ReMashed – Recommendation Approaches
         for Mash-Up Personal Learning
    Environments in Formal and Informal
      ...
Personal Environments
                     Nowadays …


                                                           More In...
Personal Learning Environments




hendrik.drachsler@ou.nl
EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR
Page 3 | September 2...
Related Work
• Pedagogical Scenarios
    Formal Learning (e.g. Hermans & Verjans, 2009)
   Informal Learning (e.g. Wild,...
Selection problem because …

…of the amount of
 data that is
 emerging in
 MUPPLEs.

…learners can be
 overwhelmed by
 the...
Can we create a Recommender System
    Today, Recommender Systems
 for MUPPLEs? our decisions
       supporting




hendri...
What is ReMashed?

A Mash-up environment that allows you to
  personalize emerging information of online
  communities wit...
Goals for ReMashed
1. End-User level
   Providing a recommender system for Web 2.0
   sources of learners in MUPPLEs.
2. R...
The 1st Release




hendrik.drachsler@ou.nl
EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR
Page 9 | September 28, 2009
The 2nd Release




hendrik.drachsler@ou.nl
EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR
Page 10 | September 28, 2009
The 2nd Release

                                             DUINE Prediction
                                           ...
How does it work?

ReMashed uses collaborative filtering to
 generate recommendations.

It works by matching together user...
How does it work?

      Cold-Start = Tag-based recommendation




               Collaborative Filtering with ratings


h...
User Profile




hendrik.drachsler@ou.nl
EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR
Page 14 | September 28, 2009
Context Variables
Formal Learning (Top-down environments)
    Curriculum (Closed-Corpus)
    Teacher directed
    Prede...
Recommendation Approaches



      Learning settings, environmental conditions
      and the task greatly affect the desig...
Formal Recommendation
                     Approach
            Adaptive                         Learning Goal Layer
     ...
Informal Recommendation
                   Approach
             Hierarchical
                                            ...
Conclusions

                                                   Fed by bottom-up
                                         ...
Future R&D




hendrik.drachsler@ou.nl
EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR
Page 20 | September 28, 2009
You can use it as well!
                      Please sign up at:
                          Register at         ReMashed
  ...
Many thanks for your interest!

               This slide is available here:
               http://www.slideshare.com/Drac...
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ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings at MUPPLE09 in ECTEL 2009, Nice, fr

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  • A solution towards this problem might be recommender system technology. The main purpose of recommender systems on the Internet is to pre-select information a user might be interested in. For instance, the well-known company amazon.com [8] is using a recommender system to direct the attention of their users to other products in their collection. The motivation for a recommender system for Mash-Up Personal Learning Environments is to improve the ‘educational provision’; to offer a better goal attainment and to spend less time to find suitable learning material. Therefore, we developed a recommender system that offers advice to learners based on their Web 2.0 resources regarding the most suitable learning materials to meet their individual competence development.
  • Organization can pre-structure and control available learning goals, knowledge domains etc. Maintenance effort, design activities needed before the runtime , Ontologies Metadata Predefined learning paths
  • It builds up a hierarchy of items by continuously merging the two most similare items / groups into a new group Measure of most frequently used keywords for each blog posting by simp Open Corpus Absence of maintenance and structure Metadata Predefined learning paths Nearly no maintenace improve through emerging behaviour le using words counts or Reuters service
  • ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings at MUPPLE09 in ECTEL 2009, Nice, fr

