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Likewise, it is likely to play a major role in m-
learning as it can be employed for the creation of
mobile web apps through the application of
Javascript libraries like jQuery Mobile. As the
format becomes accepted as the standard for user-
oriented web publishing, it will be underpinned by
an increasing number of development tools ranging
from simple text editors, many accessible online,
open source plug-ins facilitating design activities
such as canvas drawing to full-fledged development
environments aimed at widget construction or the
like.
To make possible simple forms of semantic
encoding within HTML5 documents, a syntax called
microdata has been developed and published
alongside HTML5. Called HTML5’s “best-kept
secret” by one blogger (Gilbertson, 2010), microdata
have been relatively unknown beyond web
developer circles until recently when Google,
Microsoft and Yahoo jointly announced schema.org,
a set of standardized categories and properties for
identifying and describing general purpose
semantics in web pages using microdata (events,
persons, organization, places, etc.). The aim of
schema.org is primarily to enhance the performance
and presentation capabilities of the three major
search engines, thereby hopefully improving user
experiences and satisfaction (and ultimately, no
doubt, increasing revenues).
Both the microdata standard and schema.org
have been met with a certain amount of criticism.
For example, it can be argued that microdata lack
the expressiveness and flexibility of RDFa, an
already existing standard for encoding meaning in
web documents and does not therefore constitute any
real added value in the context of semantic mark-up.
And as for schema.org, the entire project may in a
way be seen as a step away from open standards
with their insistence on implementation in open
forums and permanent availability. For instance, the
three companies in question can alter, delete parts
of, or altogether remove the documentation at
schema.org at any time if they choose to do so.
Nonetheless, the combination of microdata and
schema.org vocabularies have the potential, in my
view, to become the tool of the trade for many
learning content creators willing to add semantic
metadata to their web-based materials but reluctant
to delve into the finer details of specialized learning
metadata models often couched in slightly arcane
XML dialects. Here are some reasons why I think
this is so:
3 WHY USE MICRODATA?
Firstly, microdata (based on schema.org
vocabularies) are simple and relatively easily
learned. Microdata encode so-called items, entities
or objects, categorize them in one or more classes
and assign property values to them. The content of
an HTML5 element (section, paragraph, heading,
etc.) may thus be marked up to indicate that it deals
with, say, an item of the type “person” which has the
name property of “Shakespeare”:
<p itemscope
itemtype="http://schema.org/Person">
<span
itemprop="name">Shakespeare</span> was
born in …
</p>
Because microdata are embedded directly, but
unobtrusively, in HTML5 elements, they are also
accessible to local programming scripts and may in
this way be used for content adaptation or user
interaction purposes. A simple example would be
the visual foregrounding or extraction of all items of
a certain semantic type or the assignment of
behaviours to items with certain semantic properties.
But this is not all. Since semantic encoding is done
in a standardized way, useful scripts may be
developed, shared and employed on a global scale,
and, equally significantly, across disparate subject
matters, disciplines and subjects. For instance,
learning content developers embedding microdata
about places and locations in their materials might
be be able to download, or point to, scripts, such as
jQuery files, mapping these microdata to Google
Maps while authors textually describing subject
matter concepts and concept relations might be able
to utilize available plug-ins to visualize these as
concept maps.
Arguably, this is a somewhat novel way of
thinking about learning content metadata: Here
metadata is not conceived of as ancillary
information, detached from the content itself, but as
an integral part of its actual learning design,
possibly initially hidden from the user but ready to
be “activated” for specific communicative or
didactic purposes, such as catering for different
learning styles among users. In semiotic terms,
microdata may thus be characterized as embedded
resources or vehicles for making meaningful
changes in learning material. They may aid in
transforming learning content, i.e. making changes
in the same representational mode, say text, or
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