Secure your environment with UiPath and CyberArk technologies - Session 1
Mediabase Ready and First Analysis Report
1. Coordination and Support Action
European Commission Seventh Framework Project (IST-257822)
Mediabase Ready and First Analysis Report
Deliverable D4.3
Editor: Michael Derntl (RWTH Aachen University)
Contributors: Adam Cooper, Manh Cuong Pham, Ralf Klamma, Dominik Renzel
Dissemination level: Public
Delivery date: 2011-09-30
Work Package WP4: Weak Signals Analysis – Emerging Reality
Dissemination Level Public
Status Version 1.0 — Final
Date September 30, 2011
2. Amendment History
Version Date Editor Description/Comments
1.0 30 Sept. 2011 Michael Derntl Final version
Contributors
Name Institution Role
Michael Derntl RWTH Aachen University Editor/Author
Adam Cooper University of Bolton (CETIS) Author
Ralf Klamma RWTH Aachen University Author
Manh Cuong Pham RWTH Aachen University Author
Dominik Renzel RWTH Aachen University Author
Paul Lefrere The Open University (OU) Reviewer
Lampros Stergioulas Brunel University Reviewer
Christian Voigt Zentrum für Soziale Innovation (ZSI) Reviewer
Deliverable description in the DoW:
The deliverable will describe the continuation of the established PROLEARN Mediabase
equipped with new tools combining the existing social network analysis with topic mining.
This will realize a structural-semantic analysis of signals from the Web 2.0 strongly related
to technology enhanced learning. Results from the analysis will be reported here but can be
obtained continuously from the Web interfaces of the Mediabase afterwards.
3. Contents
1 Introduction .......................................................................................................... 1
2 The TEL-Map Mediabase ........................................................................................2
2.1 Conceptual Model of the TEL-Map Mediabase ........................................................................... 3
2.2 Components Overview .................................................................................................................. 4
2.3 Analysis Approach ......................................................................................................................... 7
2.4 Potential Questions ..................................................................................................................... 10
3 Analysis of the European TEL Project Landscape .................................................. 12
3.1 Data Set ........................................................................................................................................ 12
3.2 TEL Projects as Social Networks ................................................................................................ 14
3.3 Project Consortium Progression................................................................................................. 15
3.3.1 FP7 Projects ....................................................................................................................... 15
3.3.2 All TEL Projects – FP6, FP7, and eContentplus .............................................................. 16
3.3.3 Identifying Project Clusters .............................................................................................. 17
3.4 Organizational Collaborations .................................................................................................... 19
3.4.1 Collaborations in FP7 projects ......................................................................................... 19
3.4.2 Collaborations in all TEL Projects: FP6, FP7, and eContentplus ................................... 21
3.4.3 Dynamic SNA of the TEL Project Landscape .................................................................. 25
3.5 Geo-Mapping TEL Projects.........................................................................................................28
4 Analysis of TEL Publication Outlets ...................................................................... 29
4.1 Data Set ........................................................................................................................................ 29
4.2 Social Network Analysis of TEL Venues and Papers ................................................................. 31
4.3 Co-Authorship Network Analysis ............................................................................................... 32
4.3.1 Formal Foundations.......................................................................................................... 32
4.3.2 Overview ............................................................................................................................ 32
4.3.3 Dynamic SNA .................................................................................................................... 34
4.3.4 Most Prolific Authors and Their Topics ........................................................................... 35
4.3.5 Overall TEL Co-authorship Network ............................................................................... 37
4.3.6 Central Authors in the Co-Authorship Network..............................................................38
4.4 Structural-Semantic Analysis: SNA and Topic Mining Combined ........................................... 39
4.5 Citation Network Analysis .......................................................................................................... 43
5 Analysis of the TEL Social Web .............................................................................44
5.1 Social Web Data Set .................................................................................................................... 45
5.2 Formal Foundations .................................................................................................................... 46
5.3 Results.......................................................................................................................................... 47
5.3.1 TEL Blog Network and Most Central Blogs ..................................................................... 47
4. 5.3.2 TEL Blog Clusters .............................................................................................................. 49
5.3.3 Bursts ................................................................................................................................. 51
6 Embeddable Interactive Visualizations and Queries ............................................. 52
7 Key Findings for Weak Signals ............................................................................. 55
7.1 TEL Projects................................................................................................................................. 55
7.2 TEL Papers................................................................................................................................... 56
7.3 TEL Social Web............................................................................................................................ 57
8 Conclusion........................................................................................................... 57
References ................................................................................................................. 58
Appendix A: TEL Projects — Timeline ........................................................................ 60
Appendix B: TEL Projects — SNA Metrics .................................................................... 61
5. Figures
Figure 1: Concept map underlying of the TEL-Map Mediabase metamodel. ............................................. 4
Figure 2: TEL-Map Mediabase components overview model. .................................................................... 5
Figure 3: Data model of TEL projects. ........................................................................................................ 12
Figure 4: Word clouds of project descriptions. .......................................................................................... 14
Figure 5: FP7 TEL projects graph visualization. ........................................................................................ 15
Figure 6: Project consortium progression between FP6, FP7, and eContentplus projects. .................... 17
Figure 7: Visualization of the FP7 collaboration graph.............................................................................. 19
Figure 8: Center region cut-out of the FP7 collaboration graph. ..............................................................20
Figure 9: Word cloud of the 20 word stems with highest frequency in the FP7 project descriptions .... 21
Figure 10: Partner collaborations spanning FP6, FP7, and eContentplus projects. ................................ 22
Figure 11: Local clustering of organizations plotted against (a) PageRank and (b) degree. .................... 24
Figure 12: Overall development of collaboration network since 2004. .................................................... 26
Figure 13: Impact of newly launched projects the collaboration network................................................ 26
Figure 14: Impact of organizations on collaboration. ................................................................................ 27
