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Abstract—The european higher education area (EHEA) aims
to create a more comparable, compatible and coherent space of
Higher Education in Europe. After a decade of reforms, most of
the required instruments are already in place, so the next years
are key to complete the implementation and consolidation of the
EHEA. However, data challenges pose a threat to pivotal EHEA
processes such as credit recognition that require institutions to
cooperate and integrate their datasets scattered across diverse
repositories in multiple data formats. Linked Data principles
and practices have the potential to address such difficulties, as
demonstrated with the emergence of a global data space –the
Web of Data. This paper first discusses the benefits of Linked
Data for the emergent EHEA, devising a new set of Linked
Data-driven scenarios that leverage current EHEA processes.
Further, a set of preliminary Linked Data deployments is
examined to assess the advantages and identify the challenges
ahead in the EHEA landscape.
Index Terms—European higher education area (EHEA),
linked data, semantic web, technology-enhanced learning
(TEL).
I. INTRODUCTION
The so-called Bologna Process aims to create the European
Higher Education Area (EHEA), involving 47 countries
across Europe. As stated in the Bologna Declaration [1] the
goals of the emergent EHEA are to facilitate the
compatibility and comparability of study programmes, to
encourage the mobility of students and staff, increase
employability and promote the attractiveness of the EHEA
world-wide. After more than ten years from the beginning of
the Bologna Process, national legislation and regulation
reforms of Higher Education have been completed in most
countries [2]. As a result, most of the fundamental elements
of the EHEA are now in place including the adoption of a
three-cycle degree structure, a credit-based system, standards
and guidelines for quality assurance, and the definition of
qualification frameworks based on learning outcomes, as
summarized in Fig. 1.
However, the achievement of certain strategic goals as
Manuscript received June 25, 2014; revised September 12, 2014. This
research has been partially funded by the European Commission BYTE
project FP7 GA 619551, the Spanish Ministry of Economy and
Competitiveness project TIN2011-28308-C03-02, and the Autonomous
Government of Castilla and León, Spain, project VA301B11-2.
Guillermo Vega-Gorgojo is with the Department of Informatics,
University of Oslo, Norway (e-mail: guiveg@ifi.uio.no).
Thanassis Tiropanis is with the School of Electronics and Computer
Science, University of Southampton, UK (e-mail: tt2@ecs.soton.ac.uk).
David E. Millard is with the School of Electronics and Computer Science,
University of Southampton, UK (e-mail: dem@ecs.soton.ac.uk).
envisioned in the EHEA has appeared to be especially
challenging [3]. Specifically, mobility within the EHEA has
not increased substantially, study programmes should be
more compatible and comparable, and the employability goal
remains elusive [4]. One of the reasons is that many EHEA
processes such as credit recognition or quality assurance are
very demanding due to excess of bureaucracy and human
intervention [5], [6]. Furthermore, these processes typically
require the cooperation of multiple EHEA institutions, thus
posing challenges on the integration and the interoperability
of data dispersed in institutional repositories [7].
Similar difficulties for integrating datasets from different
sources have been reported many times in other domains
such as enterprise systems integration [8]. Particularly
noteworthy is the Linking Open Data project that has led to
the creation of a global data space based on an increasing
number of contributors [9], [10]. The goal of this project was
to bootstrap the development of a Web of Data based on
common standards and rules such as the Linked Data
principles [11] for publishing data on the Web. The success
of this initiative has been such that even governments have
begun to release some of their datasets as Linked Data with
the aim of increased transparency and efficient administrative
procedures [12].
