A Hybrid Peer Recommender System for a Online Community Teachers
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A Hybrid Peer Recommender System for a Online Community Teachers
Cristian 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
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Outline
• Introduction to Kelluwen Project• What is Being Recommended?• Recommendation Model• Evaluation/Results• Conclusions/Future Work
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Geog. Context: Southern Chile
In this part of South America
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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.
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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 .
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What is being recommended?
Educational Activity (materials, durations, link to comments, status)
Recommendations: comments & suggestions made by other teachers that already guided the same activity
… MAX 3 students per group…
… ppt too long, might skip some slides…
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Recommendation Model (1/3)
• Given:– C: the set of all users– S: the set of all “recommendable” items– u: the utility function ( how useful is an item to a user)– profile of user c, a vector of |p| attributes.– profile of ítem s, a vector of |r| attributes.– R = () rating matrix, where is the rating given by user c to item s– N = () reply matrix, where is the amount of replies that the user i does to
messages sent by user j.
• The utility of an item b for a user a is then given by
= u1: peers’ similarity u2: item value
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Recommendation Model (2/3)
• Peers’ similarity considers (u1)Variable ID Values
School Socio-economical Level NS
Low: 1Mean Low: 2
Mean: 3Mean High: 4
High: 5
School Quality of TIC Infrastructure
CT Sum of three values
Size of School Locality TLLower than 10.000 habitants: 1
Between 10.000 and 100.000 habitants: 2Greater than 100.000 bahitants: 3
Mean Number of Students per Classroom
N Mean number of stufents by classroom for each teacher
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Recommendation Model (3/3)• Items assessed based on creator’s value (u2):
Variable ID Values
Number of didactical designs applied by teacher t (sending the
suggestion s)
Number of didactical designs applied by teacher t (sending the suggestions s), normalized to the
[0,1] interval
Activity evaluation from the teacher sending the suggestion s
Very well: 0
Well: 1
Bad: 1
Very Bad: 0
Message Nature Activity comment: 0
Suggestion: 1
Number of rating of all messages from teacher t (sending the
suggestion s)
Number of rating of all messages from teacher t (sending the suggestion s), normalized to the [0,1]
interval
Number of ratings of suggestion s Number of rating of suggestion s, normalized to the [0,1] interval
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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).
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User satisfaction survey
¿Did I consider the recommendation while
executing the educational activities?
70%Used the
recommendations in their educational
activities
¿Were the recommendations useful to develop my activities?
68%Thought that the
recommendations were useful during the activity execution
Totally agree
Disagree
Agree
Totally agree
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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.
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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.
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Questions? Thanks
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Backup slides
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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
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• Encuesta de Satisfacción de Usuarios
¿Aprecio las recomendaciones de mis pares como un mecanismo de apoyo para el desarrollo de las actividades?
70%El sistema adaptativo de
recomendación de pares es un mecanismo de apoyo a sus
actividades docentes
Resultados