Figure 15: Development of the ratio of projects coordinated by novice organizations ............................ 27
Figure 16: Google Map overlay with organizations involved in TEL projects. .........................................28
Figure 17: Data model for TEL papers and events. ....................................................................................30
Figure 18: Word cloud of most frequent terms in TEL conference paper titles. ...................................... 31
Figure 19: Development model for conference communities.................................................................... 32
Figure 20: Cumulative annual (co-)author figures of selected TEL conferences over the last 10 years. 33
Figure 21: Co-authorship network visualization for the TEL conferences. .............................................. 33
Figure 22: Co-authorship network measures of five conferences in TEL................................................. 35
Figure 23: Most frequent terms in papers of top TEL authors in 2010. ................................................... 37
Figure 24: Complete co-authorship network in the core TEL venues. ..................................................... 37
Figure 25: Co-authorship network of the “inner circle” of authors in the core TEL venues. ..................38
Figure 26: Citation network measures of five conferences in TEL. .......................................................... 44
Figure 27: Relational model of the TEL blogosphere. ............................................................................... 45
Figure 28: Number of blogs added to and blog entries indexed in the TEL-Map Mediabase. ................ 46
Figure 29: TEL blogs link network visualization, excluding self-references. ........................................... 47
Figure 30: Top 100 word stems appearing in 2011 blog entries of the top 20 blogs................................ 49
Figure 31: Colored TEL blog clusters. .........................................................................................................50
Figure 32: Bursty terms appearing only in 2011. ....................................................................................... 51
Figure 33: Bursty terms with rising frequency over the last three years. ................................................. 52
Figure 34: Visualization of the same SQL query as a table (left) and as a graph (right). ........................ 53
Figure 35: SQL query visualization as an annotated timeline. .................................................................. 54
6. Tables
Table 1: Uses of social network analysis and topic mining in the TEL-Map Mediabase. ........................ 10
Table 2: Overview of the 77 TEL Projects in the TEL-Map Mediabase..................................................... 13
Table 3: TEL project clusters in FP6, FP7, and eContentplus (ECP) and the word clouds of their project
descriptions. ................................................................................................................................................. 18
Table 4: Top 30 organizations involved in TEL projects by PageRank. The numbers in square brackets
next to the values represent the rank of that value among all 604 organizations.................................... 23
Table 5: Strongest partnership bonds over all TEL projects in FP6, FP7 and eContentplus. ................. 25
Table 6: Selection of conferences relevant to the TEL community. .......................................................... 31
Table 7: Fifteen most prolific authors at conferences and journals with a broad TEL scope. Names
marked with an asterisk (*) indicate authors currently based in Europe. ................................................ 36
Table 8: Top 15 TEL authors by betweenness centrality. .......................................................................... 39
Table 9: Top ten co-author pairs in core TEL venues. ............................................................................... 39
Table 10: Betweenness centrality of authors of papers identified in D4.1. ...............................................40
Table 11: Summary of structural-semantic analysis: themes and matching papers. ............................... 41
Table 12: Top twenty blog sources by PageRank. The number in square bracket indicates the blog’s
overall rank for the respective metric. .......................................................................................................48
Table 13: Clusters of TEL blogs indexed in Mediabase.............................................................................. 49
7. 1 Introduction
The European Framework Programmes (FP) for Research and Technological Development are a key
pillar of the European research area and act as the primary vehicle for the European Union to create
and sustain growth, employment and global competitiveness [3]. FPs are complex frameworks
defining the specific research programmes and challenges to be tackled over a seven-year period with a
multi-billion Euro budget. In FP7, the Cooperation programme, which also hosts the Technology
Enhanced Learning (TEL) thematic area, received the largest share of the total FP7 funds. For the
twenty-six partly completed and partly running TEL projects in FP7 the European Commission has
provided or will provide a total funding of more than one hundred million Euro. People and
organizations with a stake in TEL research and development are likely to be interested in knowing
where this enormous amount of money went and what impact it has generated and is generating on
the TEL landscape. First and foremost, the European Commission itself is interested in what impact
the spending has generated over the years. In addition, there are many organizations and individuals
in Europe that have a stake in TEL, e.g. technology providers, technology adopters, and higher
education institutes, to name a few (see [16] for a more comprehensive list of TEL-Map stakeholders).
To address the issue of generating such information based on strong and weak signals in a variety of
(web) sources, one core threads pursued in TEL-Map includes the application of social network
analysis and visualization as well as topic mining.
This deliverable reports on social network analysis and topic mining work performed in WP4, “Weak
Signal Analysis—Emerging Reality,” to support weak signal analysis and the mapping of the current
TEL landscape. To achieve this, the deliverable outlines the conceptual foundations of the TEL-Map
Mediabase, where all underlying data sources were stored, and presents first results of the analyses.
The main task underlying the work reported in this deliverable is Task 3 in WP4, which comprises the
following objectives:
• TEL-Map Mediabase: Based on the PROLEARN Mediabase the aim was to develop a TEL-
Map Mediabase, which shall contain social media artifacts and related resources to support
the mapping of the TEL landscape and complement the Delphi-based weak signal analysis
approach reported in D4.1 [23]. The focus in the TEL-Map Mediabase shall be on issues,
topics, and structures of relevance to TEL. This required a filtering of existing Mediabase
content, an extension of the sources fed into Mediabase with TEL-related content and
development of new tools to support analyses of these extended sources. The TEL-Map
Mediabase is presented in Section 2.
• Social Network Analysis: One of the pillars of the analysis methodology in WP4 is social
network analysis (SNA) of actors involved in TEL and their relationships. “Actor” is meant as
an abstract concept in this context, which can refer to various kinds of entities like people,
conferences, projects, publications, and so forth. SNA offers highly effective methods for
obtaining interactive visualizations and network metrics for these social networks, allowing
the identification of the most important actors from a wide range of perspectives. In this
deliverable the focus is on analysis of TEL projects and organizations involved in those
projects (Section 3); TEL papers, authors and publication outlets (Section 4); and TEL social
media sources (Section 5).
• Topic Mining: In addition to the network-metrics and structural analysis approach taken in
SNA, the analysis methodology shall be complemented with a topic mining approach. The
objective is to identify bursty topics, shifts in topics, emerging/declining topics from a variety
of sources in TEL, achieving a structural-semantic analysis of signals. This is tackled in
combination with SNA in Sections 3 through 5.
–1–
8. • Continuous analysis: As indicated in the title, this deliverable was conceived to present a
first analysis report, while TEL stakeholders shall be supported in continuously obtaining up-
to-date analysis results from the Mediabase web interfaces. This requires web-based tools for
continuous analysis of TEL sources (dealt with in Section 6) and an online resource page
where data sets and data processing components can be accessed and/or downloaded. For the
latter goal, a resource page was set up on the TEL-Map homepage. This D4.3 resource page is
available at http://telmap.org/?q=content/d4.3 and will be continuously updated with
pointers to results obtained, tools developed, and analyses performed in WP4—Task 3, which
will continue to run until the end of the project.
