Metadata Standards: What, Who & Why
Katholieke Universiteit Leuven, Belgium
Abstract: In order to be able to (re-)use digital content, interested
users must be able to identify and locate relevant documents. This requires
descriptive data, nowadays generally referred to as metadata. Technical
standards for a scaleable deployment on a global scale are required if
we want to achieve a critical mass of resources. In this paper, we present
the current status of ongoing work in this area, with a particular emphasis
on the IEEE LTSC Learning Object Metadata standard [IEEE,
2001] and related developments in the context of the ISSS Learning
Technologies Workshop [ISSS, 2001].
Keywords: metadata, learning technology standardization
Category: H.3 - Information Storage and Retrieval
1 What are Metadata: Introduction and Background
Metadata is often defined as:
A somewhat more informative definition is [IEEE,
| 'information about an object, be it physical
Thus, metadata are basically descriptive data. As such, metadata are
at the heart of more general developments in the area of digital libraries
[Fox et al., 2001]. Basic metadata elements indicate
the title, author, year of publication and similar simple bibliographic
data. Richer metadata structures also cover technical features, copyright
properties, annotations and so on.
The purpose of metadata is 'to facilitate search, evaluation,
acquisition, and use' of resources [IEEE, 2001]. Moreover,
in the case of educational resources, the purpose is also 'to facilitate
the sharing and exchange of learning objects, by enabling the development
of catalogs and inventories while taking into account the diversity of
cultural and lingual contexts in which the learning objects and their metadata
will be exploited' [IEEE, 2001].
In the documents on metadata and the 'semantic web' from the web consortium,
metadata is often used for descriptive data that can be processed by machines
[Berners-Lee et al., 2001]. This is a more restricted
interpretation than the one we adopt here. By explicitly including descriptive
data that need to be interpreted by humans, we want to recognize the importance
and relevance of such metadata.
We will explicitly not focus on metadata for geo-spatial applications,
library applications (Z39.50, MARC variants) and continuous media specific
standards (such as MPEG-7), though most of what we will present here is
applicable to those more specific contexts as well. This paper will also
not deal with the Dublin Core specification, that defines 15 elements
for cross-domain search. The Dublin Core specification has recently been
submitted for approval as an American national standard, referred to as
Z39.85. There is a Memorandum of Understanding between the IEEE LTSC LOM
group (see below) and the Dublin Core initiative, with the intent to investigate
common mechanisms for interoperability between the two metadata schemes,
potentially based on an interoperable approach that builds on the RDF framework
of the World Wide Web Consortium. More details on how Dublin Core relates
to the IEEE LTSC LOM specification can be found in [Duval,
2 Why Standards: Interoperability
Generally speaking, technical standards are important because they make
it possible to develop interoperable tools and services [Paepcke
et al., 1998]. In this context, my favourite definition of interoperability
is [Rust & Biede, 2000]:
| 'enabling information that originates in one
context to be used in another in ways that are as highly automated as possible'.
There are a number of noteworthy aspects in this definition:
- The central notion is that of crossing boundaries of context:
this may involve straightforward technical boundaries (like when metadata
are served from a server by a particular vendor to a client from another
origin), but also more subtle boundaries, such as linguistic ones (like
when metadata are to be translated), social ones (like when metadata intended
for teachers need to be transformed into metadata for learners), or, more
generally, cultural ones (like when metadata refer to national or regional
educational contexts, such as 'bac+2' in France). It is clear that the
technical boundaries are the easier ones to cross.
- The definition above mentions 'as highly automated as possible'.
Obviously, it is to be preferred that the process of crossing context boundaries
is fully automated, as in the case when documents are translated from one
format (like LaTeX or Microsoft Word) into another one (like HTML or Adobe
Portable Document Format). However, the definition makes it clear that
this process is not always fully automatic, as for instance in the case
when examples in a document need to be replaced by examples from another
application domain. (For instance, a document on the concept of 'calibration',
originally developed for the automobile manufacturing industry may be reused
in the context of medical measuring equipment.)
