Folksonomy

This new buzzword is very related to my research work. In fact by definition, available on wiki, folksnonomys are used to categorize Web Pages and other web content using ended labels called tags. This simple semantic approach is an interesting solution for my structuring knowledge problem. Maybe to simple, but yet an interesting approach. Of course the folksonomy term may occur in other contexts as well, but the process of tagging is considered very useful for searching, discovering, and navigating over time. Moreover a well-developed folksonomy is ideally accessible as a shared vocabulary that is both originated by, and familiar to, its primary users. In a web community the tagging sharing is rather interesting concept, where by sharing user-generated content they reach a common level of knowledge.

Trail Fire

As usual the Web 2.0 Show brought another interesting technology solution, which after hearing the podcast seemed very promising and the people involved what are doing . Well the concept is, based on a social collection of bookmarks; provide a trail of web sites, which can be posted. The interesting aspect is the filter usage, where users could read the posts on every side. Is works as a top layer where we can read the other users comments. The sites remain intact, but for the trailfire users it seems as if they are posted on the web site.

Existing Learner Models

In a recent study1) I came up with a nice way to classify different learner models:

Existing Learner Models

PAPI Learner: describes learner information for communication among cooperating systems. It has six categories

IMS LIP covers information similar to that found in a person’s CV, focusing more on the learner’s history and learning experience. This is due to the fact that LIP was developed to model the lifelong records of learners’ achievement and to transfer their records between institutions. Learner’s information in LIP is presented in eleven categories

eduPerson is a specification released jointly by Internet2 (www.internet2.edu) and Educause (www.educause.edu). Similar to PAPI and IMS LIP, eduPerson was designed to facilitate communication between higher education institutions, in particular to move information about people between US universities . The information covered by this standard is similar to the one found in an employee information system, as most of the elements hold data about the person and the organization they are a member of. Since its main purpose is exchanging data, the descriptions provided are very detailed comparing to other standards. eduPerson associates learner information with forty-three elements classified in two categories

Dolog LP is a learner profile suggested by Dolog et al that uses RDF (www.w3.org/TR/rdfprimer/) and learner ontologies to enable semantically enhanced learning systems to provide personalisation services. It takes advantage of the flexibility of RDF in encoding user profiles to include attributes from multiple schema, and the ability to add more attributes as necessary depending on how it will be used. Since the aim of Dolog LP is to provide personalisation services, the model was based on the combination of PAPI and IMS LIP. It describes a learner in five categories

FOAF is an RDF vocabulary that provides a set of properties and classes to describe people, documents and organizations. It was developed for building communities and social groupings. FOAF distinguishes five categories for describing a person:

Moreover the same paper compares all these five learning models based on a specified taxonomy

1)Towards a Semantic Modeling of Learners for Social Networks

Asma Ounnas, Ilaria Liccardi, Hugh C Davis, David E Millard, and Su A White, presented at AH06

http://www.win.tue.nl/SW-EL/2006/camera-ready/01-AsmaOunas.pdf

Web 2 Learn

The major task in a research PhD programme is the difficulty in tracking all the research flows around a particular topic. In fact even the definition of the topic is itself a daunting task. Well at least this task I have completed because now, after several months of thoughts, I have my Topic.
My research area is Web Based Learning Environments. Of course it is a very wide area, and in order to narrow it, I chose the Instructional Design on adaptive learning environments. That is I am more interested in developing a learning platform, which differentiate users with personalization and profiling properties.

Moreover, I am deeply interested in the new web technologies, namely the so called web 2.0, with the associated e-learning 2.0. This brings a new social dimension, especially the social bookmarking and social networking, which in my point of view will have an interesting usage in learning scenarios. In fact the argumentation theory, from the educational field, will be interesting to analyze using this approach. Another interesting theory, which can support my claims, is the Conectivism theory, presented by George Siemens, and I will be pleased to make my proof of concept based on his explanations to the new learning approaches.

Well my research is just giving it's first steps, but I am sure whatever my conclusions in the end might be, the research itself and the time thinking consumption on these interesting matters is worthy enough.