Your SlideShare is downloading. ×
  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. May I suggest? Three PLE recommender strategies in comparison PLE Conference, Southampton, July 11-13, 2011
    • Felix Mödritscher (speaker) , Barbara Krumay
    • Vienna Univ. of Economics &Business, Austria
    • Sten Govaerts, Erik Duval
    • Katholieke Universiteit Leuven, Belgium
    • Ingo Dahn
    • University of Koblenz-Landau, Germany
    Sandy El Helou, Denis Gillet EPFL, Switzerland Alexander Nussbaumer, Dietrich Albert Graz University of Technology, Austria Carsten Ullrich Shanghai Jiao Tong University, China
  • 2. Agenda
    • PLEs and recommendations – why?
    • Three approaches from the ROLE project
      • Federated Search Widget
      • Community-based PLE Recommender
      • Psycho-pedagogical Recommender
    • Comparison of the PLE recommenders
    11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  • 3. 1. Personal Learning Environments (PLEs)
    • PLE = “ set of tools, services, and artefacts gathered from various contexts and to be used by learners ” [Henri et al., 08]
    • Characteristics of PLE-based activities:
    • several actors (different roles) ...
    • ... use technology (tools) to ...
    • ... connect to learner networks and ...
    • ... collaborate on shared artefacts ...
    • ... in order to achieve common goals. [Wild, 09]
    • Problems: Learners/teachers have varying attitudes and skills in using ICT!
    • can cause negative feelings or states (frustration, distraction, etc.);
    • hindering to proceed with learning or failing to achieve goals
    • [Windschitl & Sahl, 02; Nguyen-Ngoc & Law, 08]
    11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  • 4. 1. PLEs and Recommendations
    • Recommendations are necessary “if users have to make choices without sufficient personal experiences of alternatives ” [Resnick & Varian, 97]
    • For TEL: Examples described in the RecSysTEL workshop proceedings!
    • For PLEs:
      • Pre-given PLE designs for specific needs
      • Possible PLE entities (artefacts, tools, peers) helpful for a specific situation
    • Recommendations are a powerful instrument for empowering learners to design their PLEs and use technology for learning...
    • However: Different solution approaches driven by different disciplines...
    • And: CF techniques not sufficient! (global vs. local top-n)
    11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  • 5. 2. Approach 1: Federated search widget ‘Binocs’
    • Aggregate heterogeneous resources from different (social media) repositories
    • Save, share, assess, and repurpose resources according to user’s interests
    • Actions taken into account: select resource, like/dislike, preview
    • Learning/social context derived from course
    • Forward contextual data to a recommender system (3A contextual ranking service, Graaasp [El Helou et al., 09] )
    • Ranking according to previous interactions and relevance to search query
    11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  • 6. 2. Approach 2: Community-based recommender ‘PLEShare’
    • Practice sharing repository on the Web; to be integrated into PLE solutions (Web-API)
    • Idea: users share PLE experiences voluntarily
    • Two demos: (a) PLEShare widget, (b) PAcMan add-on
    • PAcMan: allows designing tool bundles in the form of tagged bookmarking lists (=activities); simple features for sharing such activities and retrieving/reusing them
    • Shared data used for generating two kinds of recommendations: (1) activity patterns for starting new activities [‘Pattern Store’] (2) top-n PLE items (artefacts, tools, peers) for a specific context [no explicit feature but available via Web-API]
    • Techniques: CF, clustering
    11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  • 7. 2. Approach 3: Psycho-pedagogical recommender
    • Developed according to theoretical models (self-regulated learning) and relevant taxonomies [Fruhmann et al., 10]
    • Based on learning goals and competences (learner monitoring and questionnaires)
    • Realised as widget for providing: (1) support for planning new activities; (2) guidance for ongoing activities; to find appropriate resources (artifacts, tools, peers)
    • Additional features planned: allowing learners to give feedback on recommendations (implicitly through usage data); provision of explanations; visual feedback on planned and completed activities
    • Techniques: rule/model-based recommender
    • Remark: no full-featured prototype available
    11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  • 8. 3. Comparison of our PLE recommenders 11-13/07/2011 PLE Conference,Southampton, 2011 /9 , © Binocs widget PLEShare PP recommender recommender strategy CF, PageRank-like & content-based CF & IR/clustering (cliques, topics, ...) rule/profile-based (competences) data & data gathering on entering search terms, automated tagged bookmarks, voluntarily shared questionnaires, automated (profile) estimated accuracy high (works well in specialized scope; fallback through IR) average (requires ‘initialization’, cf. cold start & sparsity) average (rules and profile must be given) PLE scenario support & usability average (PLE design phase not considered) good (currently only focus on PLE design); usable prototypes good; restricted to pre-def. domains; no cold-start problem privacy concerns sufficient anonymization privacy statement, anonymized activity recordings (=patterns) raw usage data not used; user profiles not addressed yet preliminary experiences preferences for Google results; uptake in business setting better three studies; works but requires pilot users sharing patterns (e.g. teachers) internal evaluations; efforts to integrate new data; requires modelling expertise
  • 9.
    • Please vote for our mediacast
    • if you like the idea of PLE practice sharing!
    Thanks for your attention! 11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©