In the Higher Education field some preliminary works
propose the use of Linked Data technologies to address the
challenges for Technology-Enhanced Learning (TEL), as
well as supporting activities such as student admission [7],
[13], [14]. In the wider context of the EHEA the impact of
such technologies should be even more significant given the
requirements for cross-institutional exchange and integration
of data. Despite this, there are no studies in the literature
motivating the use of Linked Data technologies in the EHEA
to the extent of the authors' knowledge. This paper aims to
discuss the potential of Linked Data for supporting EHEA
processes. In this regard, the use of these technologies for
addressing the data requirements of the emergent EHEA is
analysed and we propose a set of Linked Data-driven
scenarios. Furthermore, a set of preliminary Linked Data
deployments is revised to assess the benefits of these
technologies, as well as to identify the challenges ahead to
better support Europe's Higher Education landscape.
II. THE OPPORTUNITY OF LINKED DATA FOR THE EHEA
This section begins with a discussion of the suitability of
Linked Data to support EHEA processes. Then, a set of
Linked Data-driven scenarios is outlined, thus illustrating the
potential of these technologies for the realization of the
EHEA.
The Opportunity of Linked Data for the European Higher
Education Area
Guillermo Vega-Gorgojo, Thanassis Tiropanis, and David E. Millard
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Journal of Information and Education Technology, Vol. 6, No. 1, January 2016
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DOI: 10.7763/IJIET.2016.V6.659
A. The Potential of Linked Data for the Implementation of
the EHEA
Linked Data technologies provide for the exposure of data
as well as links (relationships) among data on the Web [10],
[15]. Exposed data may originate from databases, Web pages
or files such as excel spreadsheets in different organisations.
Linked Data technologies make use of Uniform Resource
Identifiers (URIs) to identify data and relationships and they
provide for describing, storing and querying Linked Data
based on standards such as RDF [16], RDFa [17] and
SPARQL [18]. There are proposals and methodologies on
how relational database tables can be exposed as Linked Data
[19] and proposed rules for the emerging Web of Data [11].
These rules encourage the reuse of URIs where possible in
order to make it easy to obtain the same type of data from
different sources and to perform aggregation and simple
reasoning. In addition, there are requirements on supporting
the „dereferencing‟ of URIs, i.e. obtaining further
information about the corresponding data or relationship
[11].
Fig. 2. Potential linked data-driven scenarios fro the EHEA.
Linked Data technologies can support data interoperability
and integration and thus provide for building more advanced
applications over the Web. In addition, a Linked Data
infrastructure can pave the way for adding more expressive
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Fig. 1. Time line of the Bologna process and main achievements.
descriptions of exposed data and could, in turn, support
advanced inferencing and more intelligent applications. In
this way, the Linked Data represent a bottom-up approach to
implementing the Semantic Web vision [20]; in this approach
the value of data exposure, interoperability and integration is
significant enough to be given priority over building
consensus on common vocabularies and ontologies.
It has been reported that a Linked Data infrastructure
across Higher Education Institutions (HEIs) can help the
sector meet certain challenges [7]. In particular, the findings
of a survey that was conducted by the UK JISC-funded
SemTech project in 2008-2009 suggest that such
infrastructures could address challenges related to learning,
teaching and supporting HEI processes [13]. According to [7]
relevant learning and teaching challenges that could benefit
from Linked Data infrastructures include 1) course creation,
delivery and revision, 2) access to teaching and learning
material across institutions, and 3) cross-curricular activities
in emerging areas; challenges related to Higher Education
processes involve 1) curriculum development or alignment, 2)
degree programme accreditation, and 3) cross-institutional
collaboration.
These Higher Education challenges, which Linked Data
infrastructures could help to address, are also related to the
international cooperation envisioned by the Bologna Process
and to the programme and curriculum reform for
convergence of the EHEA. Specifically, data exposure in
interoperable formats as defined in the Semantic Web can
support advanced data processing and integration
applications. In addition, the emergence of vocabularies and,
where necessary, vocabulary mappings for the EHEA could
further increase the potential for data integration. The Linked
Data design recommendations [11] show that vocabulary
convergence could improve interoperability significantly,
although it is not a prerequisite for Linked Data applications.
Existing tools identified by the SemTech project [13] could
provide for efficient exposure and mapping of Linked Data.
