OpenU master class, #LearningAnalytics #MC_LA, September 2013

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: soude.fazeli@ou.nl Twitter: https://twitter.com/SoudeFazeli Skype: soude_fazeli_celstec