    1. 1. ReMashed – Recommendation Approaches for Mash-Up Personal Learning Environments in Formal and Informal Learning Settings Hendrik Drachsler, Dries Pecceu, Tanja Arts, Edwin Hutten, Peter van Rosmalen, Hans Hummel & Rob Koper
    2. 2. Personal Environments Nowadays … More Information Blog Reader Providers Social Bookmarking Various Communities hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 2 | September 28, 2009
    3. 3. Personal Learning Environments hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 3 | September 28, 2009
    4. 4. Related Work • Pedagogical Scenarios  Formal Learning (e.g. Hermans & Verjans, 2009) Informal Learning (e.g. Wild, Kalz & Palmer, 2008) • Use Case Studies 4 experiments (e.g Van Harmelen, 2008) • Technology Development Language design for a PLE (e.g. Moederitscher, Wild, Sigurdarson, 2008) Widget Interoperability (e.g. Sire, Vagner, 2008) http://mindmeister.com/15237440/r-d-on-mupples hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 4 | September 28, 2009
    5. 5. Selection problem because … …of the amount of data that is emerging in MUPPLEs. …learners can be overwhelmed by the plethora of information. hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 5 | September 28, 2009
    6. 6. Can we create a Recommender System Today, Recommender Systems for MUPPLEs? our decisions supporting hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 6 | September 28, 2009
    7. 7. What is ReMashed? A Mash-up environment that allows you to personalize emerging information of online communities with a recommender system. You tell what kind of Web 2.0 services you use and then you are able to define which contributions of other members you like and do not like. hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 7 | September 28, 2009
    8. 8. Goals for ReMashed 1. End-User level Providing a recommender system for Web 2.0 sources of learners in MUPPLEs. 2. Researcher level 1. Offering researchers a system for the evaluation of recommendation algorithms for learners in MUPPLEs. 2. Creating user-generated-content data sets for recommender systems in MUPPLEs. hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 8 | September 28, 2009
    9. 9. The 1st Release hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 9 | September 28, 2009
    10. 10. The 2nd Release hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 10 | September 28, 2009
    11. 11. The 2nd Release DUINE Prediction Engine Database User Interface of Items hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 11 | September 28, 2009
    12. 12. How does it work? ReMashed uses collaborative filtering to generate recommendations. It works by matching together users with similar tastes (neighbours) on different Web 2.0 resources (delicious, Flickr, blog feeds, Slideshare, Twitter, and YouTube). hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 12 | September 28, 2009
    13. 13. How does it work? Cold-Start = Tag-based recommendation Collaborative Filtering with ratings hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 13 | September 28, 2009
    14. 14. User Profile hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 14 | September 28, 2009
    15. 15. Context Variables Formal Learning (Top-down environments)  Curriculum (Closed-Corpus)  Teacher directed  Predefined learning resources, learning goals  Maintenance Informal Learning (Bottom-up environments)  More self-directed learning goals  Responsible for own learning pace / path  Learning resources from different providers (Open-Corpus)  Lack of maintenance hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 15 | September 28, 2009
    16. 16. Recommendation Approaches Learning settings, environmental conditions and the task greatly affect the design of recommender systems in TEL. systems in TEL. hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 16 | September 28, 2009
    17. 17. Formal Recommendation Approach Adaptive Learning Goal Layer Sequencing (top-down) Conceptual Layer Content Layer hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 17 | September 28, 2009
    18. 18. Informal Recommendation Approach Hierarchical Clustering Layer n clustering (bottom-up) Clustering Layer 1..n Content Layer hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 18 | September 28, 2009
    19. 19. Conclusions Fed by bottom-up approach Fed by top-down approach hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 19 | September 28, 2009
    20. 20. Future R&D hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 20 | September 28, 2009
    21. 21. You can use it as well! Please sign up at: Register at ReMashed remashed.ou.nl. starts mashing. http://remashed.ou.nl Enter your favorite Taste your Web 2.0 potatoes. personal flavor of Web 2.0. Join the community. hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 21 | September 28, 2009
    22. 22. Many thanks for your interest! This slide is available here: http://www.slideshare.com/Drachsler Email: hendrik.drachsler@ou.nl Skype: celstec-hendrik.drachsler Blogging at: http://elgg.ou.nl/hdr/weblog Twittering at: http://twitter.com/HDrachsler hendrik.drachsler@ou.nl EC-TEL 2009, 2nd MUPPLE workshop, Nice, FR Page 22 | September 28, 2009

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