ICALT 2007 in Japan

Last week I attended the 7th IEEE Intenational Conference on Advanced Learning Technologies (ICALT 2007) in Niigata, Japan. It was indeed an exiting time there. I´ve been once in east Asia when I attended ICALT 2005 in Taiwan, but Japan is somehow different with an amazing mixture of traditional Japanese and modern Occidental lifestyle. I spent the first 2 nights in Tokyo and then moved to the harbor city Niigata one day after the earthquake. The conference was held in nice Toki Messe, Niigata Convention Center. Below are some pictures from my stay in Tokyo and Niigata. In a next post, I´ll write more about my experience there.

How to write a Google Gadget

Michael Mahemoff has written a nice introduction on the Google Gadget API and how to write a gadget, targeted at developers who already know Ajax. To define gadgets, the author writes:

The gadget is an XML file sitting on your server. In my case, http://ajaxify.com/run/widgets/google/diggroundup.xml. It will get cached, so effectively it must be a static file.

The user adds your gadget to their igoogle portal, or codes it into their own website, by specifying this URL (it may be done indirectly - via the gadget registry. You'll appear in the registry if you've submitted your gadget to igoogle.)

The gadget is rendered as an iframe, so you have all the usual security constraints which stop you borking the portal and other gadgets. This also means you can't communicate with other Gadgets other than via than remote calls to a common third-party server (or has anyone tried hooking them together using the iframe-fragment identifier hack? )

Google vs Everyone

Adam Ostrow takes a look at 10 markets where Google wants to win.

1. Search
2. Advertising
3. Video
4. Blogging
5. Mobile
6. Start Pages
7. Communications
8. Social Networking
9. Photo Sharing
10. Office Suite

The author writes "Google is the 600-pound gorilla: the company that no one wants to see build a competing product. Google dominates many of the markets it enters, whether by building a superior product or acquiring one".

ALOA: A Web Services Driven Framework for Automatic Learning Object Annotation

Yesterday, Nanda Firdausi Muhammad successfully defended his master thesis "ALOA: A Web Services Driven Framework for Automatic Learning Object Annotation". Under my supervision, Nanda has designed, implemented, and evaluated a Web Services driven framework for IEEE LOM compliant automatic matadata generation. The primary focus has been on the flexibility and extensibility of the framework, such that new metatata generation services/modules can easily be plugged into the basic system.

The proposed solution and the implemented system fulfill these requirements; the system already implements different modules and is capable of generating a big part of the LOM metadata from different types of learning objects (e.g. HTML, PDF, PPT, Word). The goal of flexibility was achieved as the system provides a public Web Services API that can be used by third party applications and has a SOA based architecture that makes it possible to extend the framework with new components.

The main components of ALOA are Extractors and Generators. An extractor is responsible for extracting text information from a learning object along with more properties about the learning object. Only one extractor can be defined for each learning object type e.g. html, pdf, ppt, word. A generator is responsible for the actual metatada generation. It uses the output of an extractor and applies data mining techniques to generate one or parts of the metadata. Everyone should be able to easily create a new extrator/generator and plug it into ALOA via the admin interface. A detailed documentation on how to implement a new extractor or generator is available. Please do not hesitate to contact me if you would like to create your own component/service/module or need more information about the ALOA framework.

This thesis was a successful cooperation between Informatik 5, RWTH Aachen University and the computer science department of the Katholieke Universiteit Leuven in the framework of the EU Network of Excellence PROLEARN.


Try it out! Your valuable feedbacks, suggestions, comments, and ideas are welcome!

The Future of Learning and Knowledge Management

Related to the posts:

• Learning and knowledge management are 2 sides of the same coin
  

• Deficiencies in current knowledge management approaches
  

Learning and knowledge are social, personal, flexible, dynamic, distributed, ubiquitous, complex, and chaotic in nature. We need thus to rethink how we design new models for learning and knowledge management (KM) that mirror those characteristics. The table below illustrates seven critical factors that must be addressed to ensure that future learning and KM models will endure. In the modern media and knowledge-intensive era of collaboration culture, the one-size-fits-all, centralized, static, top-down, and knowledge-push models of traditional learning and KM initiatives need to be replaced with a more social, personalized, open, dynamic, emergent, and knowledge-pull model for learning and KM.

Success Factors	Requirements
Knowledge networking and community building	Learning and KM models need to recognize the social aspect of learning and knowledge and as a consequence place a strong emphasis on knowledge networking and community building to leverage, sustain, and share knowledge in a collaborative way.
Content-centric vs. user-centric	Recognizing that learning and knowledge are personal, learning and KM approaches require a move away from one-size-fits-all content-centric models, and move towards a user-centric model that puts the learner/knowledge worker at the centre and gives her the control.
Centralized vs. distributed	Learning and KM solutions need to operate with a more decentralized and socially open approach, based on small pieces, loosely joined and distributed control.
Top-down vs. bottom-up	Learning and KM solutions need to follow an emergent bottom-up approach, driven by the learner/knowledge worker and based on sharing rather than controlling.
Knowledge-push vs. knowledge-pull	Recognizing that learning and knowledge are dynamic and flexible in nature, learning and KM approaches require a shift in emphasis from a knowledge-push to a knowledge-pull model.
Adoption	For learning and KM approaches to be adopted, their systems need to be both simple and useful (high Perceived ease of use PEOU and Perceived usefulness PU).
Knowledge sharing culture and trust	A bottom- up approach and distributed control build a base for successful knowledge sharing and trust. Encouraging people to build their personal social networks and join communities based on their needs helps to ensure trust and motivates them to share.

Critical success factors for future learning and knowledge management initiatives

Source:

Chatti, M. A., Jarke, M. and Frosch-Wilke, D. (2007) ‘The future of e-Learning: a shift to knowledge networking and social software‘, To be published in the International Journal of Knowledge and Learning IJKL 3(4).