RecSysTEL2012 slides
Upcoming SlideShare
Loading in...5
×
 

RecSysTEL2012 slides

on

  • 316 views

 

Statistics

Views

Total Views
316
Views on SlideShare
316
Embed Views
0

Actions

Likes
0
Downloads
3
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    RecSysTEL2012 slides RecSysTEL2012 slides Presentation Transcript

    • A Trust-based Social Recommenderfor TeachersSoude Fazeli, PhD candidateDr. Hendrik DrachslerDr. Francis BrounsProf. Dr. Peter Sloep page 1
    • 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
    • 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), (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
    • 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
    • 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., 2010)page 11
    • 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
    • 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
    • Social datapage 14
    • • 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
    • Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 16
    • 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
    • Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 18
    • 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
    • Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 20
    • 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
    • Proposed research1. Requirement analysis • Literature review • Interview study2. Dataset-driven study3. User evaluation study4. Pilot study page 22
    • 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
    • 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
    • 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
    • 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