A. Linked Data-Driven EHEA Scenarios
In order to exploit the potential of Linked Data for the
further development of the EHEA, stakeholders should
expose their key datasets into the Linked Data cloud. For
instance, HEIs could offer as Linked Data their course
catalogues [21] that include information on programmes,
qualifications, modules, learning outcomes, ECTS credits
and so on. Likewise, data reporting agencies could expose
their statistics, indicators and reports as Linked Data. Table I
summarizes the key datasets that could be offered by the
main stakeholders in the EHEA.
Note that some institutions are beginning to expose some
of their datasets as Linked Data. For instance, the University
of Southampton has recently launched the Open Data Service
(http://data.southampton.ac.uk/) and other European
universities are also embracing Linked Data –Linked
Universities (http://linkeduniversities.org) lists some of these
institutions. Moreover, Eurostat now offers a Linked Data
version (http://eurostat.linked-statistics.org/) of their datasets
and some governments and administrations have begun to
release data from the public sector following the Linked Data
principles. These pioneering initiatives should be followed
by other institutions, thus contributing to constitute an
incipient EHEA Web of Data.
TABLE I: MAIN STAKEHOLDERS IN THE EHEA AND KEY DATASETS
Stakeholder Key datasets
Governments
- Regulations, guidelines
- Project, grant, position calls
- Indicators, statistics, reports
Higher Education Institutions
- Institutional information
- Course catalogue
- Student records
- Staff records
Quality assurance agencies
- Agency information
- Criteria
- Indicators, statistics, reports
Employers
- Employer information
- Employment offerings
Data reporting agencies - Indicators, statistics, reports
Digital libraries and repositories
- Research resources
- Teaching materials
With the gradual exposure of institutional datasets, it is
envisioned that there will be an emergence of a new breed of
Linked Data-driven applications in the EHEA landscape. As
a result, some current human-intensive tasks could be
automated or at least better supported with technology.
Considering the main application areas of the EHEA, a new
set of scenarios can be devised, as summarized in Fig. 2:
Mobility. The availability of HEI course catalogues
enables the development of expert systems for searching
programmes and modules. This way, students‟ mobility
could be fostered allowing them to find appropriate
degrees for their interests. Moreover, the exposure of
other datasets, e.g. student records, can facilitate
additional features such as checking programme
prerequisites or suggesting mobility grants.
From the perspective of a HEI, students‟ mobility involves
many bureaucratic processes like programme admission,
recognition of ECTS credits or the transferability of students‟
records that could be supported with Linked Data-based tools.
For instance, programme mappings via qualification
frameworks and learning outcomes can help to assess the
similarity of modules across institutions, thus facilitating
recognition processes.
Employability. In this area there is ample room for
improvement, since qualifications are not always
comprehensible for employers and they are commonly
unaware of instruments such as the Diploma Supplement
[4]. A promising solution is the description of
qualifications in terms of learning outcomes, thus
enabling the development of job matching services based
on skills and competencies. With an EHEA Web of Data,
employers can even obtain evidence of the acquisition of
a particular competency. Further, generation of curricula
could be partially automated gathering data from student
or staff records and HEI course catalogues.
Academics. The potential of Linked Data for Higher
Education was supported in [13], identifying various
suitable TEL scenarios. One example is the search and
matching of teaching resources across institutions.
Student tutoring and scaffolding can also be leveraged by
recommending materials for an assignment or finding a
tutor to support students‟ activities. In addition,
supporting scenarios for programme and module design
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can be devised: identifying niches for new modules,
comparing programmes across institutions or integrating
information scattered across different datasets for
documentation purposes.
Research. In this case, many scenarios can be found
requiring some kind of resource searching and matching
such as researchers, groups, papers, projects, conferences,
grant calls and so on. Moreover, notification and
recommendation services are especially suitable for
awareness and to deal with deadlines. Further, highly
bureaucratized processes like project proposal
preparations could be eased with semi-automatic
generation of application forms and curricula.
