openu master class, #learninganalytics #mc_la, september 2013
TRANSCRIPT
A Recommender system for Social Learning Pla6orms
Soude Fazeli
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
A socially-‐powered, mul3lingual open learning pla6orm in Europe
Open Discovery Space (ODS)
Recommendations!
Which recommender algorithm best fits ODS platform?
To find out which recommender algorithms are most suitable for social learning platforms like ODS
Data-driven study 1. Goal
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
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
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)"
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
• 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
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
Soude Fazeli PhD candidate Open University of the Netherlands email: [email protected] Twitter: https://twitter.com/SoudeFazeli Skype: soude_fazeli_celstec