In regard to the embedding of this work into TEL-Map’s overall WP structure, the WP4’s mission—i.e.
the identification of weak signals that can inform the overall road-mapping process—also requires us
to propel the convergence of different analytical methods. For instance, this can be achieved in WP4 by
feeding results from one analytical method into another one in order to cross-validate and enrich
existing findings, but it also needs to happen between WPs, e.g. by informing WP5’s gap analysis and
WP3’s scenario building. Gap analysis aims to explore why some technologies seem to be much more
prominent in TEL research than in TEL practice (e.g. consider the uptake of 3D worlds) and other
technologies are slowly becoming mainstream with no matching amount of research available (e.g.
laptops in schools or social media at the workplace). Here, weak signals can inform an in-depth
analysis of specific technologies by considering the spread of awareness of that technology across
various communities as well as the use of synonyms referring to the same set of issues but under
different labels. Likewise, scenario building events (WP3) can be informed through weak signals as
they are early indicators of change that have the potential to alter the future of TEL adopters and TEL
providers. In this context, scenarios that consciously consider weak signals increase their robustness,
leading to better strategic planning processes.
This deliverable is structured as follows. In Section 2 we introduce the TEL-Map Mediabase,
containing data relevant to TEL in terms of projects, publications, and social media. Each of the
subsequent sections presents first analyses performed and results obtained in the TEL-Map Mediabase
sources, i.e. TEL projects in Section 3, TEL publications in Section 4, and TEL blogosphere in
Section 5. An embeddable, widget-based toolkit for enabling stakeholders to query and visually
interact with the data contained in the TEL-Map Mediabase is presented in Section 6. Section 7 draws
key findings from the analysis for weak signals collection from the core analysis sections, and Section 8
wraps up the deliverable with a discussion of limitations and an outlook on upcoming work in WP4.
2 The TEL-Map Mediabase
TEL-Map Mediabase is an evolution of the established PROLEARN Mediabase. In this section we first
describe the original idea and concept of Mediabase and continue with detailing the structure, content,
and meta-model of the enhanced TEL-Map Mediabase.
In the PROLEARN project1, a TEL project funded by the European Commission under FP6, one core
effort was the creation and maintenance of a media base for TEL in Europe, providing different target
audiences like scientists, policy makers, and communities of practice with digital information obtained
from mailing lists, newsletters, blogs, RSS/Atom feeds, websites, and so forth [10]. In addition to
collecting large amounts of data, one key objective was the provision of easy-to-use end-user tools for
extracting and presenting relevant information contained in the Mediabase, e.g. for cross-media social
network analysis, self-observation and self-modeling of communities [18], collaborative
administration and retrieval of media artifacts, etc. The key concepts in the metamodel of the
PROLEARN Mediabase are (cf. [10], p. 248-9):
1 http://www.prolearn-project.org
–2–
9. • Community as a sub-network of the whole network, representing trustful relations among its
members;
• Process as a value-adding set of activities performed by community members, e.g. acquisition,
retrieval, monitoring;
• Actor as humans, users or groups of humans/users performing and being affected by
processes;
• Medium as an artifact produced or consumed by processes.
For the development of the TEL-Map Mediabase, particular emphasis was put on the TEL
blogosphere, which is being observed and continuously retrieved using special-purpose crawlers (cf.
[9]); the blogosphere sources in the Mediabase were extended by the TEL-Map members. In addition,
the artifacts stored and indexed in the Mediabase were extended with digital information on European
TEL projects as well as publications in TEL-related conferences and journals.
2.1 Conceptual Model of the TEL-Map Mediabase
TEL-Map aims to empower stakeholders to find relevant projects and useful outputs as well as new
collaborators for TEL projects; it also aims at giving a rich overview of different types of actors
involved in the TEL domain (see DoW, p. 17-18). WP4 in particular focuses on analyses and
visualizations from social media items gathered and automatically crawled from relevant sources. To
realize these ambitious objectives, we have enhanced and extended the metamodel and the content of
the existing PROLEARN Mediabase. This enhanced TEL-Map Mediabase additionally includes
information on TEL projects and participants funded by the European Commission, as well as authors
and their papers published in TEL-related conferences and journals.
The conceptual model of TEL-Map Mediabase is displayed in Figure 1. It exposes three main areas:
• TEL Social Media: blogs, feeds, and blog entries; currently focusing on the blogosphere that
includes TEL-related blog sources.
• TEL Projects: information on projects funded by the European Commission under FP6, FP7,
and eContentplus, including information on participating organizations.
• TEL Papers: information on papers published in TEL-related journals, conferences, and
workshops.
For each of these three areas there is a dedicated database schema. These schemas are described in
detail in the relevant sections. There are several components (crawlers, importers, exporters, and end-
user tools) which were developed to obtain the relevant data, to feed the data into the database, as well
as to extract and interact with the data. These are described in Section 2.2.
Limitations. While the TEL-Map Mediabase databases contain an enormous amount of data, there
are several concepts and their links in Figure 1 which are currently not or only partly represented in
the data. These include:
• Meeting and Project Meeting: While we have data on conference and workshop events in the
TEL Papers database, we do not yet have data on project meetings (some of which are
collocated with other events). This information is missing since there we do not yet have
mechanisms of automatically obtaining these data.
• Deliverable: Project deliverables are also not yet included. This can be done in the future by
crawling the web pages of the projects stored in our TEL Projects database. However, we
expect that manual editing will be required, since the deliverable pages are not uniform across
different projects. For some projects, the deliverables cannot be found at all on the project
website.
–3–
10. TEL Social Media
Blogosphere
part of
has
Blog Comment
has
TEL Papers
ref's
ref's published
at
Entry Publication Paper Venue
ref's post is a
has is a
has
author of
Person Author
associated is a
with
TEL Projects Journal
Organization
take Deliverable
part in
consortium
member Conference Workshop
produce
Project
Project
meeting
organize
Meeting
is a
collocated with
Figure 1: Concept map underlying of the TEL-Map Mediabase metamodel.
• Person: The concept “person” is actually the glue between the three different databases, since a
person can be an author of a paper in the TEL Papers data, the owner of a blog in the TEL
Media data, and a member of an organization participating in a project indexed in the TEL
Projects data. We do currently not have an automated procedure that is capable of matching
and obtaining data related to persons, mostly because the data is not readily available (e.g.
some blogs do not contain personal information on their author, and most projects do not
provide detailed information on the persons involved). We aim to work toward this integration
in upcoming WP4 work.
2.2 Components Overview
The components of TEL-Map Mediabase are conceptually arranged in different groups or layers (see
Figure 2): the information to be used for weak signal analysis in the context of Mediabase is contained
in many different web data sources. To collect and filter the relevant information in structured format,
a set of importers and crawlers were deployed, which ingest the relevant data into different databases
(or database schemas). To process the data for analysis, visualization or any other kind of interaction,
a set of exporters enables end-user applications to obtain and present the data. The layers and their
components are described in detail below.
