Reusable Learning Objects: a Survey of LOM-Based Repositories
Filip Neven
K.U.Leuven, Computer Science Dept.
Celestijnenlaan 200 A
B-3001 Leuven, Belgium
+32-16-327659
Filip.Neven@cs.kuleuven.ac.be
Erik Duval
K.U.Leuven, Computer Science Dept.
Celestijnenlaan 200 A
B-3001 Leuven, Belgium
+32-16-327066
Erik.Duval@cs.kuleuven.ac.be
ABSTRACT
In this paper, we survey the field of learning object repositories.
Learning objects are typically relatively small content
components that are meant to be reusable in different contexts.
Associated to these learning objects are metadata, so that they
can be managed, searched, etc. As the international
standardization in this area is making important progress, the
number of these repositories is growing rapidly, and the whole
field of learning objects is rapidly maturing as a research area in
its own right.
Categories and Subject Descriptors
K.3.1 [Computer uses in education]: Computer-assisted
instruction (CAI), Distance Learning.
General Terms
Reusable Learning Objects, Metadata, Digital Libraries
Keywords
Metadata, Learning Object Metadata (LOM), Learning Object
repositories
1. INTRODUCTION
In this paper, we will compare the features and architecture of
repositories that typically contain learning objects or references
to them, as well as metadata based on the Learning Object
Metadata standard (LOM). We will refer to such Learning
Object Repositories as LOR’s. After a short explanation on the
nature and use of LOM repositories, we will summarize and
explain the criteria we used for the comparative analysis. Next,
we will summarize our findings and provide a non-exhaustive
overview of pro’s and con’s of different features and
architectural properties of a LOR.
1.1 LOM Repositories (LOR’s)
In order to improve the availability of good quality multimedia
learning material, ‘share and reuse’ of content components,
called ‘Learning Objects’ (LO’s), is crucial. Moreover, an
adequate search and retrieval system is required to support
flexible access to the LO’s. LO Metadata capture characteristics
of the LO’s and their potential educational application, using a
newly approved IEEE standard [1].
A LOM repository or LOR stores both LO’s and their metadata,
either by storing them physically together or by presenting a
combined repository to the outside world, while the metadata
and LO’s are actually stored separately. The metadata scheme a
LOR uses is based on the IEEE LOM standard, through a
process that is typically referred to as an ‘application profile’
[2].
A LOR allows registered or unregistered users to search and
retrieve LO’s from the repository. Searching for LO’s is based
on criteria that relate to LOM data elements. A LOR typically
supports simple and advanced queries, as well as browsing
through the material by subject or discipline. In a simple query,
keywords given by the user are matched against the text in a
number (or all) of the metadata elements. An advanced query
allows a user to specify values for specific metadata elements
(e.g. ‘easy’ or ‘medium’ for ‘Difficulty level’), and sometimes
also to rely on logical combinations of search criteria. Browsing
typically allows the end user to descend in a tree of disciplines
and sub-disciplines to get an impression of the objects available
in different domains.
2. COMPARISON CRITERIA
In this section, we summarize the criteria we used in our
comparative analysis. The goal of these criteria is not to select
the ‘best’ LOR. Evaluating a LOR should be done with context,
purpose and target user group in mind.
First, there are a number of criteria that apply to the LO’s
involved, like the subject(s) they cover, the metadata scheme
used, and the number of LO’s currently available.
Another non-architectural distinction between LOR’s is whether
they provide some kind of personal service to the user. A simple
example of this kind of service is a personal workspace with
bookmarks to LO’s that the user finds interesting. A more
sophisticated example is a system that keeps track of user
interests based on which LO’s he actually downloads. Such a
system can then notify the user when new such LO’s are
inserted. Another example is a system with which the user can
create personal templates for the metadata of the LO’s he
introduces.
More important in the context of this paper are criteria that
relate to the architectural properties of a LOR. One fundamental
difference between LOR’s is whether they are client-server
based or follow a peer-to-peer approach. In this overview, we
will only discuss the client-server based systems. (More details
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Multimedia 02, December 1-6, 2002, Juan-les-Pins, France.
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on a peer-to-peer based system we developed can be found in
[3].)
