Mash-Up Personal Learning Environments. Dr. Hendrik Drachsler

Transkript

1 Decision Support for Learners in Mash-Up Personal Learning Environments Dr. Hendrik Drachsler

2 Personal Nowadays Environments Blog Reader More Information Providers Social Bookmarking Various Communities Page 2 15.Dezember 2009

3 Personal Learning Environments co pe e ce o Page 3 15.Dezember 2009

4 PLEs are Mash-Ups. Mash-Up Personal Learning Environment MUPPLE

5 MUPPLEs follow the Learning Networks concept The learner is in centre stage, not the organization. Learners can publish, share, rate, tag and adjust learning content and customize and construct their own PLEs. Open Corpus that emerges form the bottom upwards (Koper & Sloep, 2002) Page 5 15.Dezember 2009

6 Informal learning = emergence Page 6 15.Dezember 2009

7 Selection problem because of the amount of data that is emerging in MUPPLEs. learners can be overwhelmed by the plethora of information. Page 7 15.Dezember 2009

8 Can Today, we create Recommender a Recommender Systems System for MUPPLEs? supporting our decisions i Page 8 15.Dezember 2009

9 What is ReMashed? A Mash-up environment that allows you to personalize emerging information of online communities with a recommender system. YoutellwhatkindofWeb20servicesyou of 2.0 use and then you are able to define which contributions of other members you like and do not like. Page 9 15.Dezember 2009

10 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 t t data sets for recommender systems in MUPPLEs. Page Dezember 2009

11 How does it work? ReMashed uses collaborative filtering to generate recommendations. It works by matching together users with similar il tastes t (neighbours) on different Web 2.0 resources (delicious, Flickr, blog feeds, Slideshare, Twitter, and YouTube). Page Dezember 2009

12 How does it work? Cold-Start = Tag-based recommendation Collaborative Filtering with ratings Page Dezember 2009

13 Version 1.0 Page Dezember 2009

14 Version 1.0 DUINE Prediction Engine Database of Items User Interface Page Dezember 2009

15 User Profile Page Dezember 2009

16 Future R&D Page Dezember 2009

17 You Please can use sign it as up well! at: Register at remashed.ou.nl. ReMashed starts t mashing. Enter your favorite Web 2.0 potatoes. Join the community. Taste your personal flavor of Web 2.0. Page Dezember 2009

18 Many thanks for your interest! This slide will be available here: Skype: celstec-hendrik.drachsler Blogging at: Twittering at: Page Dezember 2009