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Amirkabir University of Technology

Tehran, Iran

June2012

Index

 Introduction

 Contribution

 Basic Theory

 System Design

 Analysis of the Learners

 Analysis of the Resources

 System Architecture

 Proposed Method for Learner Classification

 Result

 Conclusion

2

Information Overload

Introduction

Recommender System

Motivation

 rarely is being used in E-learning

 offering the right resources

 learner characteristics

 shortest possible time

3

Contribution

 Collaborative filtering

 Two groups

 Self-paced learning or recommending?

4

 Target User

 Self-paced learning method

 Recommender system

 Collaborative Filtering Method

 User-based method

 Item-based method

5

architecture of recommender system

 Learners

 collaborative filtering unit

 learning resources

 two sub-systems

6

60 participants

First group : self-paced learning

Second group: recommender system

7

Analysis of the Resources

 10 resources about “hardware ergonomic”

 abstract

 5 suitable resources

8

Subsystem1

Data Entry

DB

System Architecture

Subsystem2

Data Entry

Learners

Collaborative Technique

Similar Users Finding

Similar User's

Sources Select

Collaborative

Filtering

Method

Resources

Selection

Resources

Score

Test

Test

Recommended

Resources

Learning

Resources

9

5 questions in the registration section

Compare answers more similar answers = more scores

Score user (i) = 2Q1 + 2Q2 + 4Q3 + 6Q4 + 6Q5

Q = {0 , 1}

10

Group 1 Similar Users Group 2

CF

11

12

Comparison of Selected Resources for Group1

(left) and Received Resources for Group2 (right)

60%

0%

0%

40%

Excellent

Good

Fary bad

Awful

76%

0%

8%

16%

First Group Second Group

13

80

70

60

50

40

30

20

10

0

1

Select resources-1th group 48

Select resources-2th group 72

2

24

16

3

28

24

4

12

0

5

28

20

6

20

12

7

32

64

8

20

12

9

20

4

10

44

60

11

12

8

12

12

8

Resources

14

100

90

80

70

60

50

40

30

20

10

0

Correct answers-1th group

Correct answers-2th group

Q1

84

88

Q2

40

92

Q3

48

72

Q4

44

80

Q5

56

68

Q6

56

68

Questions (Test)

Q7

32

80

Q8

56

72

Q9

64

80

15

 information overload

 recommender system

 speed and quality

 score for each activity

 Recommendations for both groups

Limitations of this Study

 few learners

 interest for studying

 educational environment

16

1.

2.

3.

4.

5.

6.

7.

8.

Adomavicius Gediminas; Tuzhilin Alexander; “Toward the Next Generation of Recommender Systems: A

Survey of the State-of-the-Art and Possible Extensions”, IEEE, pp.1-16, 2008.

Mortensen Magnus; “Design and Evaluation of a Recommender System”, INF-3981 Master's Thesis in

Computer Science, University of Troms, 2009.

John O’Donovan, Barry Smyth ,"Trust in Recommender Systems", Adaptive Information Cluster

Department of Computer Science, University College Dublin, Belfield, Dublin 4 Ireland, {john.odonovan, barry.smyth}@ucd.ie

E. Reategui , E. Boff , "Personalization in an interactive learning environment through a virtual character", Department of Computer Science, Universidad de Caxias do Sul, 95070-560 Caxias do Sul,

RS, Brazil, J.A. Campbell, a b Department of Computer Science, University College London, Gower, St.,

London WC1E 6BT, UK, Received 21 February 2007; received in revised form 29 May 2009.

Huiyi Tan1, Junfei Guo3, Yong Li2,"E-Learning Recommendation System", International School of

Software, Wuhan University, Wuhan, China, Information School, Estar University, Qingdao, China, tan6043@gmail.com

Mohammed Almulla, "School e-Guide: a Personalized Recommender System for E-learning

Environments", Kuwait University, P.O.Box 5969 Safat,First Kuwait Conf. on E-Services and E-Systems,

Nov 17-19, 2009

Vinod Krishnan, Pradeep Kumar Narayanashetty, Mukesh Nathan, Richard T. Davies, and Joseph A.

Konstan, "Who Predicts Better? – Results from an Online Study Comparing Humans and an Online

Recommender System", Department of Computer Science and Engineering, University of Minnesota-

Twin Cities, RecSys’08, October 23–25, 2008, Lausanne, Switzerland.

Ricci, F., Venturini, A, .Cavada, D., Mirzadeh, N., Blaas, D., Nones, M. "Product recommendation with interactive query management and twofold similarity". In Proceedings of the 5th International

Conference on Case-Based Reasoning, ICCBR'03, pages 479-493, 2009.

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