Quality assurance. Programme and staff accreditation
activities are becoming ubiquitous in the emergent EHEA.
These processes tend to be data-intensive, requiring
applicants to prepare exhaustive applications and
evaluators to revise the documentation and find evidence
of claims. Thus, a Linked Data approach could help to
gather data dispersed in different sources, automatically
generate some forms such as curricula, perform a
prerequisite checking of an application and so on.
Data reporting. This area is critical to assess the state of
development of the EHEA [3] and Linked Data seems a
very promising approach to enhance both availability and
quality of the data. Adoption of established Linked Data
publishing practices by all stakeholders in the EHEA can
enormously facilitate the work of data reporting agencies.
For instance, data gathering should be eased and usage of
common formats may enhance data comparability, thus
leveraging the generation of statistics, indicators and
reports. Further, exposure of institutional datasets seems a
promising field for data mining processes.
III. EMERGING LINKED DATA DEVELOPMENTS IN THE EHEA
After discussing the opportunity of Linked Data for the
realization of the EHEA, this section describes a set of
ongoing projects that illustrate the benefits of Linked Data
technologies in some of the key areas of the EHEA. The first
three projects describe university-wide infrastructures that
utilise Linked Data, while the second three projects present
novel TEL applications that exploit Linked Data
technologies.
A. Open Linked Data from the Open University
Similar to other universities in Europe, the Open
University (OU) exposes some of their institutional datasets
as Linked Data [22]. Specifically, research publications,
teaching material, course descriptions and people profiles are
currently available. Whenever possible, popular and
well-established vocabularies are employed, e.g. the
Bibliographic Ontology (BIBO
http://bibliontology.com/specification) and Friend of a
Friend (FOAF http://xmlns.com/foaf/spec/) – as well as
educational domain vocabularies such as AIISO
(http://vocab.org/aiiso/) for the organization of academic
institutions. All these data are available both through a
SPARQL endpoint and as RDF.
The technical infrastructure consists on a set of
components that extract RDF from existing OU repositories,
load this RDF into a triple store and make it available through
the Web. Beyond the exposure of datasets, some applications
that use these data have been developed, such as graph
generators, a building locator, and a course recommendation
system. Overall, these preliminary applications require little
effort to exploit OU datasets and to link to external sources
such as DBpedia (http://dbpedia.org/), thus demonstrating
the benefits of the Linked Data approach.
B. The Southampton Learning Environment
The Southampton Learning Environment (SLE) project is
a TEL initiative across the University of Southampton as part
of its Curriculum Innovation Programme. It involves a large
group of stakeholders from different faculties, the
University‟s information and communication technologies
(ICT) professional services department and the students‟
union. SLE uses Linked Data technologies for the integration
of data in repositories across or outside the University with
the aim of improving the student and staff experience, of
making graduates more employable and of enabling remote,
blended and distance learning alleviating the teaching space
problem [23].
SLE is in the process of deploying an RDF layer over
repositories such as timetable information, virtual learning
environments, eAssessment, library and computer assisted
assessment. The integration of external and cloud-based
services and repositories such as GoogleDocs, DropBox,
TurnitIn and Delicious is also envisaged. The deployment of
SPARQL endpoints to support this RDF layer involves a
separation between repositories providing authoritative and
non-authoritative university data and a separation between
private and public university data sources. SLE also
considers community engagement models for the
development of applications over this layer; as a first
iteration an Open Linked Data portal
(http://data.southampton.ac.uk) was implemented to this end,
providing an one stop access point to data sources and an
application store to host applications that were crowdsourced
from across or outside the University. The portal features a
number of innovative applications include a university
amenities map and a number of smartphone-based
applications.
C. Talis Aspire
Talis Aspire [24] is a resource management system widely
adopted by HEIs in the UK. With this system, teachers can
prescribe learning materials to students through a
conventional Web interface; behind the scenes, RDF triples
are produced and persisted to a centralized triple store,
enabling the linking of resources across institutions. Whilst
BIBO is used to annotate scholar materials, academic and
course information is described with AIISO. With such a
volume of structured data, Talis Aspire shines at discovering
resources via browsing, searching and recommendation
mechanisms.