Thus, the notion of interoperability is not a binary one, where systems
would be either interoperable or not. Rather, there is a higher or lower
degree of interoperability. This is well illustrated by the standards on
paper size: in Europe, the 'DIN A4' standard, from German origins, prevails,
whereas, in the United States, the 'U.S. letter' standard is more widespread.
These standards have become so widely accepted that they are almost 'invisible':
we assume that papers fit in binders, that binders fit in closets, that
paper trays of printers have the correct size, etc. Moreover, we all take
it for granted that we can buy the hardware involved from different companies
and make the different components work together without any transformation.
Nevertheless, many of us have the experience of printing that goes less
than perfect when we download and print documents that have been formatted
for the 'other' standard size.
The above illustrates the main advantage of interoperability: it prevents
end users from being locked into proprietary systems. The World-Wide Web
is a perfect example of how standards (in this case: URL, HTTP and HTML
[Berners-Lee, & Fischetti, 1999] can be the basis
of open, interoperable systems, that allow end users a choice of client
and server systems alike. The Web also illustrates that interoperability
is not always absolute: because of the diverging additions to the official
W3C standards that Netscape and Microsoft support, some features may only
be available on one platform, or the developers may be required to develop
those features in non-standard ways, separately for each supported platform.
2.2 Layers of interoperability
It is useful to distinguish different levels of interoperability [see
table 1]. At the most machine oriented level, there
is network protocol interoperability, where the relevant standards include
TCP/IP and HTTP. The HTTP standard for instance enables a Web browser and
server to exchange messages, even when these software components were developed
by different vendors, operating under different operating systems, on different
kinds of hardware, etc.
Secondly, there is the level where data gets bound to a particular representation
format or data binding. A typical web example is the representation of
a document in HTML. For metadata, the most popular bindings nowadays are
XML, or, more specifically, RDF [W3C, 2001].
|| TCP/IP, HTTP
|| Data binding
|| HTML, XML, RDF
|| Metadata scheme
|| LOM, Dublin Core
|| Ontologies, classifications, vocabularies, taxonomies
Table 1: Layers of interoperability
The level that we will focus on in the remainder of this paper is that
of the conceptual data model or metadata scheme, that specifies the data
elements of which a metadata instance is composed. Metadata instances based
on a common metadata schema have a high degree of 'semantic interoperability'
[Forte et al., 1999].
The binding of metadata schemes in level 2 representations is typically
defined in a binding specific way. As an example, an XML DTD has been developed
for the IEEE LTSC LOM specification, in order to define an XML binding
of LOM. Similarly, alternative mechanisms can be used to bind to the same
representation format (for instance: XML Schema) or alternative representations
(for instance: RDF or SQL schemas). In [Section 3], we deal with standards
for layer 3 interoperability.
Finally, ontologies, classifications, vocabularies and taxonomies attempt
to define common semantics. In most cases, these conceptual structures
are restricted to a particular domain. Typically, they define the relevant
concepts in that domain, and their interrelationships. The intent is to
enable consistent interpretation of statements that make use of these concepts.
An example of this layer of interoperability is the reference 'Category:
H.3 - Information Storage and Retrieval' in the header of this paper: it
refers to the ACM Computing Reviews classification widely adopted in the
domain of computer science. Common classification structures are the basis
of consistent descriptions that support systematic access for indexers
and end users. More sophisticated such approaches are based on knowledge
engineering technologies [Hill et al., 2000].
3 Who and What: an Overview of Metadata Standards
3.1 Who: an Overview
Three 'official' accredited standardization organisations are
active in the field of educational technologies in general, which includes
the more specific field of learning object metadata. These organizations
- The IEEE Learning Technologies Standardization Committee (LTSC)
was set up in 1996. Its purpose is to standardize the 'smallest, useful,
doable specification that has technically feasibility, commercial viability,
and widespread adoption'. Besides working groups on for instance 'Computer
Managed Instruction', 'Simple Identifiers' and others, there is a group
that focuses on 'Learning Object Metadata' (LOM) [IEEE,
- The Centre Européen de Normalisation organizes a workshop
on Learning Technologies since 1999, under the umbrella of the so-called
'Information Society Standardization System' (ISSS). The main purpose of
this workshop is to 'promote the development and adoption of appropriate
standards, taking into account the diversity of cultural backgrounds and
languages that exists within Europe'. After an initial requirements analysis
[ISSS, 2000], work has now started on LOM related
work (see below), copyright, quality issues, educational modelling languages,
etc. [ISSS, 2001].
