The amount of information sources and the available data is growing dramatically fast nowadays. It is very difficult time for teachers to keep up with changes, especially in information domain, and to find new and appropriate sources of information; this problem also affects e-Learning. Contemporary e-Learning systems deliver predefined, rigid courses which usually do not take into account user specific conditions, like wishing to broaden his or her knowledge in wide range of domains at the same time. Without constant maintenance, electronic courses are also getting outdated. Moreover, all of the current solutions seem to underestimate the potential of informal learning.
IKHarvester (Informal Knowledge Harvester) is a SOA layer for Didaskon, its Didaskon. It provides REST
based Web Services for harvesting data from SSIS (blogs, wikis and JeromeDL, a social semantic digital library),
saving them in the informal knowledge repository, and providing them in a form of informal Learning Objects
(LOs) that are described accroding to LOM (Learning Object Metadata) standard.
The Didaskon project delivers a framework for composing an on-demand curriculum
from existing learning objects provided by e-Learning services. The selection and work-flow
scheduling of learning objects is based on the semantically annotated specification of the user’s
profile consisting of current knowledge (pre-conditions), anticipated resulting skills (goal) and the
technical details of the client’s platform.
We describe the architecture of Didaskon: the main subsystems and their interfaces. We show pos-
sible types of learning paths which Didaskon can suggest to a student. We describe other external
components to be used in our system. We characterise the composition algorithm, which will cre-
ate the course. We describe the implementation process and provide API documentation of created
classes. Finally, we analyse possible treats and problems which we can face in further work.
Composer component provides interface for composing course for a student. In general this is the only interface
that is directly used by external user application. It cooperates with FOAFRealm, LOST and SSIS components.
E-Learning grows on the fertile soil of the Internet technologies; it fails, however, to reach their full potential. With new, emerging technologies of the second generation Internet there is even more to be captured and adopted: knowledge sharing with blogs, wikis, and social bookmarking services. In this article we argue that those technologies can be adapted to improve user experience in e-Learning; we present an online social bookmarking system called social semantic collaborative filtering. SSCF supports SIOC metadata which ultimately transforms it in to a browser of blogs, fora, and other community sites. We show how a digital library system, such as JeromeDL, utilizing this technology can be used in the e-Learning process, which takes advantage of recent research in the Internet.
For a long time, the Internet has been playing a
great role in our lives; it entertains but also educates.
There are a lot of blogs, wikis, fora, social
bookmarking and social media services. Collectively
called online communities, they create networks where
users can feel free to band together: share ideas and
opinions, publish links and works and comment them.
In fact, online communities live by virtue of
collaborating users. There are a lot of blogs and wikis
covering specific domain of interest; they are treasuries
of knowledge in that domain. Thus, online communities
are powerful source of informal knowledge.
Current e-learning systems deliver predefined courses tailored for a generic student on
one of generic levels of skills or knowledge. Learning services usually do not take into account user
specific conditions, such as on aspiration to broaden his / her knowledge in a wide range of domains
at the same time; therefore, student has to ”attend” different courses at the same time. Also, current
systems do not employ informal source of knowledge, such as social semantic information sources
(semantic wikis, semantic blogs, social semantic digital libraries), multimedia, or bookmarking ser-
vices.
We describe the state of the art in learning objects descriptions, user profiles and composition al-
gorithms; we analyze existing solution.Finally we draw conclusions that will help us to understand
main problems we are likely to encounter while developing the Didascon system.
The amount of information on the web is constantly growing caus-
ing an erosion of its usefulness through information overload. The Semantic Web
copes with this erosion by creating a web in which information is interlinked
and effectively queried. However, the question arises how to transform the infor-
mation on the Web into a graph of semantically interlinked metadata? Currently
there is no one global solution to this problem; a number of methods for provid-
ing/acquiring semantics were created but none are widely adopted. Additionally,
a lot of semantics are delivered by online communities in the form of tags; there
is no standard way to utilize this information either.
In this paper we introduce a standard process of harvesting web semantics. The
process employs existing methods to collect or acquire data from web resources;
it also enriches the gathered information with semantics from social annotations.
IKHarvester is a prototype that implements this process to gather the maximum
amount of semantics possible from a given source. As a result IKHarvester cre-
ates and delivers interlinked semantic data enriched with community annotations,
accessible to other applications.
Only recently, researchers and practitioners alike have begun to fully understand the potential of eLearning and have concentrated on new tools and technologies for creating, capturing and distributing knowledge. In order to support and extend those solutions we propose the idea of incorporating the informal knowledge into Learning Management Systems. Contributing to the body of research, problems of existing eLearning technologies are documented highlighting areas of definite improvement. Finally, sematic web harvesting technology as a solution is explored in the form of the knowledge acquisition tool called IKHarvester.
(C) Copyright 2003-2006 by Digital Enterprise Research Institute (DERI) and WETI & Main Library Gdansk University of Technology, Poland and Sebastian Ryszard Kruk.
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