D. MEducator
The mEducator project (http://www.meducator.net/) aims
to create a federated repository of health educational
materials. Existing medical education repositories are not
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interoperable due to competing data schemas and
heterogenous API interfaces. Ref. [25] proposes a general
architecture for integrating existing Web educational
resources (data and services) and exposing them as Linked
Data: each repository is annotated semantically, then
resource annotations are retrieved and stored in a triple store,
and finally automatically enriched with dataset interlinking.
This approach is followed in mEducator, exposing as Linked
Data the resulting dataset describing resources according to
the mEducator Resource RDF schema
(http://purl.org/meducator/ns/). mEducator exemplifies an
evolutionary development that takes profit of Linked Data to
provide a federated repository without requiring the
substitution of its parts that remain operable.
E. Quiz Games with Greek DBpedia
Ref. [26] presents a web game that uses datasets derived
from the Greek DBpedia. It provides several quiz types and is
purposed for educational purposes by native speakers, taking
profit from the DBpedia internationalization effort. The
application architecture is relatively simple, generating
thousands of questions from the Greek DBpedia using 30
seed SPARQL queries to retrieve facts belonging to
geography, history, and so on. This game has been tested in a
primary education pilot study, obtaining a positive reception.
This project illustrates the serendipitous reusability enabled
by the Web of Data: the creation of the DBpedia is a
collaborative effort that can be easily exploited in novel and
unanticipated ways by taking profit of its underlying data
structure.
F. SEEK-AT-WD
SEEK-AT-WD is a Linked Data-based search system of
tools for Higher Education [27]. Given the number of tools
that can be employed to leverage current learning settings,
this system aims to provide guidance to teachers. It is a
follow-up of Ontoolsearch [28], a system that supports
semantic search of educational tools that can be visually
constructed using the terms defined in the vocabulary
Ontoolcole [29]. Since Ontoolsearch employs its own
isolated dataset, it is difficult and costly to keep tool
information up to date. In contrast, SEEK-AT-WD exploits
the Web of Data to gather tool descriptions available as
Linked Data in DBpedia, Freebase
(http://www.freebase.com/) and other open datasets.
Ontoolcole is still employed to provide a homogenous view
of educational tools, developing a set of mappings to
translatedescriptions in external data sources to
thisvocabulary. As a result, this proposal is much more
sustainable, taking profit of available information in the Web
of Data to keep updated the tool dataset in an automated way.
IV. DISCUSSION
The scenarios and the specific projects that are outlined in
the previous sections illustrate that Linked Data technologies
are not only appealing, but they can serve to leverage key
activities of the EHEA. However, many key datasets for the
development of the EHEA remain hidden in institutional
realms, thus forming part of the so-called Deep Web [30]. In
other cases, datasets are publicly available on the Web,
typically in HTML format; however, they lack the required
structure and semantics for more automated processing (e.g.
searching mechanisms) and are largely disconnected from
other data sources. In consequence, independent reports on
the state of the EHEA [4] as well as the strategic agenda [3]
emphasize the need of better data exposure both for
spreading the EHEA and for monitoring achievements.
Anyway, the situation is changing and many ongoing
efforts are devoted to exposing existing datasets as Linked
Data. In this regard, demonstrators such as the Open Linked
Data are of great importance to induce other EHEA
institutions to follow this path. While early experiences
report that setting up a Linked Data service is not trivial [31],
this situation is likely to improve in the near future since tools
are rapidly maturing and more clear guidelines and best
practices are crystallizing [32]. For instance, EHEA
institutions can choose among different types of data
publishers depending on the need of supporting legacy data
sources or not: one option is the use of a triple store that
natively supports RDF; so-called RDFizers can convert
legacy formats into RDF; mapping tools such as D2R Server
[33] can be employed to expose relational databases as
Linked Data; in addition, some commercial data storage
platforms like Virtuoso (http://virtuoso.openlinksw.com/)
offer a Linked Data interface. The mEducator project
illustrates the creation of a federated dataset from legacy
repositories though the use of Linked Data.
