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Collaborative Filtering for Teaching in a Learning of 3.0 Environment

Collaborative Filtering for Teaching in a Learning of 3.0 Environment. Juhaida Abdul Aziz Parilah M Shah Rosseni Din Rashidah Rahamat. Abstract. Who involve?. Educators. Teachers. Interested in web application. Abstract. diverse life styles . cultures. Collaborative T & L.

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Collaborative Filtering for Teaching in a Learning of 3.0 Environment

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  1. Collaborative Filtering for Teaching in a Learning of 3.0 Environment Juhaida Abdul Aziz Parilah M Shah Rosseni Din RashidahRahamat

  2. Abstract Who involve? Educators Teachers • Interested in web application

  3. Abstract diverse life styles cultures Collaborative T & L religion

  4. What to look? Previous studies Web 2.0 in education e.g. Wikis, Blogs, Twitter Multimodal online information

  5. What to look? Knowledge repositories • Compare & contrast of Web 1.0 & web 2.0 technologies

  6. What to look?

  7. Introduction CF AHS WWW RS Recommender systems (RS) commonly used to help search the desired items, depending on the objects to be recommended; e.g. course to enrol, learning materials etc. Adaptive Hypermedia System (AHS) share the same goal; personalize the materials to learners’ needs. World Wide Web (www) search anything, anytime and anywhere without boundaries. Collaborative Filtering (CF), a system that can find users with similar interests and preferences.

  8. Related Studies Researches use several recommendation strategies namely: CBF C & KD CF DMT Content-based filtering Clustering,knowledge discovery, etc Collaborative filtering Data mining techniques (Ghauth & Abdullah 2009)

  9. Related Studies Nachmais (2003): RS AHS Though the technologies are personalized , improvement is necessary to suit the learners’ quality preferences and expectations. Some researchers identified these in RS & AHS, proposed some solutions to overcome the problems. Factor of limited time hinders learners from locating suitable learning information, they may end up with unsuitable material.

  10. Table1. Recommendation strategies, input, and output of the current research

  11. What is WWW ?? Users use and navigate hyperlinks to view pages that consist of texts, images and other multimodal sources to suit their needs (Kekre, et al. 2009).

  12. Evolution of Web PC Era Web 1.0 Web 2.0 Web 3.0 Web 4.0 The world wide web The social web The semantic web The desktop The intelligent web

  13. Evolution of Web source:http://hlwiki.slais.ubc.ca/index.php/Web_3.0

  14. Point to Ponder

  15. History of Web

  16. History of Web

  17. Web 1.0- The Information Portal

  18. Web 1.0- The Information Portal

  19. Web 2.0- The Web as Platform

  20. Web 2.0- The Web as Platform

  21. Web 2.0- The Web as Platform

  22. Web 3.0- Semantic & Intelligent Web Wheeler (2009) predicted the e-learning of web 3.0 is to have at least four key drivers: Extended smart mobile technology 3D visualisation interaction Collaborative intelligent filtering Distributed Computing

  23. Web 3.0- Semantic & Intelligent Web

  24. What is Web 3.0 - based T & L?

  25. What is Web 3.0 - based T & L? WHY??? Web 3.0 technologies; (mobile learning, immersive technologies, and the semantic web are custom made for learning)

  26. What is Web 3.0 - based T & L?

  27. What is Web 3.0 - based T & L?

  28. T & L: Preference prediction

  29. Collaborative Filtering (CF)

  30. Collaborative Filtering (CF)

  31. Collaborative Filtering (CF)

  32. Collaborative Filtering

  33. What is E-learning Recommender Systems (RS)? HOW? Adapted from http://truyen.vietlabs.com

  34. Ever heard of…… Adapted from http://truyen.vietlabs.com • GroupLens? • Amazon recommendation? • Netflix Cinematch? • Google News personalization? • Strands? • TiVo? • Findory?

  35. Want some evidences? Adapted from http://truyen.vietlabs.com • Netflix: • 2/3 rented movies are from recommendation • Google News: • 38% more click-through are due to • recommendation • Amazon: • 35% sales are from recommendation

  36. But, what do RS do, exactly? Adapted from http://truyen.vietlabs.com 1. Predict how much you may like a certain product/service. 2. Compose a list of N best items for you. 3. Compose a list of N best users for a certain product/service. 4. Explain to you why these items are recommended to you. 5. Adjust the prediction and recommendation based on your feedback and other people.

  37. Adaptive Hypermedia Systems (AHS)

  38. Ever heard of Adaptive Hypermedia System? • Using a set of algorithms while interacting to the Adaptive Hypermedia system, (AHS) user can select the most appropriate content to be presented (Bhosale 2006). • Adaptive educational hypermedia tailors what the learner sees to the learner's goals, abilities, needs, interests, and knowledge of the subject, i.e.by providing hyperlinks that are most relevant to the user (Wikipedia).

  39. What to recommend for T & L in Malaysian context? • A • Are these aspects fit into the Malaysian educational context? • Are the teachers ready to implement in their teaching approach? AHS? CF? RS?

  40. Questions by Wheeler (2009);

  41. Questions by Wheeler (2009);

  42. As a Matter of Fact,

  43. A big turning point of new policy has been taken by the Malaysian Ministry of Education based on school assessment & in line with other countries like the US, Britain, Germany, Japan and Finland. As a Matter of Fact,

  44. Conclusion

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