- ISO and IEC have set up a Joint Technical Committee (JTC1) that, since
1999, has a subcommittee on Learning Technologies. At this moment,
this more formal body is initialising its operations. In the domain of
metadata, it has invited the IEEE LTSC to submit its LOM standard as soon
as LTSC deems appropriate.
Besides the formal standardization bodies, there are numerous consortia
that carry out technical work in the field of educational technologies.
Once this work leads to mature specifications, those specifications can
be submitted to the accredited standardization organizations. Conversely,
consortia often represent communities of practice that adopt standards
as they are developed by accredited organizations.
The consortia with a more direct standardization impact on metadata
- The ARIADNE Foundation regroups academic and industrial members
[ARIADNE, 2001]. At the core of its infrastructure
is the so-called Knowledge Pool System, a distributed repository of pedagogical
documents and their associated metadata [Duval et
al., 2001]. An integrated Web-Based Learning Environment supports the
development of courses that reuse resources from the Knowledge Pool System.
- The IMS consortium regroups vendors of Learning Management Systems,
authoring tools, and related products [IMS, 2001].
IMS does not develop implementations, but focuses on the development of
specifications that can then be submitted to the standardization bodies
mentioned above. Work is currently ongoing in the area of content packaging,
question and test interoperability, etc.
- ADL was originally a U.S. Army initiative for interoperability
developments in the area of learning technologies, but it has substantially
increased its scope and relevance. One of its major milestones is the Sharable
Content Object Reference Model [ADL, 2001]. The major
aim of the SCORM model is to define an overall specification for interoperability
between components of a digital learning infrastructure, based on the IEEE
LTSC LOM and CMI standards.
The IEEE LTSC LOM standard is based on early work by the ARIADNE and
IMS consortia, which led to a joint submission of a base document. Since
then, ARIADNE, IMS and ADL have all contributed to further development
of the LOM specification within the IEEE working group. At the time of
writing, the LOM standard is in ballot.
ARIADNE, IMS and ADL are now developing their own so-called 'application
profiles' of the LOM standard: these are specifications that adapt the
standard to the specific needs of their communities. In practice, this
can for instance involve a mandatory status for some data elements, or
more restricted vocabularies than those contained in the IEEE specification
3.2 What: Learning Object Metadata
3.2.1 Learning Objects
In the context of the IEEE LTSC LOM, the term "Learning Object"
should be understood in its most general sense. The definition in the standard
| "a learning object is defined as any
entity, digital or non digital, that may be used for learning, education
Thus, learning objects can be of any size, type, etc. In principle,
they need not be digital, and can include people, rooms, equipment, etc.
This concept of a generalized learning object is in contrast to that of
an object as a discrete item or piece of content, often within a hierarchical
content model that progresses from the level of raw media, up through content
objects, learning objects and then lessons, courses, curricula, etc.
Moreover, a learning object need not be restricted to a static object
or piece of content: it can also be a momentary collection or assembly
of content, for instance adapted to the specific needs of a particular
learner in a given situation and time.
3.2.2 Base Scheme
The IEEE LTSC LOM standard defines a so-called base scheme. This is
basically a collection of data elements that can be used to describe a
learning object. The LOM scheme regroups data elements in nine categories:
- The General category groups information that describes the learning
object as a whole. This category includes elements like identifier, title,
language, keywords, etc.
- The Lifecycle category groups the features related to the history
and current state of the learning object. It also describes the individuals
or organizations that have affected the learning object during its evolution.
Data elements in this category include the version, status, and contributors
(authors, publishers, etc.).
- The Meta-metadata category groups information about the metadata,
rather than about the learning object that they describe. This includes
an identifier for the metadata instance, contributors to the metadata,
the language used in the metadata, etc.
- The Technical category groups the technical requirements and
characteristics of the learning object. This category describes for instance
the MIME type of the learning object, its size, location, required soft-
and hardware, etc.
