A Trust-based Social Recommenderfor TeachersSoude Fazeli, PhD candidateDr. Hendrik DrachslerDr. Francis BrounsProf. Dr. Pe...
The doctoral study is funded by• NELLL(Netherlands Laboratory for Lifelong Learning at the OUNL)• Open Discovery Space (OD...
A social space for teachers                      Learning Networks?page 3
page 4
Recommender systems?page 5
A proposed recommender system for teachers       page 6Based on the framework proposed by Manouselis &Costopoulou (2007)
Supported tasks•   Find Novel resources     • (Recker et al, 2003), (Lemire et al, 2005), (Rafaeli et al,         2005), (...
User model•   History-based models and user-item ratings matrix•   Ontology•   TEL variables: knowledge level, interests, ...
A proposed recommender system for teachers       page 9Based on the framework proposed by Manouselis &Costopoulou (2007)
Similarity               Sparsity!page 10
(Golbeck, 2009; Kamvar et al., 2003; Ziegler & Golbeck, 2007; Massa & Avesani, 2004; Lathia et al., 2008; Fazeli et al., 2...
Trust in recommender systems•   Trustworthy users == like-minded users•   Assuming that trust is transitive     • (if A tr...
A social recommender system:T-index approach(Fazeli et al., 2010)•   Creates trust relationships between users     •    Ba...
Social datapage 14
•   RQ1: How can the sparsity problem within    educational datasets be solved by using inter-user    trust relationships ...
Proposed research1.   Requirement analysis           •   Literature review           •   Interview study2.   Dataset-drive...
1. Requirement analysis  •   Goal       • Investigating the teachers’ main needs and requirements  •   Method       • Nomi...
Proposed research1.   Requirement analysis           •   Literature review           •   Interview study2.   Dataset-drive...
2. Dataset study  •   Goal       • To find out the most suitable recommender system algorithm for teachers  •   Method    ...
Proposed research1.   Requirement analysis           •   Literature review           •   Interview study2.   Dataset-drive...
3. User evaluation study  •   Goal       • To study users’ satisfaction  •   Method       • Questionnaire       • Pre and ...
Proposed research1.   Requirement analysis           •   Literature review           •   Interview study2.   Dataset-drive...
4. Pilot study  •   Goal       • To deploy the final release       • To test it under realistic operational conditions wit...
Conclusion• The aim is to support teachers to find the most    suitable content or people•   Recommender systems as a solu...
Ongoing and Further work •   Requirement analysis      • Data collection (done)            • Teachers from the Netherlands...
Soude FazeliPhD candidateOpen University of the NetherlandsCentre for Learning Sciences and Technologies(CELSTEC) PO-Box 2...
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RecSysTEL2012 slides

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  1. 1. A Trust-based Social Recommenderfor TeachersSoude Fazeli, PhD candidateDr. Hendrik DrachslerDr. Francis BrounsProf. Dr. Peter Sloep page 1
  2. 2. The doctoral study is funded by• NELLL(Netherlands Laboratory for Lifelong Learning at the OUNL)• Open Discovery Space (ODS) A socially-powered, multilingual open learning infrastructure to boost the adaptation of eLearning Resources in EuropeRun-time: 2011-2015 page 2
  3. 3. A social space for teachers Learning Networks?page 3
  4. 4. page 4
  5. 5. Recommender systems?page 5
  6. 6. A proposed recommender system for teachers page 6Based on the framework proposed by Manouselis &Costopoulou (2007)
  7. 7. Supported tasks• Find Novel resources • (Recker et al, 2003), (Lemire et al, 2005), (Rafaeli et al, 2005), (Tang & McCalla, 2005), (Drachsler et al, 2009)• Find peers • (Beham et al, 2010), (Recker et al, 2003)• For more examples, please refer to • Manouselis N., Drachsler H., Verbert K., Duval E. (2012). Recommender Systems for Learning book, Springer.page 7
  8. 8. User model• History-based models and user-item ratings matrix• Ontology• TEL variables: knowledge level, interests, goals and tasks, and background knowledge• Capturing social data with help of a standard specification: • FOAF • CAM • Annotation scheme (In the context of Organic.Edunet)page 8
  9. 9. A proposed recommender system for teachers page 9Based on the framework proposed by Manouselis &Costopoulou (2007)
  10. 10. Similarity Sparsity!page 10
  11. 11. (Golbeck, 2009; Kamvar et al., 2003; Ziegler & Golbeck, 2007; Massa & Avesani, 2004; Lathia et al., 2008; Fazeli et al., 2010)page 11
