PLE Recommendations


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PLE Recommendations

  1. 1. May I suggest? Three PLE recommender strategies in comparison PLE Conference, Southampton, July 11-13, 2011 <ul><li>Felix Mödritscher (speaker) , Barbara Krumay </li></ul><ul><li>Vienna Univ. of Economics &Business, Austria </li></ul><ul><li>Sten Govaerts, Erik Duval </li></ul><ul><li>Katholieke Universiteit Leuven, Belgium </li></ul><ul><li>Ingo Dahn </li></ul><ul><li>University of Koblenz-Landau, Germany </li></ul>Sandy El Helou, Denis Gillet EPFL, Switzerland Alexander Nussbaumer, Dietrich Albert Graz University of Technology, Austria Carsten Ullrich Shanghai Jiao Tong University, China
  2. 2. Agenda <ul><li>PLEs and recommendations – why? </li></ul><ul><li>Three approaches from the ROLE project </li></ul><ul><ul><li>Federated Search Widget </li></ul></ul><ul><ul><li>Community-based PLE Recommender </li></ul></ul><ul><ul><li>Psycho-pedagogical Recommender </li></ul></ul><ul><li>Comparison of the PLE recommenders </li></ul>11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  3. 3. 1. Personal Learning Environments (PLEs) <ul><li>PLE = “ set of tools, services, and artefacts gathered from various contexts and to be used by learners ” [Henri et al., 08] </li></ul><ul><li>Characteristics of PLE-based activities: </li></ul><ul><li>several actors (different roles) ... </li></ul><ul><li>... use technology (tools) to ... </li></ul><ul><li>... connect to learner networks and ... </li></ul><ul><li>... collaborate on shared artefacts ... </li></ul><ul><li>... in order to achieve common goals. [Wild, 09] </li></ul><ul><li>Problems: Learners/teachers have varying attitudes and skills in using ICT! </li></ul><ul><li>can cause negative feelings or states (frustration, distraction, etc.); </li></ul><ul><li>hindering to proceed with learning or failing to achieve goals </li></ul><ul><li>[Windschitl & Sahl, 02; Nguyen-Ngoc & Law, 08] </li></ul>11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  4. 4. 1. PLEs and Recommendations <ul><li>Recommendations are necessary “if users have to make choices without sufficient personal experiences of alternatives ” [Resnick & Varian, 97] </li></ul><ul><li>For TEL: Examples described in the RecSysTEL workshop proceedings! </li></ul><ul><li>For PLEs: </li></ul><ul><ul><li>Pre-given PLE designs for specific needs </li></ul></ul><ul><ul><li>Possible PLE entities (artefacts, tools, peers) helpful for a specific situation </li></ul></ul><ul><li>Recommendations are a powerful instrument for empowering learners to design their PLEs and use technology for learning... </li></ul><ul><li>However: Different solution approaches driven by different disciplines... </li></ul><ul><li>And: CF techniques not sufficient! (global vs. local top-n) </li></ul>11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  5. 5. 2. Approach 1: Federated search widget ‘Binocs’ <ul><li>Aggregate heterogeneous resources from different (social media) repositories </li></ul><ul><li>Save, share, assess, and repurpose resources according to user’s interests </li></ul><ul><li>Actions taken into account: select resource, like/dislike, preview </li></ul><ul><li>Learning/social context derived from course </li></ul><ul><li>Forward contextual data to a recommender system (3A contextual ranking service, Graaasp [El Helou et al., 09] ) </li></ul><ul><li>Ranking according to previous interactions and relevance to search query </li></ul>11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  6. 6. 2. Approach 2: Community-based recommender ‘PLEShare’ <ul><li>Practice sharing repository on the Web; to be integrated into PLE solutions (Web-API) </li></ul><ul><li>Idea: users share PLE experiences voluntarily </li></ul><ul><li>Two demos: (a) PLEShare widget, (b) PAcMan add-on </li></ul><ul><li>PAcMan: allows designing tool bundles in the form of tagged bookmarking lists (=activities); simple features for sharing such activities and retrieving/reusing them </li></ul><ul><li>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] </li></ul><ul><li>Techniques: CF, clustering </li></ul>11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  7. 7. 2. Approach 3: Psycho-pedagogical recommender <ul><li>Developed according to theoretical models (self-regulated learning) and relevant taxonomies [Fruhmann et al., 10] </li></ul><ul><li>Based on learning goals and competences (learner monitoring and questionnaires) </li></ul><ul><li>Realised as widget for providing: (1) support for planning new activities; (2) guidance for ongoing activities; to find appropriate resources (artifacts, tools, peers) </li></ul><ul><li>Additional features planned: allowing learners to give feedback on recommendations (implicitly through usage data); provision of explanations; visual feedback on planned and completed activities </li></ul><ul><li>Techniques: rule/model-based recommender </li></ul><ul><li>Remark: no full-featured prototype available </li></ul>11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©
  8. 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. 9. <ul><li>Please vote for our mediacast </li></ul><ul><li>if you like the idea of PLE practice sharing! </li></ul><ul><li> </li></ul>Thanks for your attention! 11-13/07/2011 PLE Conference,Southampton, 2011 /9 , ©