- The Educational category groups the educational and pedagogic
characteristics of the learning object. It indicates the interactivity
type (active, expositive, etc.), learning resource type (exercise, simulation,
questionnaire, etc.), interactivity level, semantic density, educational
context (primary education, higher education, vocational training, etc.),
typical age range, etc.
- The Rights category groups the intellectual property rights
and conditions of use for the learning object. For this category, LOM adopted
a fairly simple approach, indicating whether or not any cost is involved,
and whether copyright and other restrictions apply. The idea is to refer
to other standards for more complex modelling of rights management metadata
- The Relation category regroups features that define the relationship
between this learning object and other ones, with an indication of the
type of the relationship ('based on', 'part of', etc.).
- The Annotation category provides comments on the use of the
learning object and information on when and by whom the comments were created.
- The Classification category describes where the learning object
can be classified within a particular classification system. As any classification
can be referenced, this category provides for a simple extension mechanism.
3.2.3 Data Elements
For each data element, the base scheme defines:
- name: the name by which the data element is referenced;
- explanation: the definition of the data element;
- size: the number of values allowed;
- order: whether the order of the values is significant (only
applicable for data elements with multiple values);
- value space: the set of allowed values for the data element
- typically in the form of a vocabulary (see below) or a reference to another
standard (such as vCard, ISO8601 for the representation of dates, etc.);
- data type: a set of distinct values; *
- example: an illustrative example.
Some data elements contain sub-elements. Data elements with sub-elements
do not have values directly, but indirectly, through their sub-elements.
As an example, the element that indicates the learning object that the
described object is related to (Relation.Resource) has a value indirectly
only, through one or more of its subelements (Relation.Resource.Identifier,
Relation.Resource.Description or Relation.Resource.CatalogEntry).
Vocabularies are recommended lists of appropriate values, that
define the value space of a data element. Other values, not present in
the list, may be used as well. However, metadata that rely on the recommended
values will have the highest degree of semantic interoperability, i.e.
the likelihood that such metadata will be understood by other end users
is highest. As an illustration, the data element Educational.LearningResourceType
may have a value from the LOM vocabulary, such as for instance "Questionnaire".
This option is preferred if the values in the vocabulary can adequately
express the intended meaning. If the indexer wants to assign a value that
is not part of the list given in the LOM document for that data element,
then the indexer may designate the value as, for instance, ("http://www.vocabularies.org/LearningResourceType",
"MotivatingExample"). This option provides more flexibility to
the indexer of learning objects, at the expense of semantic interoperability.
User defined values will not be used consistently throughout the larger
community. In the example above, a URI was used to indicate the source
of the vocabulary. This approach is certainly good practice, but using
a URI is not a requirement.
For each of the data elements, the specification includes the data
type from which it derives its values, such as Date, Character string,
etc. Of particular interest is the notion of 'LangString', used to represent
a phrase in a human language. A value of this type can consist of multiple
(Language, String) tuples where Language indicates the human language (according
to the ISO639 standard) and String holds the actual character string (according
to ISI/IEC10646-1). An example of this concept, as represented in an XML
binding could be:
<string xml:lang="en">Draft Standard for Learning
<string xml:lang="nl">Voorstel van Standaard voor
Metadata van Leerobjecten>/string>
In this case, two titles are defined for the learning object: one in
English, and one in Dutch.
A LOM metadata instance may contain extension data elements.
Such elements cannot replace data elements in the LOM structure.
3.3 European efforts on LOM
The IEEE LTSC Learning Object Metadata schema is explicitly recognized
by the ISSS Learning Technologies Workshop as the commonly accepted global
standards solution for describing learning objects through metadata. The
Learning Technologies Workshop is complementing this global activity with
a number of projects that address Europe's specific requirements [ISSS
- A first project will ensure that the IEEE LTSC LOM, as the globally
accepted solution, is capable of addressing specific European cultural
requirements (such as multilinguality). The outcome of this project may
be a proposed addendum for LOM.
