Information Technology Reference
In-Depth Information
Knowledge concepts and learning units form
the backbone for more general domain knowledge
items such as subsections, sections, chapters and
so on.
Learning units constitute the learning content
presented to learners. Learning units may be
static educational pages or fragments from which
educational pages are dynamically generated.
Each learning unit is associated with one or more
knowledge concepts either prerequisite or out-
come. Knowledge of learning unit's prerequisite
concepts is required for its comprehension. By
studying learning units, learners gain knowledge
of outcome concepts. The distinct representation
of domain's pedagogical structure (knowledge
concepts) and actual learning content (learning
units) greatly facilitates domain knowledge up-
dates. Research has focused on
learning objects
that is, reusable/sharable learning fragments that
can be dynamically selected/assembled to create
learning content presented to users (Northrup,
2007).
To facilitate management/selection/ordering
of learning units, domain knowledge frequently
includes a
meta-description
(metadata) of learn-
ing units based on their general attributes. There
exist
standards
for learning unit meta-description
such as
ARIADNE
,
IEEE LTSC Learning Object
Metadata (LOM), Dublin Core
and
SCORM
. Such
standards enable small, reusable and sharable
learning content items, existence of interoper-
able learning content repositories and assembly
of learning content on-the-fly. Such goals are
significant in Web-based environments in which
metadata is exploited by users/systems searching
for relevant educational content.
Several issues/problems concerning domain
knowledge should be dealt with. Such issues are
the following:
edge (Hatzilygeroudis & Prentzas, 2006).
Structural knowledge is concerned with
types of entities (i.e., concepts, chapters,
etc.) and their interrelations. Often, those
relationships are hierarchical concern-
ing generalization/specialization relation.
Relational knowledge concerns relations
among domain entities. Those relations
may be causal or dependency relations.
•
Domain knowledge structure creation.
Tools/techniques assisting in creating do-
main knowledge structure would be help-
ful (Baia & Chen, 2008; Hatzilygeroudis
& Prentzas, 2008).
•
Learning unit creation.
Creation of learning
content (e.g. exercises, examples) is usual-
ly done manually requiring time and effort.
Tools facilitating learning unit creation
are useful (Murray, 1999; Brusilovsky,
2003). Also methods for (semi-)automatic
creation decrease IES development time
especially if such methods are domain-
independent (Fischer & Steinmetz, 2000).
Due to the fact that large sets of exercises
are necessary for learner assessment, for-
malization of the concept of parametric
exercises (i.e. exercises whose generation
is based on a set of parameters) is also re-
quired (Gutiérrez, Losa, & Kloos, 2008).
•
Provision of learning unit meta-description
(metadata).
Learning unit meta-description
is often provided manually. This process
is time-consuming, expensive and error-
prone. Methods to (semi-) automatically
provide such data are required (Jovanovic,
Gasevic & Devedzic, 2006).
•
Maintenance of domain knowledge items.
During IES operation, domain knowledge
may undergo changes/refinements to pro-
vide improved learning experiences. Such
changes may involve domain knowledge
structure and/or learning units. Due to do-
main knowledge size, tools/methods facili-
tating domain knowledge management are
•
Choosing a representation scheme for
domain knowledge.
The representation
scheme should be able to naturally rep-
resent structural and relational knowl-
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