1. Collaborative Filtering for Teaching in a Learning of 3.0 Environment Juhaida Abdul Aziz Parilah M Shah Rosseni Din Rashidah Rahmat Universiti Kebangsaan Malaysia

2. Abstract Who involve? Educators Educators Teachers Teachers Those interested in technology and internet applications applications T & L collaboratively: diverse life styles cultures religion Abstract Who involve??? educators  educators  teachers  those interested in web applications T & L collaboratively:  diverse life styles  cultures  religion

3. What to look?? Previous studies Web 2.0 in education e.g. Wikis, Blogs, Twitter Web 2.0 in education e.g. Wikis, Blogs, Twitter Multimodal online information Multimodal online information Knowledge repositories Knowledge repositories Compare & contrast of Web 1.0 & web 2.0 technologies Compare & contrast of Web 1.0 & web 2.0 technologies

4. What to look?? Web 3.0 (semantic web ) -how it could be combined with 2.0 in T & L? Web 3.0 (semantic web ) -how it could be combined with 2.0 in T & L? The ‘intelligent agents’ - filter out whatever unwanted and allow what the users want. The ‘intelligent agents’ - filter out whatever unwanted and allow what the users want.

5. Introduction World Wide Web (www) search anything, anytime and anywhere without boundaries. World Wide Web (www) search anything, anytime and anywhere without boundaries. Recommender systems (RS) commonly used to help search the desired items. Recommender systems (RS) commonly used to help search the desired items. RS in e-learning differed depending on the objects to be recommended; e.g. course to enrol, learning materials and etc. RS in e-learning differed depending on the objects to be recommended; e.g. course to enrol, learning materials and etc.

6. Introduction Collaborative Filtering (CF), a system that can find users with similar interests and preferences. Collaborative Filtering (CF), a system that can find users with similar interests and preferences. Adaptive Hypermedia System (AHS) share the same goal; personalize the materials to learners’ needs. Adaptive Hypermedia System (AHS) share the same goal; personalize the materials to learners’ needs.

7. Related Studies Researches use several recommendation Researches use several recommendation strategies namely: strategies namely: Collaborative filtering Collaborative filteringCollaborative filteringCollaborative filtering Data mining techniques Data mining techniquesData mining techniquesData mining techniques Content-based filtering Content-based filteringContent-based filteringContent-based filtering Clustering, knowledge discovery, etc Clustering, knowledge discovery, etcClustering,knowledge discoveryClustering,knowledge discovery (Ghauth & Abdullah 2009).

8. Table1. Recommendation strategies, input, and output of the current research Researchers Recommendation StrategiesInputOutput Bandura (1997), Brusilovsky (2001) and Adomavicius (2005), Data mining techniques learner’s activities/ access history, learners rating, item attributes related items/ documents, related links, learning activities, courseware module Bandura (1997), Brusilovsky (1998), and Castells (2007) Collaborative filtering Bandura (1997), Brusilovsky (2007), Content-based filtering Brusilovsky (2007), Castells (2007), Chen (2005) Clustering, Knowledge discovery, metadata Adopted from (Ghauth & Abdullah 2009)

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

10. What is WWW?? 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). 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). Evolution? Evolution? 1.PC Era (the desktop) 2.Web 1.0 ( the world wide web) 3.Web 2.0 ( the social web) 4.Web 3.0 ( the semantic web) 5.Web 4.0 ( the intelligent web)

11. Some thoughts to be shared on evolutions of webs

15. Web 1.0- The Information Portal Web 1.0- The Information Portal

17. Web 2.0- The Web as Platform

20. Wheeler (2009) predicted the e-learning of web 3.0 is to have at least four key drivers: a) Distributed computing Distributed computingDistributed computing b) Extended smart mobile technology b) Extended smart mobile technologyExtended smart mobile technologyExtended smart mobile technology c) Collaborative intelligent filtering c) Collaborative intelligent filtering d) 3D visualisation interaction 3D visualisation interaction3D visualisation interaction Web 3.0- Semantic and Intelligent Web

23. What is Web 3.0 - based Teaching and Learning?

24. Web 3.0 technologies; (mobile learning, immersive technologies, and the semantic web are custom made for learning)

25. Web 3.0?

27. Collaborative filtering User-based method Content-based method Matrix Factorization Content-based filtering Hybrid: Linear/sequential/switching combination T & L: Preference prediction

28. Collaborative Filtering (CF) Collaborative Filtering (CF) Content-based method (2001), deployed at Amazon; Eg: I have watched so many good & bad movies. I have watched so many good & bad movies. Would you recommend me watching “Fast and Furious 5”? Would you recommend me watching “Fast and Furious 5”? The idea is to pick from my previous list 20-40 movies that share similar audience with “Fast and Furious 5”, then how much I will like The idea is to pick from my previous list 20-40 movies that share similar audience with “Fast and Furious 5”, then how much I will like depend on how much I liked those early movies.

29. In short: I tend to watch this movie because I have watched those movies … or In short: I tend to watch this movie because I have watched those movies … or People who have watched those movies also liked this movie (Amazon style). People who have watched those movies also liked this movie (Amazon style).

31. Collaborative filtering (CF) is an alternative method to rate “similar” users to predict the items that have not being rated. Kangas (2002) Collaborative filtering (CF) is an alternative method to rate “similar” users to predict the items that have not being rated. Kangas (2002) CF has the control to filter out whatever unwanted and allow what the users want. CF has the control to filter out whatever unwanted and allow what the users want.

32. What is E-learning Recommender Systems (RS)??E-learning Recommender Systems (RS) To recommend to us something we may like It may not be popular It may not be popular How? Based on our history of using services Based on other people like us Ever heard of “collective“collective intelligence” intelligence”? Adapted from http://truyen.vietlabs.com

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

34. Want some evidences? (Celma & Lamere, ISMIR 2007) Netflix:  2/3 rented movies are from recommendation Google News:  38% more click-through are due to recommendation Amazon:  35% sales are from recommendation Adapted from http://truyen.vietlabs.com

35. But, what do recommender systems do, exactly? 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. Adapted from http://truyen.vietlabs.com

36. Adaptive Hypermedia Systems (AHS)

37. 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). 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 Hypermedia systemAdaptive Hypermedia system 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 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).

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

39. Questions by Wheeler (2009); 1.Some aspects such as the users’ choice to accept or deny the use of web 3.0. 2.The teachers’ willingness to accept the technologies. 3.The students’ readiness to be autonomous learners and mind setting towards 3.0 learning environment as well as the success and failure of web 2.0.

40. As a Matter of Fact,  Malaysian educational system is exam- oriented (The Star Online 2006 & Tun Hussin 2006).  Instead, Malaysians need a fresh and new philosophy in their approach to exams (Ahmad 2003).

41. As a Matter of Fact,  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.

42. Conclusion Future study The users need to be involved in a lot of speculation in the buzz of digital and education. Lots of efforts into materialising the changing for T and L to take place around the technology. Ensure technology 3.0 won’t do any harm to the users. Teachers/ educators need to discuss and scrutinise their practice and make explicit pedagogies underpin to meet the current demands.

43. Thank you