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A Hybrid Peer Recommender System for a Online Community Teachers

A Hybrid Peer Recommender System for a Online Community Teachers
by Cristian Miranda, Julio Guerra, Denis Parra, Eliana Scheihing

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A Hybrid Peer Recommender System for a Online Community Teachers

  1. 1. A Hybrid Peer Recommender System for a Online Community TeachersCristian Miranda*, Julio Guerra**, Denis Parra**, Eliana Scheihing* Southern University of Chile* University of Pittsburgh**SRS: 3rd International Workshop on Social Recommender Systems ~ at UMAP 2012
  2. 2. Outline• Introduction to Kelluwen Project• What is Being Recommended?• Recommendation Model• Evaluation/Results• Conclusions/Future Work
  3. 3. Geog. Context: Southern Chile In this part of South America
  4. 4. Kelluwen Project• Kelluwen is a mapudungun word that means “Group of People” that we used as “Collaborative Work”.• The Kelluwen project address deficits in socio- communicate skills of vulnerable students in ages 12~17 from• Technology involved: The creation of a community of students, teachers and researchers supported by web 2.0 tools.
  5. 5. What is being recommended? • Our RecSys recommends “comments and suggestions” made by teachers when they conduct these educational activities. • Educational Activities. These are activities performed in class by students. They report them and comment them in the Kelluwen platform. • Teachers that are guiding some activity for the first time can have a big support if they receive comments from teachers (peers) that have already conducted that activity .
  6. 6. What is being recommended?Educational Activity (materials, durations, … MAX 3 studentslink to comments, status) per group… Recommendations: comments & … ppt too long, suggestions made by other teachers might skip some that already guided the same activity slides…
  7. 7. Recommendation Model (1/3) u1: peers’ similarity u2: item value
  8. 8. Recommendation Model (2/3) • Peers’ similarity considers (u1) Variable ID Values Low: 1 School Socio-economical Mean Low: 2 Level NS Mean: 3 Mean High: 4 High: 5 School Quality of TIC CT Sum of three values Infrastructure Lower than 10.000 habitants: 1 Size of School Locality TL Between 10.000 and 100.000 habitants: 2 Greater than 100.000 bahitants: 3 Mean Number of Students N Mean number of stufents by classroom for each teacher per Classroom
  9. 9. Recommendation Model (3/3)• Items assessed based on creator’s value (u2): Variable ID Values Number of Number of didacticaldesignsappliedbyteachert (sendingthesuggestionss), normalizedtothe [0,1] didacticaldesignsappliedbyteache interval rt (sendingthesuggestions) Verywell: 0 Activityevaluationfromtheteacher Well: 1 sendingthesuggestions Bad: 1 VeryBad: 0 MessageNature Activitycomment: 0 Suggestion: 1 Number of rating of Number of rating of allmessagesfromteachert (sendingthesuggestions), normalizedtothe [0,1] allmessagesfromteachert interval (sendingthesuggestions) Number of ratingsof suggestions Number of rating of suggestions, normalizedtothe [0,1] interval
  10. 10. Evaluation(1/2) nDCG • Evaluation done with nDCG (normalized discounted cummulative gain) • Phase 1: 29 teachers provided comments and suggestions to their educational activities inside the Kelluwen system • Phase 2: # recommendations generated between Oct/Nov 2011: 314 • Phase 2: # recommendations that were provided feedback: 96 • Here there’s a plot with nDCG of 39 recommendation sets (6 people).
  11. 11. User satisfaction survey Used the 70% recommendations in their educational activities Thought that the recommendations were useful during the 68% activity execution ¿Did I consider the Totally agree ¿Were the recommendations useful recommendation while to develop my activities? executing the educational Disagree activities? Agree Totally agree
  12. 12. Conclusions • We introduced a RecSys that recommends novel items (comments of teachers to educational activities) in a novel domain (a community portal for students, teachers and researchers) • The RecSys had a good level of acceptance: 68% of the teachers that considered the recommendations found them useful in their educational activities.
  13. 13. Future work• Incorporate user engagement techniques to obtain more feedback of the utility of recommendations.• To Develop a “recommendation history”: Users would be able to see which recommendations they have received and how they evaluated them.• Include new metrics to evaluate the recommender system.
  14. 14. Questions? Thanks
  15. 15. Backup slides
  16. 16. Sensibility Analysis • Best combination of k1 (0.8) and k2 (0.2) values to combine u1 (peer similarity) and u2 (item and item- creator value) ~ best nDCG
  17. 17. Resultados • Encuesta de Satisfacción de Usuarios El sistema adaptativo de recomendación de pares es un mecanismo de apoyo a sus 70% actividades docentes ¿Aprecio las recomendaciones de mis pares como un mecanismo de apoyo para el desarrollo de las actividades?