  12. 12. Trust in recommender systems• Trustworthy users == like-minded users• Assuming that trust is transitive • (if A trusts B and B trusts C, then A trusts C)• Inter-user trust phenomena helps us to infer a relationship between users Alice Bobpage 12 Carol
  13. 13. A social recommender system:T-index approach(Fazeli et al., 2010)• Creates trust relationships between users • Based on the ratings information• Proposes T-index concept • To measure trustworthiness of users • To improve the process of finding the nearest neighbours• Inspired on the H-index • Used to evaluate the publications of an author• Based on results, T-index improves structure of trust networks of userspage 13
  14. 14. Social datapage 14
  15. 15. • RQ1: How can the sparsity problem within educational datasets be solved by using inter-user trust relationships which originally come from the social data of users?• RQ2: How can teachers’ networks be made to evolve by the use of social data? page 15
  16. 16. Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 16
  17. 17. 1. Requirement analysis • Goal • Investigating the teachers’ main needs and requirements • Method • Nominal group technique (NGT) • 18 teachers (novices, experts, mentors and supervising teachers) from the Limburg area, the Netherlands • Inviting teachers to cluster the generated ideas by WebSort • Survey on use of social media by teachers • Description • “What kind of support do you need to provide innovative teaching at your school?" • Writing down the ideas, discussion, clustering, ranking the ideas • Expected outcomes • An inventory list of teachers’ needs and requirements • A framework to identify suitable recommender systems’ strategies for our page 17 target users
  18. 18. Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 18
  19. 19. 2. Dataset study • Goal • To find out the most suitable recommender system algorithm for teachers • Method • An offline empirical study of candidate algorithms (T-index, Pearson, Slope- one, Tanimoto) • TravelWell, Organic.Edunet, eTwinning (TELeurope, Mace, Openscout) • Variables to be measured • Prediction accuracy, coverage, and F1 • Expected outcomes • If the T-index approach can help to deal with the sparse data in the used datasets • Which of the recommender system algorithms suits teachers best (Verbert et al., 2011) page 19
  20. 20. Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 20
  21. 21. 3. User evaluation study • Goal • To study users’ satisfaction • Method • Questionnaire • Pre and post test of knowledge gain • Variables to be measured • Interestingness and value-addedness (Tang & McCalla, 2009) • Prediction accuracy, coverage, and F1 measures • Expected outcomes • Initial feedback by end-users on users’ satisfaction as an input for pilot study page 21
  22. 22. Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 22
  23. 23. 4. Pilot study • Goal • To deploy the final release • To test it under realistic operational conditions with the end-users • Method • Evaluating performance of the designed recommender system algorithm • Study the structure of the teachers’ networks • Variables to be measured • Prediction accuracy and coverage • Effectiveness in terms of total number of visited, bookmarked, or rated learning objects for two groups of users • Indegree distribution to study how the structure of teachers’ networks changes • Expected outcomes • Empirical data on prediction accuracy and coverage • The visualization of teachers’ networks page 23
  24. 24. Conclusion• The aim is to support teachers to find the most suitable content or people• Recommender systems as a solution• How to overcome the sparsity problem by use of social data page 24
  25. 25. Ongoing and Further work • Requirement analysis • Data collection (done) • Teachers from the Netherlands • European teachers in an Open Discovery Space Summer School, Greece • Publishing the results as an extensive requirement analysis for teachers all over the Europe (in progress) • Data set study • Testing data sets with users’ ratings (successfully done) • Testing data sets based on implicit users’ feedback (tags, bookmarks, comments, blogs, etc) (in progress) page 25
  26. 26. Soude FazeliPhD candidateOpen University of the NetherlandsCentre for Learning Sciences and Technologies(CELSTEC) PO-Box 29606401 DL Heerlen, The Netherlandsemail: soude.fazeli@ou.nl page 26
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