Collaborative Filtering for Teaching in a Learning of 3.0 Environment

Juhaida Abdul AzizParilah M Shah

Rosseni DinRashidah Rahmat

Universiti Kebangsaan Malaysia

AbstractWho involve???

educators teachers those interested in web applications

T & L collaboratively: diverse life styles cultures religion

What to look??• Previous studies• Web 2.0 in education e.g. Wikis, Blogs,

Twitter• Multimodal online information• Knowledge repositories• Compare & contrast of Web 1.0 & web 2.0

technologies

What to look??

• 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.

Introduction• World Wide Web (www) search anything,

anytime and anywhere without boundaries.• 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.

Introduction• 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.

Related Studies• Researches use several recommendation strategies namely:• Collaborative filtering• Data mining techniques • Content-based filtering • Clustering, knowledge discovery, etc

(Ghauth & Abdullah 2009).

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

Researchers Recommendation

Strategies Input Output

Bandura (1997), Brusilovsky (2001) and Adomavicius (2005),

Data mining techniqueslearner’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)

Related studies

• 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.

• Though the technologies are personalized , improvement is necessary to suit the learners’ quality preferences and expectations.

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).

• 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)

Some thoughts to be shared on evolutions of webs

Web 1.0- The Information Portal

Web 2.0- The Web as Platform

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

Web 3.0- Semantic and Intelligent Web

What is Web 3.0 - based Teaching and Learning?

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

Web 3.0?

• Collaborative filtering•User-based method• Content-based method•Matrix Factorization

• Content-based filtering• Hybrid:•Linear/sequential/switching combination

T & L: Preference prediction

Collaborative Filtering (CF)Content-based method (2001), deployed at Amazon; Eg:•I have watched so many good & bad movies.•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 likedepend on how much I liked those early movies.

• 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).

Collaborative Filtering (CF)

•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.

What is E-learning Recommender Systems (RS)

??To recommend to us something we may like• It may not be popular

How?• Based on our history of using services• Based on other people like us• Ever heard of “collective intelligence”?

Adapted from http://truyen.vietlabs.com

Ever heard of•GroupLens?• Amazon recommendation?• Netflix Cinematch?• Google News personalization?• Strands?• TiVo?• Findory?

Adapted from http://truyen.vietlabs.com

Want some evidences?(Celma & Lamere, ISMIR 2007)

•Netflix: 2/3 rented movies are from recommendation•Google News: 38% more click-through are due torecommendation•Amazon: 35% sales are from recommendation

Adapted from http://truyen.vietlabs.com

But, what do recommendersystems 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

Adaptive Hypermedia Systems (AHS)

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).

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?

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.

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).

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.

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.

Thank you