Juhaida Abdul Aziz Parilah M Shah Rosseni Din Rashidah Rahamat

Collaborative Filtering for Teaching in a Learning of 3.0

Environment

Juhaida Abdul Aziz

Parilah M Shah

Rosseni Din

Rashidah Rahamat

Education Comparative in Curriculum for Active Learning Between Indonesia and Malaysia

Abstract

Who involve?

Who involve?

Educators Educators

Teachers Teachers

Interested in web application

Interested in web application


Abstract

diverse life styles

cultures

religion

Collaborative T & LCollaborative T & L


Web 2.0 in education e.g. Wikis, Blogs,

Twitter

Multimodal online

information

Previous studies

What to look?


What to look?

Knowledge repositoriesKnowledge repositories

Compare & contrast of Web 1.0 & web 2.0 technologies

Compare & contrast of Web 1.0 & web 2.0 technologies


What to look?

• How it could be combined with 2.0 in T & L?Web 3.0

(Semantic Web)

• Filter out whatever unwanted and allow what the users want.

Intelligent Agents


Introduction

WWW RS CF AHS

World Wide Web (www) search anything, anytime and anywhere without boundaries.

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.

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

DMT CBF C & KD

Collaborative filtering

Data mining techniques

Content-based filtering

Clustering, knowledge discovery, etc

Researches use several recommendation strategies namely:

CF

(Ghauth & Abdullah 2009)


Related Studies

RS

Factor of limited time hinders learners from locating suitable learning information, they may end up with unsuitable material.

Nachmais(2003):

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.

AHS


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

ResearchersRecommendation

Strategies Input Output

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 (Ghauth & Abdullah 2009)


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).

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 of Web

Web 1.0Web 1.0 Web 2.0Web 2.0 Web 3.0Web 3.0PC EraPC Era

The desktop

The world wide

web

The social web

The semantic

webWeb 4.0Web 4.0

The intelligent

web


Evolution of Web

source: http://hlwiki.slais.ubc.ca/index.php/Web_3.0

http://hlwiki.slais.ubc.ca/index.php/Web_3.0


Point to Ponder


History of Web


History of Web


Web 1.0- The Information Portal


Web 1.0- The Information Portal


Web 2.0- The Web as Platform






Web 3.0- Semantic & Intelligent Web

Wheeler (2009) predicted the e-learning of web 3.0 is to have at least four key drivers:

Distributed Computing

Collaborative intelligent filtering

Extended smart mobile technology

3D visualisation interaction


Web 3.0- Semantic & Intelligent Web


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)






T & L: Preference prediction

• User-based method• Content-based method• Matrix FactorizationCollaborative

filtering

• Linear/sequential/switching combinationContent-based

filtering & Hybrid


Collaborative Filtering (CF)

• I have watched so many good & bad movies.

• Would you recommend me watching “Fast and Furious 5”?

Content-based method (2001),

deployed at Amazon; Eg:

• 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.

The idea is



• I tend to watch this movie because I have watched those movies … or

In short:

• People who have watched those movies also liked this movie.

(Amazon style)




Collaborative Filtering

• CF is an alternative method to rate “similar” users to predict the items that have not being rated.Kangas

(2002)

• has the control to filter out whatever unwanted and allow what the users want. CF


What is E-learning Recommender Systems (RS)?

Based on other people like us;

Ever heard of “collective

intelligence”?

To recommend to us something we may like & it

may not be popular

Based on

our history

of using

services

HOW?

Adapted from http://truyen.vietlabs.com


Ever heard of……

• GroupLens?

• Amazon recommendation?

• Netflix Cinematch?

• Google News personalization?

• Strands?

• TiVo?

• Findory?



Want some evidences?

• Netflix: 2/3 rented movies are from

recommendation• Google News: 38% more click-through are due to• recommendation• Amazon: 35% sales are from recommendation



But, what do RS 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?

A

Are these aspects fit into the Malaysian educational context? Are the teachers ready to implement in their teaching approach?

CF?

RS? AHS?


Questions by Wheeler (2009);

• To accept or deny the use of web 3.0.Users

Choice?

• To accept the technologies.Teachers’ Willingness?


Questions by Wheeler (2009);

• To be autonomous learners and mind setting towards 3.0 learning environment

• & the success and failure of web 2.0.Students’ Readiness?


As a Matter of Fact,

Malaysian educational system is

exam-oriented (The Star

Online 2006 & Tun Hussin

2006).

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.

Documents

Juhaida Abdul Aziz Parilah M Shah Rosseni Din Rashidah Rahamat