OpenU master class, #LearningAnalytics #MC_LA, September 2013

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OpenU master class, #LearningAnalytics #MC_LA, September 2013

  1. 1. A  Recommender  system  for  Social   Learning  Pla6orms   Soude  Fazeli    
  2. 2. 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
  3. 3. Open Discovery Space (ODS) A  socially-­‐powered,  mul3lingual     open  learning  pla6orm   in  Europe   Recommendations! Which recommender algorithm best fits ODS platform?
  4. 4. Data-driven study 1. Goal To find out which recommender algorithms are most suitable for social learning platforms like ODS
  5. 5. Data-driven study 2. Method •  Testing several recommender algorithms –  Classical collaborative filtering algorithms –  T-index approach •  A graph-based recommender •  Datasets –  MovieLens (standard dataset) –  MACE, OpenScout, Travel well (similar to the ODS dataset) •  Using Mahout Data Mining Framework
  6. 6. 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    
  7. 7. Data-driven study 4. Result 4.1. F1 score: a combination of precision and recall OpenScout% 0.14" 0.12" 0.1" Tanimoto4Jaccard"(CF1)" 0.08" Tanimoto3Jaccard"(CF1)" 0.06" Loglikelihood"(CF2)" Euclidean"(CF3)" 0.04" Euclidean"(CF3)" Graph4based"(CF4)" 0.02" Graph3based"(CF4)" Loglikelihood"(CF2)" 3" 5" 7" F1@10% F1@10% MACE% 0.1" 0.09" 0.08" 0.07" 0.06" 0.05" 0.04" 0.03" 0.02" 0.01" 0" 0" 10" 3" size%of%neighborhood%(n)% 5" 10" MovieLens% Travel%well% 0.2" 0.06" Tanimoto3Jaccard"(CF1)" 0.04" Loglikelihood"(CF2)" Euclidean"(CF3)" 0.02" Graph3based"(CF4)" 0" 3" 5" 7" size%of%neighborhood%(n)% 10" F1@10% 0.1" 0.25" 0.08" F1@10% 7" size%of%neighborhood%(n)% Tanimoto0Jaccard"(CF1)" 0.15" Loglikelihood"(CF2)" 0.1" Euclidean"(CF3)" 0.05" Graph0based"(CF4)" 0" 3" 5" 7" 10" size%of%neighborhood%(n)% F1  of  the  recommender  algorithms  for  different  datasets,  based  on   the  size  of  neighborhood    
  8. 8. Data-driven study 4.2. Degree centrality: to identify central users 250   200   150   MovieLens   degree   OpenScout   100   MACE   Travel  well   50   0   u1   u2   u3   u4   u5   u6   u7   u8   u9   u10   Top-­‐10  central  users   Degree  distribuVon  of  top-­‐10  central  users  for  different  datasets  
  9. 9. Conclusion •  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!
  10. 10. 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
  11. 11. Soude Fazeli PhD candidate Open University of the Netherlands email: soude.fazeli@ou.nl Twitter: https://twitter.com/SoudeFazeli Skype: soude_fazeli_celstec

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