Importers. This layer includes services and processes that obtain relevant data from web sources and
transform these data into a structured, relational database format.
• Blog Crawler: The blog crawler is deployed as a cron job, which runs every night. It crawls the
RSS/Atom feeds and the websites of indexed sources and extracts new entries and ingests
–4–
11. Web Data Sources
European Community DBLP Publisher
Information Pages Blogosphere
Bibliography Pages
LearningFrontiers
Importers Portal
Projects DBLP Abstracts Blog Feed Feed
Crawler Importer Crawler Crawler Importer Aggregator
Mediabase
Databases Commander
TEL Projects TEL Papers TEL Media
Exporters
CSV Data GraphML Visualization
Exporter Exporter Widget Creator
Legend
Service /
Data Processing Apps Process
Graph Visualization Query Widgets
and Analysis Apps
Database
R Excel Query Query
yEd Graphviz Visualizer Explorer
End-User
Application
Matlab ...
Gephi ...
Data Flow
Figure 2: TEL-Map Mediabase components overview model.
those into the database. Upon ingestion it not only stores the raw HTML of the entries; it also
extracts a plain-text, non-markup version of the content, the comments associated with each
blog entry, the URLs it references, and it computes burstiness of terms occurring in blog
entries. The blogs scheduled for indexing are entered in two ways: (1) directly through the
Mediabase Commander on the Learning Frontiers portal, or (2) indirectly through the Feed
Aggregator, which is installed on the Learning Frontiers portal to collect links to relevant RSS
or Atom feeds. These feeds are automatically ingested into the TEL Media database by the
Feed Importer.
• Abstracts Crawler: The TEL Papers database contains data like title, authors and citations on
TEL-related papers. Since DBLP, the data source of the TEL papers database, does not contain
abstracts and keywords, the goal of this crawler is to enhance the basic paper information with
abstracts and keywords. The following conferences were crawled: ECTEL, ICWL, ICALT, ITS,
DIGITEL and WMTE. Since the crawler supports the abstract pages of springerlink.com
(Springer Verlag), computer.org and IEEExplore, the crawler can be used to crawl many more
conferences. The crawler is written in Ruby using the Mechanize Library for extracting the
information from the HTML pages. The crawler does not directly interact with the TEL papers
database. Instead, desired information from the database has to be exported and imported as
CSV data.
• Feed Importer: One objective of TEL-Map is to analyze the voices in TEL to detect weak
signals. This required enriching the Mediabase with TEL-related social media artifacts2. On
the Learning Frontiers portal, we installed the aggregator module, which allows registered
2 See task 3 in the description of WP4 in the DoW, p. 39: “We will integrate current RSS aggregators to
enhance the contents of the Mediabase.”
–5–
12. users to provide links to their favorite TEL-related feeds, either RSS or Atom feeds. This
module offers several forms of access to the aggregated feeds, e.g., directly through Drupal’s
mysql relational database or through a machine-processible OPML file that contains all RSS or
Atom feed sources, or through the Learning Frontiers portal front-end, which will display the
recent feed entries to the user as an HTML page. To integrate the aggregated feeds into
Mediabase, we developed a module that fetches all feeds from the feed aggregator that were
not yet ingested into Mediabase; for each matching feed, the module then creates a
blogwatcher project entry (including the feed’s tag associations) in Mediabase. Once a day, a
blog crawler processes the blogs and adds all blog entries to Mediabase (including older
entries that do not show up in the current RSS/Atom feed).
• DBLP Importer: The records in the papers database were obtained from DBLP, a free and
open bibliography mainly for computer science and its sub-disciplines. DBLP data is valuable
since it includes information on conference series and journals, authors, and the papers
published in the conferences and journals. Importing the data is done via an XML file that
includes all DBLP records. The DBLP importer extracts these records and stores them in a
relational database schema. In addition it is capable of extracting citation information on the
imported papers using the CiteSeerX database.
• Projects Crawler: In order to collect information about the running (or completed) TEL
projects, we developed a crawler that automatically scrapes data from the project factsheets on
the CORDIS website (for FP6 and FP7 projects), as well as from the eContentplus pages. All
projects funded under TEL-related calls were scraped. The extracted information contains
data like project description, start and end dates, project participants, funding and cost,
project coordinator, etc. The data from these fact sheets were in a first step transformed to an
XML-based format, which can be used by XML-processing applications like the project
landscape story on the Learning Frontiers portal3. In a second step, the data was fed into a
relational database schema to be used e.g. by the Drupal installation that is hosting the
Learning Frontiers portal4. Analyses performed using the projects data obtained by this
crawler are reported in Section 3.
Databases. The TEL-Map Mediabase database consists of a collection of three relational database
schemas, which are used to store and index TEL-related projects, papers, and social media artifacts
(currently mainly blogs).
• TEL Projects: This database includes details on TEL projects funded under FP6, FP7, and
eContentplus programmes. It includes detailed information on the projects like start and end
dates, cost, EC funding, coordinator, and consortium members. The TEL projects database is
fed by the Projects Crawler. Details on the project data set are given in Section 3.1.
• TEL Papers: This database includes information on TEL-related conference series, conference
events, journals, authors, and papers published in the conferences, workshops and journals. It
is fed by the DBLP Importer. Details on the papers data set are given in Section 4.1.
• TEL (Social) Media: This database includes TEL-related blogs, including the blog entries,
comments and analytical information like length, words occurrences, and word burst for
certain entries. Details on the blogosphere data set are given in Section 5.1.
Exporters. To enable analysis of the TEL-Map Mediabase data, the data are accessible either natively
via clients that connect to the database(s) using the database drivers, or via exporters. The exporters
ease the process of obtaining data for analysis by providing a set of predefined export formats.
3 http://learningfrontiers.eu/?q=story/tel-project-landscape
4 http://learningfrontiers.eu/?q=project_space
–6–
13. • CSV Data Exporter: Includes a set of scripts that export data contained in the databases into
CSV format (CSV = comma separated values). These CSV files are supported by most data
processing applications like Excel, R, SPSS, and so forth.
• GraphML Exporter: Data can also be exported as graphs for social network analysis. The data
is exported in the most common graph exchange format, i.e. the XML-based GraphML
language. These GraphML files can be imported, visualized, and analyzed in graph
visualization and analysis applications like yEd, Gephi, or the igraph library for R. For many
other graph visualization and analysis software packages, there are conversion tools from and
to GraphML.