Data modelling is another key element for publishing
datasets, and vocabularies provide the necessary structure to
enable data processing by applications. Vocabularies should
be open and shared by a community of users in order to
facilitate data integration processes. In the outlined projects,
bibliographic resources are described with BIBO, people
profiles with FOAF and so on. Noteworthy, learning specific
vocabularies such as AIISO, mEducator schema and
Ontoolcole are also employed. Currently, there is a rise of
ongoing proposals for describing specific domains in the
EHEA such as competencies, outcomes, learning
opportunities and assessment –see for example the ICOPER
Reference Model
(http://www.icoper.org/results/reference-model) and the
ASPECT Vocabulary Bank for Education
(http://aspect.vocman.com/vbe/). Other remarkable
initiatives include the Bowlogna ontology [34] for academic
settings, the Learning Resource Metadata Initiative (LRMI
http://www.lrmi.net/) for learning objects and XCRI
(http://www.xcri.co.uk/) for describing courses, although it is
increasingly difficult to keep track of all vocabulary offerings.
Indeed, some of these proposals compete and may overlap
thus requiring schema mappings and data fusion techniques
to provide for data interoperability. In the mid-term
vocabulary convergence can be expected in many cases. In
this regard, the role of standardization bodies such as the
CEN Workshop on Learning Technologies (CEN provides
specifications, agreements, guidelines, and recommendations
of special relevance to the EHEA, see
http://www.cen.eu/CEN/sectors/sectors/isss/activity/Pages/
wslt.aspx) is of great importance to establish a common
ground.
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As a note of caution, Linked Data technologies are yet to
address challenges related to scalability. Federated queries
over a large number of Linked Data repositories across
institutions is yet to be addressed although there has been
significant work in the area of federated databases and early
work on supporting distributed queries in the Web of Data
[35]-[37]. Until these technologies are mature enough to
support federated queries on a large scale, triple stores are
expected to store data from different sources and therefore
reduce the requirement for distributed queries and repository
federation. In addition, the privacy of certain information in
Linked Data repositories (e.g. student or staff records) is
another cause of concern. Although access control can be
imposed on repositories, more elaborate control that will
ensure that private information cannot be queried or inferred
(privacy of RDF graphs) is currently supported only by
specific environments [38].
Once datasets are openly available, new applications can
be developed to consume these data to provide relevant
functionalities for the EHEA. In the simplest case,
applications can be designed to explore Linked Data sources,
allowing users to browse, search or visualize a dataset, e.g.
the charting application of the Open Linked Data. However,
the real value of Linked Data comes out when interlinking
datasets and exploiting the Web of Data. In this regard,
platforms like Talis Aspire effectively create a web of scholar
resources, allowing the discovery and recommendation of
materials by exploiting the links (resources by topic, course,
popularity, etc.). Moreover, the SLE project shows how
Linked Data technologies can profoundly impact TEL by
opening up institutional data, allowing more transparent and
flexible use of data within an institution, and creating
opportunities for creative third-party applications.
Additionally, the Greek DBpedia quiz game and the
SEEK-AT-WD projects illustrate how the Web of Data can
be used as a data source, reducing the overall authoring effort
and improving the sustainability of the solution. While the
outlined cases are mainly driven by a single organization,
cross-institutional developments are expected in the near
future, especially in mobility, employability and quality
assurance EHEA scenarios that require further coordination
actions. One early example is the Linked Universities project
(http://linkeduniversities.org/) that aims to create a common
space of universities‟ Linked Data thus reducing the overall
effort.