The most fundamental distinction between client-server based
systems is whether the LOR consists of a single server holding
the metadata and the LO’s (or the references to these
documents), or that the LOR is distributed. When the LOR is
distributed, are the metadata and/or documents being replicated
or do different servers cooperate in handling a search request, as
in for instance a ‘federated search’. Another issue is the type of
storage used for the metadata and the LO’s. Are LO’s stored in
the server or are they distributed over the Internet? And in what
format are the metadata stored? Very valuable is the possibility
of interconnecting different systems: another important factor
then is whether a LOR allows to propagate a search request to
other LOR’s, or whether it gathers LO’s and metadata from
other LOR’s.
3. COMPARATIVE ANALYSIS OF LOR’S
ARIADNE is a LOR that has evolved from over 6 years of
collaborative work [4,7]. It consists of a hierarchical network of
replicating nodes, where metadata of all objects are replicated,
as well as the free LO’s. LOM instances can be validated. Work
is ongoing in the area of automatic metadata generation [5].
SMETE supports searching and browsing resources in the
domains of Science, Mathematics, Engineering and Technology
[8]. A personal profile gives access to a workspace with
bookmarks. LO’s are recommended, based on past user
interaction with the LOR. Members with similar interests can be
identified. Federated searches that support querying remote
SMETE LOR’s, are under development.
The Learning Matrix [9] and iLumina [10] are other examples of
LOR in the same subject domain. iLumina groups related LO’s
in so-called “virtual collections” [6]. HEAL [11] is a LOR
focused on health science education and will soon be integrated
in SMETE. They are doing research on automatic content
harvesting.
MERLOT [12] covers all disciplines. Besides the MERLOT-
Central LOR, which provides access to the entire collection, a
number of discipline specific websites offer modifications of the
central functionality and interfaces to meet the requirements of
specific disciplines. Reviewing by peers is central to
MERLOT’s strategy. Discipline teams rate documents on 3
criteria: content quality, potential effectiveness and ease of use.
The LearnAlberta Online Curriculum Repository [13] is in its
early stages of development: at the moment, few LO’s are
available, but new material is being created. The goal is to create
a collection of LOR’s in the field of Kindergarten to grade 12
education, with access through a set of linked portals.
CAREO [14] is a LOR that holds links to LO’s, as well as some
LO’s themselves. The entry page displays the newest and most
popular LO’s. A personal profile gives access to a workspace
(‘My objects’) with bookmarks. Users can access a history of
objects they have downloaded. A component is under
development to collect LO’s and their metadata from other
LOR’s.
The EdNA LOR [15] can harvest LO’s from remote LOR’s and
make its own LO’s available to other sites, relying on the Open
Archive Initiative (OAI) protocols [16]. LO’s and metadata are
collected from Australian Museums Online (AMOL) and there is
a two-way exchange of metadata between the EdNA LOR and
the Education Channel in Victoria.
Lydia Inc. [1] is a commercial network of LOR’s. It has a central
site (Lydia Global Repository or LGR) where users can search
for LO’s after registering. The metadata for each LO describe
ownership and price, and users can use Lydia’s transaction
basket to buy objects. Organizations can set up their own Virtual
Private Repository (VPR). A VPR allows organizations to
introduce private content and provides access to all free content
available on the central server. A VPR is actually located on the
central site. Another option is to install an Enterprise
Repository, which is a local server that offers full replication of
the features of the Lydia Global Repository.
4. FEATURE COMPARISON OF REPOSITORIES
In the table below, we summarize the different LOR’s we studied, with a comparative analysis of their features and characteristics.
ARIADNE SMETE Learning Matrix iLumina MERLOT HEAL CAREO Learn-Alberta EdnA Lydia
Organ-
ization
Foundation Federation
(Berkeley)
ENC. Project Cooperation US Nat.
Science
Foundation
Universitie
s
Alberta
Learning
non-profit
private
org.
Meta-data
scheme
IEEE LOM
profile
IEEE LOM
profile
IEEE LOM
profile
IEEE
LOM
profile
IEEE LOM
profile
IEEE LOM
profile
(CanCore)
IEEE
LOM
profile
(CanCore)
IEEE LOM
profile
(CanCore)
Dublin
Core
profile
IEEE
LOM
profile
(SCORM)
Subject
domain
All Science,
mathematic
s,
engineering
and
technology
Science,
mathematics,
engineering and
technology
Science,
mathemati
cs,
engineerin
g and
technology
.
All Health
science
All Kinder-garten
to grade 12
(K-12)
education
Education All
# LO’s
2498 1645 170 880 7408 N/A 1576 ? 15782 48
IPR mgmt
Free and
restricted
Free Free Free Free Free Free Free Free Free and
restricted
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