• Query Visualizer and Query Explorer: interacting with social network visualizations reaches
its limits when it comes to specific queries that focus on selected aspects of the data set or the
network graphs. To enable efficient end-user interaction with the data, we implemented a set
of query visualization widgets. These widgets can be embedded on any web page (e.g. in
iGoogle) and allow direct querying of the databases using SQL. The unique feature of these
widgets is that they can be used to visualize the query results in different formats (e.g. table,
pie chart, timeline, or graph) and that they can export the visualization of any given query as a
widget. Additionally, CSV and GraphML export (see above) of query results is supported by
the explorer widget. More details in Section 6.
Applications. End-users will mostly interact with the data through applications like Excel, R, and the
Learning Frontiers portal. While Figure 2 includes many example applications, the following list only
focuses on those that were developed for TEL-Map:
• Learning Frontiers Portal: The Learning Frontiers portal is the single-access-point portal to
results generated in the TEL-Map project. It includes two apps that can be used to contribute
to content generation in the TEL Media database: The Mediabase Commander enables adding
blogs directly to the database, and the Feed Aggregator is a Drupal module that we installed to
allow users to collect relevant feeds. The feeds are ingested into the database at regular
intervals by the Feed Importer. Note that Mediabase Commander (MBC) is also available as a
Firefox add-on.
• Query Widgets: We developed a set of widgets that can be used to (a) query the TEL-Map
Mediabase databases using SQL, (b) to automatically visualize the query results in different
formats, (c) export the query result in different formats, and (d) to export a query visualization
as a self-contained widget that can be embedded into any web site.
2.3 Analysis Approach
This deliverable reports on first results of using social network analysis (SNA) and topic mining on the
data stored in the TEL-Map Mediabase. SNA contributes to the structural analysis of actors and their
relationships and topic mining contributes to the semantic analysis of actors and relationships
between actors. The combination of SNA and topic mining thus enables the structural-semantic
analysis of TEL sources.
Social Network Analysis (SNA) is one of the work threads pursued in WP4 of TEL-Map to detect
weak signals [23, 6] indicating future directions and insight into collaboration and communication
networks in different types of media and settings. SNA constitutes a rather new field of research and
its application to digital libraries is very promising in terms of knowledge discovery [19, 20]. SNA
defines techniques used to compute metrics of different actors in a social network. These metrics
typically represent the importance of actors within their network or neighborhood, e.g. their centrality,
connectedness, etc.
–7–
14. To enable the calculation of SNA metrics for the data in TEL-Map Mediabase, the entities stored in the
Mediabase need to be modeled as a social network. A social network is modeled as a graph = ,
with being the set of vertices (or nodes) and being the set of edges connecting the vertices with one
another [2]. Any “actor” entity in the Mediabase can be modeled as a vertex, if it is connected to other
actors through any relationship of interest (modeled as edges) that can be obtained from the
Mediabase data. For instance, consider the following social network graphs:
• TEL projects can be modeled as nodes and overlaps in the consortia of any two projects can be
modeled as edges;
• Organizations can be modeled as nodes, while projects in which organizations collaborated
can be modeled as edges;
• Persons can be modeled as nodes, while co-authorships on papers relevant to TEL can be
modeled as edges;
• Papers can be modeled as nodes, while citations between papers can be modeled as edges;
• Blogs can be modeled as nodes, while links between the blogs’ entries can be modeled as
edges.
There are several different, yet complementary methods of gaining insight into the modeled social
network graphs:
(1) Visual interaction: The graph can be visualized using graph visualization software (like yEd,
Graphviz, or Gephi). Similar to maps software like Google Maps, graph visualization software typically
allows the user to zoom (vertical filter) into the visualization and to pan the visualized graph
(horizontal filter). In addition these tools often offer graph layout algorithms, which can be used to
align the vertices in a predefined shape (e.g. circular, organic, hierarchical, etc.). Graph visualization
generally provides a holistic, condensed view on the overall network.
(2) Data querying: Interacting with graph visualizations will typically spawn more specific questions
and exploratory tasks [5]. Some of these explorations cannot be performed using the visualization
alone, e.g. the number of shortest paths through the network that lead through a particular node. Such
results can be obtained by enabling querying into the graph data. We developed a web-based toolkit for
enabling this (see Section 6).
(3) SNA Metrics: SNA allows the computation of different metrics for the graph, its nodes and its
edges. In the SNA reported in this deliverable, we mainly focus on the following metrics:
• Avg. shortest path length: this is a graph metric that represents the average length of all
shortest paths through the network. Over time this metric will grow quickly initially, but slows
down or may even shrink in “mature” graphs.
• Diameter: This represents the length of the longest shortest path through the network. In
isolation this value will not be very informative; it is useful however for comparing network
development over time (see e.g. Section 3.4.3).
• Largest connected component: This measure represents the number (or the share) of nodes
that are connected with each other in the largest sub-network of the graph. The lower this
value, the higher the fragmentation in the network.
• Density: This metric represents the ratio between the number of existing connections in the
graph and the number of possible connections. The higher this value, the higher the
connectedness of the nodes. One observation of interest is the development of density over
–8–
15. time, when new nodes join the graph, to see whether these new nodes inter-connect tightly
with the existing ones.
• Betweenness centrality: The betweenness centrality of a node represents the share of shortest
paths through the network that pass through that node. The betweenness centrality is typically
higher for nodes that connect (“bridge”) two or more sub-networks (also called “connected
components”) in the network. For instance, an author who works in the intersection of
artificial intelligence and technology-enhanced learning is likely to have a higher betweenness
centrality in a co-authorship network than a person in the same network who only publishes
with members of the core artificial intelligence community.
• Degree centrality: The degree of a node is represented by the number of its direct ties with
other nodes, i.e. edges coming in and leading out of that node. Typically this value is
normalized into a value between 0 and 1 by dividing the degree of a node by the number of
other nodes in the graph. This is the simplest centrality measure for network analysis
• Closeness centrality: This measure is used to determine how close a node is to all other nodes
that are reachable via edges. The closeness centrality is obtained by computing the mean
length of these (shortest) paths. Nodes with a favorable closeness centrality are important
nodes in the sense that they can easily reach other nodes for collaboration, information, or
influence.
• PageRank: This measure became widely known through Google’s use of it for ranking web
sites by importance [17]. The PageRank of a node depends on the PageRank of nodes
connected to it. So a node being connected to another node that is important makes the source
node more important, too. With increasing distance between nodes this “diffusion” of
importance to other nodes is gradually reduced by a damping factor.