As a wrap-up of this discussion, Table II extracts the main
challenges that can serve as a roadmap for better supporting
the emergent EHEA with Linked Data technologies.
TABLE II: LINKED DATA CHALLENGES AHEAD FOR SUPPORTING THE
EMERGENT EHEA
Challenge Comment
Exposure of more datasets
Steady progress and better supporting
tools
Availability of vocabularies
New proposals and vocabulary
convergence
Linked Data technology
concerns
Scalability and data privacy
Appealing applications Demonstrators already available
Cross-institutional
developments
More value for the EHEA, inception
phase
V. CONCLUSIONS
The Bologna Process towards the construction of the
EHEA has been demonstrated to be especially challenging.
Steady progress over the past decade has led to a number of
milestones that include programme and curriculum reforms
as well as instruments such as the ECTS, the Diploma
Supplement and qualification frameworks. Despite this,
pivotal EHEA processes, e.g. credit recognition or student
mobility, are specially demanding due to the need for
cooperation among institutions and for overcoming the
difficulties of the integration and interoperation of data
scattered across diverse repositories.
Emergent Linked Data technologies have the potential to
address the main data challenges of the EHEA. In this sense,
initial deployments in the EHEA demonstrate the viability of
the Linked Data approach and its benefits for accessing,
integrating and consuming data in an homogeneous way. As
some pioneering EHEA institutions are releasing some of
their repositories, an incipient EHEA Web of Data is growing.
Preliminary applications show that retrieval of data is
consistent across all providers, benefitting from the
interconnection and structuring of Linked Data sources.
Furthermore, some of these developments such as SLE or
Talis Aspire are especially relevant to the TEL domain,
particularly to show the integration of disparate repositories
through a Linked Data layer.
In the near future, further progress is expected in the key
areas of the EHEA, driven by Linked Data technologies. For
instance, fostering students' mobility requires assessing the
characteristics and prerequisites of programme degrees, the
recognition of ECTS credits and competencies among HEIs,
and facilitating the transfer of student records. Exposing the
key datasets as Linked Data enables the creation of expert
information systems and advanced data processing
applications that automate, or at least better support, the
aforementioned tasks. If the challenges highlighted above are
adequately addressed, Linked Data technologies are expected
to enable a significant step towards the construction of the
EHEA.
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Guillermo Vega-Gorgojo
is a postdoctoral
researcher in
the Logic and Intelligent Data Group at
the University of Oslo in Norway. His research
interests include linked data, semantic web, big data,
search interfaces, distributed systems and e-learning.
Guillermo has a PhD in telecommunications
engineering from
the University of Valladolid in
Spain. He is also a member of the Intelligent &
Cooperative Systems
Research Group
at the
University of Valladolid, researching on disruptive technologies in Higher
Education.
Thanassis
Tiropanis
is a senior lecturer in the Web
and Internet Science Group at the University of
Southampton in the UK. His research interests
include web science, social networks, distributed
linked data infrastructures and linked data for higher
education. Thanassis has worked for many years in
research on Web infrastructures,
network and service
management, business-to-business applications, and
collaboration infrastructures for formal and informal
learning. He holds a diping in computer engineering and informatics from the
University of Patras, Greece, and a PhD in computer science from University
College London. He is a senior member of the IEEE, a chartered IT
professional with BCS, a fellow of the Higher Education Academy in the UK,
a professional member of ACM and a member of the Technical Chamber of
Greece.
David E. Millard
is a senior lecturer in the Web and
Internet Science Group at the University of
Southampton in the UK. His research interests have
long involved hypertext and Web research –
first the
area of open, adaptive, and contextual hypermedia,
and more recently Web 2.0, the semantic web,
knowledge and narrative interfaces, and the impact
of Web literacy on e-learning and mobile learning.
He
is
interested in
the
ways that
people
use
information
systems
in
the
wild, and
how we can use emergent social,
organizational, and semantic structures to help them make sense of their
world. David has a PhD in computer science from the University of
Southampton.
