Information Technology Reference
In-Depth Information
System quality implies an information process
system quality based on production of produced
information. Information quality is defined as the
quality of system product outputs, and the usage
and user satisfaction is defined as the recipients'
interaction of information and information sys-
tem product. Also, individual and organizational
effects are measured by the information system
which affects the users and the affiliated organiza-
tion of the users (Lee & Lee, 2008).
laboratories have already been developed and have
been described elaborately. Most of these labo-
ratories are based on standard hardware systems,
such as those provided by National Instruments
(NI), and are commercially available. However
they are costly and often require specialized train-
ing. The solution is to develop low cost systems
that rely partially on expensive hardware at the
server and open software at the client (Magoha
& Andrew, 2004).
Numerous digital applications have been de-
veloped to demonstrate laboratory experiments,
real-world landscape processes or microscopic
objects. Engineering students learned faster and
liked their classes more than students in traditional
on-campus classes. Cognitive scientists believe
that to learn, the material must have meaning to
the learner. Cognitive science may be defined as a
multi-disciplinary approach to studying how men-
tal representations enable an organism to produce
adaptive behavior and cognition. The following
criteria have been considered for certain virtual
field laboratory (Ramasundaram et al, 2005):
3. E-LEArnIng for
EngInEErIng EducAtIonS
Despite the dramatic expansion in e-Learning and
distance education, e-Learning in engineering
education still faces a number of setbacks that
prevent an equivalent expansion rate. For effec-
tive and complete learning in engineering, science
and technology, engineering education requires a
mixture of theoretical and practical sessions. In
order to understand how theoretical knowledge
can apply to real world problems, practical ex-
ercises are essential. While it is relatively easy
to simulate experiments, performing practical
experiments online has continued to be a chal-
lenge. Coupled with this, engineering software
is often very expensive and may not be easily
affordable by the ordinary e-Learner. Although
low cost alternatives that utilize freeware have
been successfully developed and tested, practical
laboratories that support engineering education
are still difficult to implement online (Magoha
& Andrew, 2004).
Engineering education relies heavily on capi-
tal-intensive laboratory equipment. Collaboration
with developed countries would, therefore, be one
path to enhance learning in engineering concepts
for developing countries. A collaborative labora-
tory component can bridge the gap between regular
e-Learning and e-Learning in engineering. This
can be achieved through an Internet laboratory,
examples of which are several Internet-based
•
Global
access,
i.e.,
web-based
implementation.
•
Stimulation of a variety of learning
mechanisms.
•
Interactivity to engage engineering
students.
•
Compartmentalization and hierarchical or-
ganizational structure.
•
Abstraction of 2D and 3D geographic ob-
jects (e.g. soils, terrain) and dynamic eco-
system processes (e.g. water flow) using
geostatistics and scientific visualization
techniques.
Medical Physics and Engineering (MEP) is
another example among the first professions to
develop and apply e-Learning. An indicator for
this is the first international prize in the field (EU
Leonardo da Vinci Award) presented to European
Medical Imaging Technology (EMIT) Consortium
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