openu master class, #learninganalytics #mc_la, september 2013
TRANSCRIPT
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A Recommender system for Social Learning Pla6orms
Soude Fazeli
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Link to Learning Analytics
Recommender Systems can support learners and teachers in finding the ‘right’ learning materials or peers
Recommenders take advantage of patterns in a large amount of data
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A socially-‐powered, mul3lingual open learning pla6orm in Europe
Open Discovery Space (ODS)
Recommendations!
Which recommender algorithm best fits ODS platform?
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To find out which recommender algorithms are most suitable for social learning platforms like ODS
Data-driven study 1. Goal
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Data-driven study 2. Method
• Testing several recommender algorithms – Classical collaborative filtering algorithms – T-index approach
• Datasets
– MovieLens (standard dataset) – MACE, OpenScout, Travel well (similar to the ODS
dataset)
• Using Mahout Data Mining Framework
• A graph-based recommender
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Data-driven study 3. Data
Dataset Users Learning objects
Source
MACE 105 5,696 hDp://portal.mace-‐project.eu/
OpenScout 331 1,568 hDp://www.openscout.net/openscout-‐home
Travel well 98 1,923 hDp://lreforschools.eun.org
MovieLens 941 1,512 hDp://movielens.umn.edu
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Data-driven study 4. Result 4.1. F1 score: a combination of precision and recall
F1 of the recommender algorithms for different datasets, based on the size of neighborhood
0"0.01"0.02"0.03"0.04"0.05"0.06"0.07"0.08"0.09"0.1"
3" 5" 7" 10"
F1@10%
size%of%neighborhood%(n)%
MACE%
Tanimoto4Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph4based"(CF4)"
0"
0.02"
0.04"
0.06"
0.08"
0.1"
0.12"
0.14"
3" 5" 7" 10"
F1@10%
size%of%neighborhood%(n)%
OpenScout%
Tanimoto3Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph3based"(CF4)"
0"
0.02"
0.04"
0.06"
0.08"
0.1"
3" 5" 7" 10"
F1@10%
size%of%neighborhood%(n)%
Travel%well%
Tanimoto3Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph3based"(CF4)"
0"
0.05"
0.1"
0.15"
0.2"
0.25"
3" 5" 7" 10"F1@10%
size%of%neighborhood%(n)%
MovieLens%
Tanimoto0Jaccard"(CF1)"
Loglikelihood"(CF2)"
Euclidean"(CF3)"
Graph0based"(CF4)"
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Degree distribuVon of top-‐10 central users for different datasets
Data-driven study 4.2. Degree centrality: to identify central users
0
50
100
150
200
250
u1 u2 u3 u4 u5 u6 u7 u8 u9 u10
degree
Top-‐10 central users
MovieLens
OpenScout
MACE
Travel well
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• The aim of this study is to support teachers in social learning platforms in finding the most suitable content or people
• Recommender systems can be a solution for this aim.
• The result showed that the T-index graph-based recommender can better support social learning platforms for teachers, compared to the standard algorithms.
• We are able to make more accurate and relevant recommendations to YOU!
Conclusion
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Ongoing and Further work
• Go online with the ODS platform (October 2013)
• User evaluation study (February 2014)
• Testing recommender algorithms on more datasets coming from MOOC platforms
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Soude Fazeli PhD candidate Open University of the Netherlands email: [email protected] Twitter: https://twitter.com/SoudeFazeli Skype: soude_fazeli_celstec