• Clustering coefficient: The clustering of a node (local clustering) measures how strongly the
neighborhood of the node tends towards forming a clique, where every two nodes are
connected by an edge. The clustering coefficient of the whole network is obtained by
computing the average local clustering coefficient of its nodes.
• Authorities and Hubs: authorities refer to nodes that represent authoritative sources of
information in the network that are being pointed to by good hubs; a good hub is a node that
point to many good authorities [12]. Thus there is a circular dependency between these two
metrics.
Topic Mining is an approach for discovering knowledge from text sources. Typically topics are
described by word distributions and sometimes also time distributions (cf. [24]). In the context of this
deliverable we use a simplified approach to topic mining that mainly focuses on term stems and their
frequency of appearance in the content entities stored in the Mediabase (e.g. blog text, paper abstracts,
project descriptions) at a particular point in time or in a particular time window. For the first
structural-semantic analyses reported in this deliverable, we focused on a “big picture” approach to
complementing social network metrics with content analysis for different sources and actors in the
TEL-Map Mediabase. This includes:
• For illustrating topic distribution in large sources we filtered the sources by identifying sources
that are linked to key actors in the community (e.g. central organizations in projects, entries of
central blogs). Following this, we present the core topics represented in these sources either
through word clouds or through analysis of rising and falling frequency of topic occurrence in
the sources.
–9–
16. • Building on the topic mining approach of selected TEL conferences in D4.1, we filtered the
results for sources that were contributed by key authors in these conferences’ co-authorship
networks and extracted weak signals there.
2.4 Potential Questions
The combined results of SNA and topic mining can give rich insight into the available data and be used
to detect and explore potential signals (both strong and weak ones) in the data. The matrix in Table 1
gives a brief overview of questions addressed by using SNA and topic mining on the different data
sources in the TEL-Map Mediabase.
Table 1: Uses of social network analysis and topic mining in the TEL-Map Mediabase.
Social Network Analysis Topic Mining
TEL Papers • Most central authors in TEL • Rising and falling terms in TEL paper
• Most frequent collaborations on TEL abstracts and keywords
papers • Topics addressed by most important
• Most important TEL conferences and TEL authors/papers
journals
• Development characteristics of
authorship networks in TEL conferences.
TEL Projects • Consortium progression between • Topic distribution and shifts in TEL
projects project foci over time
• Partner collaborations across TEL • Funding and partners related to topics
projects in TEL projects
• Most central organizations in TEL
projects
• Most central TEL projects
• Development of SNA metrics in project
collaboration network over time
TEL Media • Citation network in TEL blogs • Topic bursts in TEL blogs over time
• Most central web sources referenced in • Recently appearing topics
TEL blogs • Topics with a rising frequency over the
• Authorities and hubs in the TEL last years
blogosphere
• Co-occurrence of words/bursts in blog
entries
In the following, we elaborate more on the objectives and potential signals that can be identified by
tackling the questions outlined in Table 1.
TEL Papers Social Network Analysis and Topic Mining:
• Most central authors in (European) TEL: identifies authors that have a central position in the
co-authorship and citation network of TEL papers; these authors are likely to have authority
regarding the focus of current TEL research and directions for future TEL research, which can
be analyzed using topic mining.
• Most frequent collaborations in TEL: Since TEL research is collaborative work, the
identification of most important authors is complemented with collaboration frequency to
identify strong ties between authors and communities.
– 10 –
17. • Most important TEL conferences and journals: identifying the most important outlets for
publishing TEL research results will indicate venues where TEL key people meet for exchange
and collaboration. Knowing the core TEL conferences will facilitate researchers in finding
relevant collaborators.
• Development characteristics of TEL conferences: identifies patterns of development of
authorship networks, which will reveal several insightful network characteristics, e.g. whether
the TEL community is a fragmented community, whether TEL conferences develop like
conferences in other disciplines, etc.
• Rising and falling terms in TEL papers: analysis of these terms will reveal topics and topic
shifts in published TEL research. Of course, published TEL research is only a fraction of the
research actually performed, and typically conference papers are up to one year behind the
actual research work. For journal papers this lag is even worse, since journal papers often
appear only 2-3 years after submission of the manuscript.
• Topics addressed by prolific authors: Prolific or otherwise central authors identified in the co-
authorship networks of different (sets of) publication outlets can be used for revealing topics
that likely have impact on current and future work.
TEL Projects Social Network Analysis and Topic Mining:
• Consortium progression between projects and partner collaborations across TEL projects: this
will identify organizational collaboration between different (consecutive and concurrent)
projects that sustain beyond the lifetime of one project’s consortium. Strong partnership ties
between organizations on the one hand, and new project funding for participants of a project
may indicate fruitful and successful collaboration in that project and can thus be considered as
an indicator of project success.
• Most central TEL projects: analysis of consortium progression will also identify the most
central projects in terms of having the largest consortium overlap with other projects,
connecting different succeeding and preceding projects, and similar centrality measures.
• Most central organizations in TEL projects: SNA can be used to identify the most central
organizations in the TEL collaboration network in terms of number of connections, closeness
to other organizations in the network, and connections between different organizational
clusters or sub-networks.
• Development of SNA metrics in project collaboration network over time: dynamic analysis of
the collaboration network in projects over different funding calls or years will identify several
characteristics of development patterns in the European TEL “market”, including development
of collaboration network characteristics over time, impact of new projects on the collaboration
network (e.g. introduction new organizations introduced by new projects) over time, and
impact of new organizations on the creation of new collaboration ties between organizations.
• Topic distribution in projects can be analyzed using the descriptions of projects or project
clusters which were previously identified by SNA.
TEL Media Social Network Analysis and Topic Mining:
• Citation network in TEL blogs: identifies the most central blogs and blog entries in the TEL
blogosphere and can be used in combination with topic mining on those blogs to identify
trending, upcoming, and declining topics.
– 11 –
18. • Most authoritative web sources referenced in TEL blogs: in addition to citing sources in the
blogosphere, bloggers reference all sorts of sources on the web; analyzing these can help to
identify the most authoritative (type of) sources on the web for TEL bloggers (this will be
tackled in upcoming WP4 work)
• Topic bursts in TEL blogs over time: based on frequently occurring words in social media
sources we are able to identify newly emerging terms and topics as well as topics with rising or
falling frequency. This analysis is enhanced by filtering for those blogs that have a central
position in the blogosphere.