- A second project is investigating standardization actions to permit
the identification of alternative versions of resources, in different languages,
as well as the origin of the translation, all within a LOM context. The
outcome may be an application profile of LOM to deal with this specific
- A third project will ensure that LOM is localized and translated in
the languages of the EU and EFTA countries. Translations of earlier versions
of LOM already are available. These will be replaced in due time by updated
and widely accepted revised versions.
- On the semantic level of interoperability, the workshop will collect
and organize a registry of taxonomies and repositories relevant to a European
learning society, via an on-line repository. The registry will indicate
the applicability of taxonomies and vocabularies, their interrelationships,
as well as mappings and translations between different structures. This
will benefit interoperability between European learning technology systems
and services as metadata implementations will be able to rely on standardized
taxonomies and vocabularies. It is expected that many will be developed
and implemented at national level. Actions will focus on the identification
of existing taxonomies, their applicability and interrelationships. Where
possible, mappings or translations will be made between various taxonomies
and vocabularies used in multilingual and multicultural learning domains.
All in all, it looks likely that LOM will be widely adopted in Europe,
as this standard is well suited to deal with the multilingual European
context. This is not surprising, as much of the original research and development
took place under the European ARIADNE umbrella.
4 Open issues and problems
We believe that the LOM standard provides a sound basis for educational
metadata. Even if we take this for granted, a series of important issues
and problems require further attention.
Awareness about the relevance of metadata for knowledge management in
general, and for educational purposes in particular, has increased sharply
these last years. In itself, this is obviously a positive evolution. However,
it also raises issues about information dissemination towards the
community of end users and developers, and about expectation management.
Even though most of the relevant organisations have a very open approach,
where basically anyone can participate and contribute, the technical nature
of the work and the somewhat obscure formalisms involved ('acronym soup')
may make the field somewhat intimidating to newcomers or those who just
want to assess the impact on their own work. Moreover, an analysis is required
of how different communities adopt and support educational metadata for
their constituencies [Duval, 2001].
Authoring of data and metadata is (too) hard and time consuming:
automatic generation of obvious metadata is useful and possible, but especially
semantic metadata will in most cases need to be provided through human
intervention. Metadata templates can help to make this process of indexation
easier, especially when similar documents need to be described regularly.
Moreover, the development of interesting educational resources, that really
add value when compared with their paper counterparts (books, slides,
etc.) is extremely time consuming and quite complex, the more so as it
often requires a multidisciplinary team of context experts, graphical designers,
technical experts, pedagogical experts, etc.
We basically argue that standardized metadata help end users to identify
and locate relevant educational material. However, that doesn't mean that,
once such material has been identified, no further barriers to (re-)use
remain: the user interface or look-and-feel of the resource may need to
be adapted to the overall context it will fit in, there may be technical,
organisational or legal reasons that prevent the material from being made
available to new users, the pedagogical style of the resource may not be
appropriate for the intended new context, etc. This issue raises the question
of adaptation of resources, either through human intervention (which requires
interoperable authoring environments) or automatically (which is beyond
the current abilities of so-called adaptive systems).
In the more general sense, there is the open question on what exactly
needs to be standardized, and in what order. This is to be considered
in the context of political, legal and other sensitivities. The question
of appropriate priorities is even more important when one realizes that
the standardization process takes a long time: between the first stable
ARIADNE metadata specification and the LOM standard ballot, five years
were spent on consensus building, evaluations and testing!
Finally, there is the issue of interoperability in a wider sense: as
the goal is to realize an infrastructure for interoperable tools and services,
the question arises what the appropriate building blocks or components
for such an infrastructure are. Should there be document and metadata servers?
Should these be the same? Can their data modelling and management requirements
be met by traditional database technologies? Should we rather opt for a
peer-to-peer approach? How will management of educational resources tie
in with general knowledge management? What about security? Etc. Etc.
In this paper, we have argued that standardized metadata are a prerequisite
for large-scale deployment and (re-)use of educational resources. The standardization
process in this area is maturing rapidly, with the first stable specification
(IEEE LTSC LOM) now under ballot. This seems to suggest that the first
requirement for a worldwide pool with a critical mass of reusable pedagogical
documents can be met. This situation creates exciting opportunities for
further research and development in this area (design for reuse, semantic
interoperability, metadata and document authoring).
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