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A Hybrid Peer Recommender System for a Online Community Teachers

A Hybrid Peer Recommender System for a Online Community Teachers



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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  • The “vulnerable” is in terms of isolation: They are from rural areas or poor suburbs of southern Chile.Students and teachers have access to internet mainly at the Schools, but some of them ( that live in the cities) also have access from home.
  • Details of u1 and u2 can be seen in the paper. Next 2 slides explain user profile and item profile dimensions
  • The PRESTIGE of the teacher who created the item is a way to measure how good is the item.
  • X axis: X[userID].[Recommendation set ID]
  • (if short in

A Hybrid Peer Recommender System for a Online Community Teachers A Hybrid Peer Recommender System for a Online Community Teachers Presentation Transcript

  • 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
  • Outline• Introduction to Kelluwen Project• What is Being Recommended?• Recommendation Model• Evaluation/Results• Conclusions/Future Work
  • Geog. Context: Southern Chile In this part of South America
  • 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.
  • 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 .
  • 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…
  • Recommendation Model (1/3) u1: peers’ similarity u2: item value
  • 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
  • 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
  • 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).
  • 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
  • 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.
  • 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.
  • Questions? Thanks
  • Backup slides
  • 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
  • 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?