3 Analysis of the European TEL Project Landscape
There currently exists no readily available, structured data set on TEL projects funded in recent
programmes, with the exception of HTML factsheets offered on the web by the European Commission
as well as a load of project websites and deliverables produced by the project consortia. Turning
information overload into an opportunity is the driving vision of visual analytics [7], and this section
aims to achieve this vision in the context of TEL projects funded under FP6, FP7 and eContentplus
programmes by applying SNA and information visualization methods on projects and collaborations
within project consortia.
3.1 Data Set
Data Model. The database used for the analyses in this paper was scraped from publicly available
project information pages on CORDIS [4], i.e. the Community Research and Development Information
Service offered by the European Commission, and other European Community project information
pages. The scraped data was captured according to the data model presented in Figure 3 and fed into a
relational database. The data scraping was focused on TEL-related projects funded under FP6, FP7
and eContentplus.
ROLE participate Organization has_location
N 1
ID
N 1
NAME
COUNTRY
Project Geolocation
ID
ID CONTRACT_NO TITLE ACRONYM
LATITUDE
DESCRIPTION DATE_START DATE_END TYPE
LONGITUDE
PROGRAMME CALL COST FUNDING
PRECISION
WEBSITE_URL FACTSHEET_URL RCN
Figure 3: Data model of TEL projects.
– 12 –
19. Information that was not available in CORDIS includes the geographical coordinates of project
members. These locations were semi-automatically obtained by invoking the Google Maps API and
Yahoo Maps API using the partner names and countries provided in the factsheets. Since some of the
partner names produced ambiguous geographical results, the geographical coordinates will not be
correct for some institutions. Also, the spelling of organization names and country names was
inconsistent in the project fact sheets in many cases; this was corrected manually (which still does not
guarantee correctness). Additionally, organizational name changes are not accounted for. For instance,
Giunti Labs S.R.L. was rebranded to eXact Learning Solutions in 2010. In the data set, these—and all
organizations with similar rebrandings—are represented as separate entities. Likewise, organizational
mergers are not accounted for, e.g. ATOS Origin and Siemens Learning, which merged in 2011.
Selection of TEL Projects. Table 2 includes the details on the 77 TEL projects used in the following
analyses, and a visual timeline of these projects can be found in Appendix A.
Table 2: Overview of the 77 TEL Projects in the TEL-Map Mediabase.
Programme Call # Projects (acronyms)
Call 2005 4 CITER, JEM, MACE, MELT
COSMOS, EdReNe, EUROGENE, eVip, Intergeo, KeyToNature,
Call 2006 7
eContenplus5 Organic.Edunet
Call 2007 3 ASPECT, iCOPER, EduTubePlus
Call 2008 5 LiLa, Math-Bridge, mEducator, OpenScienceResources, OpenScout
CONNECT, E-LEGI, ICLASS, KALEIDOSCOPE, LEACTIVEMATH,
IST-2002-2.3.1.12 a 8
PROLEARN, TELCERT, UNFOLD
APOSDLE, ARGUNAUT, ATGENTIVE, COOPER, ECIRCUS, ELEKTRA,
FP6 IST-2004-2.4.10 b 14 I-MAESTRO, KP-LAB, L2C, LEAD, PALETTE, PROLIX, RE.MATH,
TENCOMPETENCE
ARISE, CALIBRATE, ELU, EMAPPS.COM, ICAMP, LOGOS, LT4EL,
IST-2004-2.4.13 c 10
MGBL, UNITE, VEMUS
ICT-2007.4.1 d 6 80DAYS, GRAPPLE, IDSPACE, LTFLL, MATURE, SCY
COSPATIAL, DYNALEARN, INTELLEO, ROLE, STELLAR, TARGET,
ICT-2007.4.3 d 7
FP7 XDELIA
ALICE, ARISTOTELE, ECUTE, GALA, IMREAL, ITEC, METAFORA,
ICT-2009.4.2 b 13
MIROR, MIRROR, NEXT-TELL, SIREN, TEL-MAP, TERENCE
Total: 77
a … Technology-enhanced learning and access to cultural heritage
b … Technology-Enhanced Learning
c … Strengthening the Integration of the ICT research effort in an Enlarged Europe
d … Digital libraries and technology-enhanced learning
Topics and topic shifts. To give an indication of the topic focuses in these projects, Figure 4
presents for FP6, FP7, and eContentplus a word cloud of the funded projects’ descriptions. It reveals
an interesting difference between FP6 and FP7 projects. In FP6, we find many meta-concepts in the
descriptions like project, development, research, European, while descriptions of TEL projects in FP7
expose some concrete research and learning related topics like adaptive, social, design, process,
activities, and so forth. It could be argued that during FP6 the TEL landscape was gradually beginning
to take form, while in FP7 the research agenda already included several hot topics.
5 For each eContentplus call, only projects funded under the “Educational content” category were considered.
The project SHARE-TEC (call 2007) was excluded from the data, since there was no official fact sheet available.
– 13 –
20. Looking at eContentplus in comparison to FP6 and FP7, there is a strong emphasis on content and
metadata, while still including heavy use of educational and learning as terms. Content is a term
found also in FP6 with some frequency, but it is missing in the top term list of FP7, probably showing
that the eContentplus participants and the European Commission were targeting different foci.
FP6 FP7
eContentplus All TEL projects
Figure 4: Word clouds of project descriptions.
3.2 TEL Projects as Social Networks
A TEL project—like any other collaborative type of project—can be modeled as a social network where
a number of partner organizations collaborate under coordination of a coordinating organization. A
social network is modeled as a graph = , with being the set of vertices (or nodes) and being
the set of edges connecting the vertices with one another [2].
Let be the set of projects, and let be the set of organizations involved in these projects. Function
represents the membership of any organization ∈ in the consortium of any project ∈ and is
defined as follows:
, if ∈ participated or particiaptes in ∈
∶ →
, otherwise .
The data model and these formal foundations enable powerful analyses and visualizations including
the project network, the organizational partnership network, temporal relationships between project
consortia, and the geographical mapping of organizations involved in projects. A selection of these
analyses is presented in the following sub-sections, focusing on these objectives:
• Visualizing and analyzing project consortium progression. By progression we mean
partnerships within project consortia that sustain beyond one single project. Investigating
these dynamics can be used to identify successful and strongly connected organizations
between consortia of different projects. This objective is tackled in Section 3.3.
• Visualizing and analyzing organizational collaborations within projects. Repeated
collaboration in projects will create strong ties between organizations. Computing social
network metrics for those connections will reveal the most important organizations currently
involved in TEL research. This objective is dealt with in Section 3.4.
– 14 –
21. • Interactive visualization of geographical distribution of project consortia to complement the
social network metric-based approaches with geographical map overlays, identifying hotspots
in the European TEL landscape. This objective is dealt with in Section 3.5.
