Web-based Learning Solutions for Communities of Practice

And here is where new technologies come
in the picture. The tools they offer are necessary
for their communication, for the delivery
of courses, for finding educational material. The
ODL course we propose here can combine hard
and soft knowledge in one piece and it is an alternative
form of educational material based on
new technologies that is ideal for use for long
distance learners. The course itself offers concrete
information, whilst the future plan is that in the
background artificial intelligence techniques will
capture the navigational patterns of users through
the material, incorporating additional information
such as the time they spent in each unit, repeated
visits, non-visited material, in general a student’s
behavior of study in a course that offers more than
just the directly related material. In a sense it is
an effort to capture the practice of the community
by identifying the learning patterns and not only
the navigational evidence, just a plain sequence
of visits. Although the users do not necessarily
and consciously interact to exchange experiences
and benefit by the direct observation of others, the
instructor might discover (Karacapilidis & Tzagarakis,
2007) interesting nuggets of behavioral
similarities by using the results returned by the AI
techniques employed in the course. Such results
can refer to how the students proceed through it and
even yet how their online communication or other
parameters may affect their patterns through time.
Furthermore the patterns can reveal weaknesses
and strengths of the original structure as well as
the context that can assist to the improvement of
the original design. The learners, without even
realizing it, are offering valuable information
about the way they are exploiting the material in
the course and the instructor can use it in order for
them to view an improved version of the course,
based on their own unconscious usage of it.
From that viewpoint, it is not difficult to expect
that every tutor group that collaborates on a subject
will be able to elect one person from that group to
attempt to post-process the tutors’ collaboration
results in order to extract useful abstracts that will
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Using Graphs in Developing Educational Material
allow the whole group to efficiently summarize
their collaboration fruits. An infrastructure that
allows the gathering and processing of navigational
information is a rich source of input for
any educational intelligence project; however, it
is always up to a human entity to be eventually
responsible for structuring the knowledge that
resides in collaboration workspaces.
A LEARNER’S ODL COURSE
AS A MODEL FOR THE
EDUCATIONAL PROCESS
A graph-theoretic model of a Learner’s ODL
course is a computational model. It builds on top
of some basic components which are elaborated
below and it involves, at several points, activities
of the stakeholders as described above.
A learning object is any piece of (multimedia)
data or program whose purpose (intention) is to
be used for learning. A learning object can be
recursively defined as a set of learning objects.
Examples of learning objects are the following:
the text of Odyssey, MS Word, Sketchpad, a video
lecture, a set of multiple choice questions, a Euclidean
geometry high school textbook, an MS
PowerPoint presentation of organic compounds.
A learning task is a task whose purpose is
learning. Examples of learning tasks are the following:
read, solve an exercise, write a program,
practice a musical instrument, draw a picture,
design a database, make a summary, think over,
correct, argue for/against.
A learning activity is an ordered pair: (learning
object, learning task). Examples of learning
activities are the following:
•
•
Write a program to add two numbers (learning
task) using a C++ compiler (learning
object)
Write down (type to the computer) what
you hear (the learning object is a digitized
dictation) and then check the spelling errors