Amirkabir University of Technology
Tehran, Iran
June2012

 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
Recommender System
Motivation
 rarely is being used in E-learning
 offering the right resources
 learner characteristics
 shortest possible time
3

 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




7

 10 resources about “hardware ergonomic”
 abstract
 5 suitable resources
8

Subsystem1
Data Entry
DB
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



Score user (i) = 2Q1 + 2Q2 + 4Q3 + 6Q4 + 6Q5
Q = {0 , 1}
10

Group 1 Similar Users Group 2
CF
11

12

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,
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Nov 17-19, 2009
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Konstan, "Who Predicts Better? – Results from an Online Study Comparing Humans and an Online
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