a recommender system for learning resources suggestions based on learner characteristics
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Amirkabir University of Technology Tehran, Iran. A Recommender System for Learning Resources Suggestions Based on Learner Characteristics. Ahmad A. Kardan Golsa Mirbagheri. June2012. Introduction Contribution Basic Theory System Design Analysis of the Learners - PowerPoint PPT PresentationTRANSCRIPT
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
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
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
CF
11
Self-Paced Learning
OR
Recommender System?
System Pre-Evaluation
12
40%
60%
0%
0%76%
16%8%
ExcellentGoodFary badAwful
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 11 120
1020304050607080
The Analysis of Resource Selection by the Learners of the First and Second Groups
Rea
ding
of s
ourc
es
Resources
Percentage of correct answers to questions by the users group 1&2
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q90
102030405060708090
100C
orre
ct a
nsw
ers
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, [email protected]
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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