)

Proposed system Adaptive Recommendation System for MOOC 32

Requirements:

• Adaptive

Requirements:

• Adaptive

Requirements:

• Adaptive

Requirements:

• Adaptive

Input to the system

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

Figure: A 4-layered architecture for Knowledge representation

Relationship among the concept modules

• hierarchical relationship

• non-hierarchical relationship

Example

Module id Concepts(related/unrelated)C1 if-else statementC2 switch statementC3 for loopC4 while loopC5 do-while loopC6 Using conditional statements with itera-

tive statements

Table: An example lecture

Proposed System for Adaptive Recommendations inMOOC

Example

Concept id Objective id DescriptionC1 objp syntax of if-else statementC1 obj1 check a given number is positive or negativeC1 obj2 check a given number is even or oddC1 obj3 given two numbers, check which one is

greaterC1 obj4 given three numbers, check which one is

greaterC2 objp syntax of switch statementC2 obj1 check a given number is positive or negativeC2 obj2 check a given number is even or oddC2 obj3 given two numbers, check which one is

greaterC2 obj4 given three numbers, check which one is

greater

Table: An example of concept to objective table

Example

Concept id Objective id DescriptionC3 objp syntax of the for statementC3 obj1 print the first n integers in a for loopC3 obj2 counting in a for loopC3 obj3 calculating sum in a for loopC3 obj4 calculating average in a for loopC3 obj5 calculating max/min in a for loopC4 objp syntax of the while statementC4 obj1 print the first n integers in a while loopC4 obj2 counting in a while loopC4 obj3 calculating sum in a while loopC4 obj4 calculating average in a while loopC4 obj5 calculating max/min in a while loop

Example

C5 objp syntax of the do-while statementC5 obj1 print the first n integers in a do-while loopC5 obj2 counting in a do-while loopC5 obj3 calculating sum in a do-while loopC5 obj4 calculating average in a do-while loopC5 obj5 calculating max/min in a do-while loopC6 obj1 print all the even numbers between 1 to 100

using while loopC6 obj2 print all the odd numbers divisible by 7, be-

tween 1 to 100C6 obj3 print all the numbers divisible by 3 or 5, be-

tween 1 to 100C6 obj4 print all the odd numbers divisible 3 or 5 but

not 3 and 5, between 1 to 100

State diagram of an objective

Measuring Knowledge Levels for a particular concept• Each learner’s knowledge for a particular concept can be represented

as four tuples (Un,UK ,K , L) .The membership functions for thesefuzzy sets depends on knowledge level ‘x’.

Membership functions for Un

µUn(x) =

1, x ≤ 55

1− (x − 55)/5, 55 < x < 60

0, x ≥ 60

µUK (x) =

(x − 55)/5, 55 < x < 60

1, 60 ≤ x ≤ 70

1− (x − 70)/5, 70 < x < 75

0, x < 55

0, x ≥ 75

Modeling learner

Membership functions for K and L

µK (x) =

(x − 70)/5, 70 < x < 75

1, 75 ≤ x ≤ 85

1− (x − 85)/5, 85 < x < 90

0, x ≤ 70

0, x ≥ 90

µL(x) =

(x − 85)/5, 85 < x < 90

1, 90 ≤ x ≤ 100

0, x ≤ 85

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

Three steps to achieve adaptation

• Initialization

• Update the systems assumptions about the learner.

• Prioritize the concept modules that needs to be recommended.

Initialization• Initialization of the system refers to capturing initial data about the

user e.g. user preferences, learning style, knowledge level about allthe concept taught in the week.

• Knowledge level(Ci ) = Number of objectives achieved for Ci

Total number of objectives in Ci

• Example

Figure: Initialization of concept nodes

• Example

Figure: Initialization of concept nodesProposed system Adaptive Recommendation System for MOOC 50

Prioritizing concept module- Approach 1Select the concept module According to Topological sort.

• Advantage- Select those concept modules which are affecting otherconcept modules i.e. making them ready.

• Limitation- Knowledge level of the concept as well as distance isnot considered i.e. we may select a concept which is already inlearnt state but influencing other concept modules.

Prioritizing concept module- Approach 2 Select the concept modulehaving minimum distance.

• Advantage- Minimum number of objectives to be covered in orderto learn the concept.

• Limitation- Concept module having minimum distance may haveknowledge level as learned

Prioritizing concept module- Approach 3unknown→ unsatisfactoryKnown→ known→ learnt

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

Uniqueness of Proposed System

How this approach is different then existing approaches

• Domain independent (Experts are not required for calculating thedependency of one concept on other)

• Data set is not required for learning

• Low complexity and run time updation is possible

Evaluation

All the possible approaches needs to be compared afterimplementation

• In ITS community, the common practice of evaluation is Empiricalapproaches.

• The criteria for the evaluation of knowledge representation techniqueis the mean number of times that a leaner is advised to read adomain 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 approximatelyequal otherwise the difference between the means are statisticallysignificant.

Evaluation Adaptive Recommendation System for MOOC 55

User Interfaces

Implementation Adaptive Recommendation System for MOOC 56

User Interfaces

Use-case diagram

Plan for stage 2

Looking ahead

Goals:

• Literature survey.

• Find out the problems in the existing approaches.

Plan for Stage 2 Adaptive Recommendation System for MOOC 59

Plan for stage 2

Looking ahead

Goals:

• Capture requirements.

• SRS document.

• Implementation plan

Plan for stage 2

Looking ahead

Goals:

• Implementation

Plan for stage 2

Looking ahead

Goals:

• Implementation

Plan for stage 2

Looking ahead

Goals:

• Make test plan.

• Testing

Plan for stage 2

Looking ahead

Goals:

• Resolve Issues.

• Deployment

• Write paper for UMAP 2014

References I

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 LectureNotes 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.

References II

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.

References III

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 ofLecture Notes in Computer Science, pages 448–453. Springer Berlin Heidelberg, 2001.

Thank you !!

adaptive recommendation system for mooc

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