A Recommender System for Learning Resources Suggestions Based on Learner Characteristics

Amirkabir University of Technology Tehran, Iran

Ahmad A. Kardan Golsa Mirbagheri

June2012

Introduction Contribution

Basic Theory

System Design

Analysis of the Learners

Analysis of the Resources

System Architecture

Proposed Method for Learner Classification

Result

Conclusion

Index

2

Introduction

Information Overload Recommender System

Motivation

rarely is being used in E-learning

offering the right resources

learner characteristics

shortest possible time

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?

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

Basic Theory

5

architecture of recommender system

Learners

collaborative filtering unit

learning resources

two sub-systems

6

System Design

60 participants

First group : self-paced learning

Second group: recommender system

Analysis of the Learners from the First and Second Group

7

10 resources about “hardware ergonomic”

abstract

5 suitable resources

Analysis of the Resources

8

System Architecture

Data Entry

Resources Selection

Resources Score

Test

Data Entry

Similar User's Sources Select

Similar Users Finding

Recommended ResourcesTest

Subsystem1 Subsystem2

Collaborative Technique

Learners

Learning Resources

Collaborative Filtering

MethodDB

9

5 questions in the registration section Compare answers

more similar answers = more scores

Proposed Method for Learner Classification

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

Q = {0 , 1}

10

Finding the Similar Users

Group 1 Similar Users Group 2

CFCF

11

Self-Paced Learning

OR

Recommender System?

System Pre-Evaluation

12

40%

60%

0%

0%76%

16%8%

Excellent

Good

Fary bad

Awful

Second GroupFirst Group

Comparison of Selected Resources for Group1 (left) and Received Resources for Group2 (right)

13

14

1 2 3 4 5 6 7 8 9 10 11 12

Select resources-1th group 48 24 28 12 28 20 32 20 20 44 12 12

Select resources-2th group 72 16 24 0 20 12 64 12 4 60 8 8

5

15

25

35

45

55

65

75

The Analysis of Resource Selection by the Learners of the First and Second Groups

Re

ad

ing

of

sou

rces

Resources

Percentage of correct answers to questions by the users group 1&2

Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9

Correct answers-1th group 84 40 48 44 56 56 32 56 64

Correct answers-2th group 88 92 72 80 68 68 80 72 80

5152535455565758595

Co

rre

ct

ans

we

rs

Questions (Test)

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

Conclusion

16

1. 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.

2. Mortensen Magnus; “Design and Evaluation of a Recommender System”, INF-3981 Master's Thesis in Computer Science, University of Troms, 2009.

3. 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

4. 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.

5. 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

6. 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

7. 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.

8. 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.

References

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