3.3 Project Consortium Progression
The project consortium progression graph =( , contains projects and their relationships with
each other based on overlapping consortia. The graph will show projects as nodes and an edge between
two nodes if there is any organization that has participated in both projects, i.e. = , and
= , ∶ , ∈ ∧ ≠ ∧∃ ∈ ∶ , ∧ , " .
can be modeled as a directed graph, which exposes the temporal progression of project consortia.
Each edge in this graph represents a temporal relationship between two connected projects: the edge
points from the project which started earlier to the project which started later.
3.3.1 FP7 Projects
A visualization of for the 26 FP7 projects is shown in Figure 5. The size of each node in this
visualization is proportional to the betweenness centrality [2] of that node, and the weight of the edge
was determined by the number of partners that overlap between two project consortia. The
betweenness centrality measure is an effective means of exposing nodes that act as “bridges” between
otherwise distant nodes (or groups of nodes) by computing for each node the share of all shortest
paths through the network that lead through the node.
COSPATIAL
TERENCE INTELLEO
METAFORA
MIROR
TEL-MAP
MIRROR ITEC
80DAYS GALA
STELLAR
NEXT-TELL LTFLL
DYNALEARN GRAPPLE
XDELIA
IMREAL ROLE
ECUTE
TARGET IDSPACE
MATURE
SIREN
SCY
ALICE
ARISTOTELE
Figure 5: FP7 TEL projects graph visualization.
– 15 –
22. The visualization of project connections in Figure 5 exposes one node that could be labeled as the
current “epicenter” of TEL projects in FP7. This node represents GALA, the network of excellence on
serious games [29]. There are two main factors why this project is such a strong connector:
1. the consortium is extraordinarily large with 31 participating organizations6, and
2. the project has started only recently in October 2010, following the most recently closed TEL
call in FP7 (see the projects timeline in Appendix A) .
Obviously, a project which starts later than other projects has a higher chance of having organizations
in its consortium which were already part of previous project consortia. Other projects that carried on
multiple consortium members to the GALA consortium are TARGET, GRAPPLE, and STELLAR.
Another strong, currently running project is ROLE, which is a harbor for project consortium
partnerships from previous projects, and also has overlaps with succeeding project consortia. If we had
computed the betweenness centrality of the projects taking into account the direction of the edges,
ROLE, STELLAR and MIRROR would be the most betweenness-central projects. Such a computation
would, however, statistically favor projects that have started in the middle between the begin date of
FP7 and the current date, since in this time window projects are more likely to have outgoing
consortium connections in addition the incoming ones.
3.3.2 All TEL Projects – FP6, FP7, and eContentplus
A graph of all TEL projects funded in FP6, FP7, and eContentplus is given in Figure 6. The graph
includes all 77 projects and a total of 712 connections between those projects. KALEIDOSCOPE is by
far the largest node, which can be attributed to the fact that this project had an extremely large
consortium of 83 partner organizations, which is more than five times the typical consortium size. It is
also evident in this visualization that in addition to strong ties between FP6 and FP7 projects, the
eContentplus projects have very strong connections to both FP6 and FP7. This can probably be
explained by the fact that eContentplus filled a “funding gap” in 2007 when FP6 funding was stalling
following the last FP6 projects launched in 2006, while FP7 funding was kicked off with the first TEL
projects starting in 2008. In fact, in 2007 only eContentplus projects were launched with EC funding
in our data set (compare also the dynamic network analysis in Section 3.4.3, in particular Figure 13d).
This kind of gap filling by eContentplus, where a large share of organizations funded under FP6 and
FP7 engaged in e-content focused R&D projects, could be interpreted as evidence for a “research
follows money” attitude of researchers involved in TEL. That is, if there had not been funding from
eContentplus, organizations would likely have looked for funding opportunities in TEL-related
programmes with different focus between 2006 and 2008.
A table with all projects displayed in Figure 6 along with their SNA metrics (and ranks) is given in
Appendix B.
6 See http://learningfrontiers.eu/?q=story/tel-project-landscape&proj=GALA and
http://www.learningfrontiers.eu/?q=tel_project/GALA
– 16 –
23. FP7
ALICE
SIREN
MATURE ARISTOTELE
NEXT-TELL IMREAL
ECUTE TARGET
MIROR
80DAYS
COSPATIAL
GALA METAFORA
INTELLEO MIRROR
DYNALEARN
GRAPPLEIDSPACE
XDELIA SCY
ROLE
TERENCE
STELLAR
ITEC
TEL-MAP
eContentplus LTFLL
LiLa eViP
FP6
I-MAESTRO
mEducator
ECIRCUS
MACE
EdReNe KeyToNature APOSDLE
UNFOLD
OpenScout
ASPECT COOPER
JEM Math-Bridge
TELCERT RE.MATH
iCOPER
EUROGENE
PROLEARN
MELT KALEIDOSCOPE CONNECT
EduTubePlus Intergeo
COSMOS MGBL ARGUNAUTARISE
ELEKTRA
PROLIX
Organic.Edunet
TENCOMPETENCE
OpenScienceResources
CITER E-LEGI UNITE LEACTIVEMATH
PALETTE
LT4EL ICLASS
VEMUS
ICAMP ELU
KP-LAB
L2C
LEAD
LOGOS
CALIBRATE
ATGENTIVE
EMAPPS.COM
Figure 6: Project consortium progression between FP6, FP7, and eContentplus projects.
3.3.3 Identifying Project Clusters
The project consortium progression graph was subjected to cluster analysis using the Louvain
method described in [1]. This method first divides the nodes into local clusters, and then collapses
each clusters’ nodes into a single node. These two steps are applied repeatedly until the final set of
clusters is reached.
There are 6 resulting clusters of projects as listed in Table 3:
• Cluster C0 includes mostly FP7 projects, with some FP6 and eContentplus projects, which
focus on learning, development, research and technology as evident form the word cloud
extract from these projects’ descriptions.
• Cluster C1 exposes the strongest thematic focus on learning (and education) of all clusters;
there are no other terms that really stand out. The cluster includes a mix of all funding
programmes.
• Cluster C2 shows a strong topical emphasis on content, collaboration, knowledge and support;
this cluster is well represented by projects from all funding schemes.
– 17 –
24. • Cluster C3 includes projects related development, content, competence, tools and testing. In
this cluster there is the smallest gap between frequency of occurrence of learning and other
terms.
• Cluster C4 has a strong focus on science and education, and also school is a term that stands
out.
• Cluster C5 emphasizes mostly on content, development and technology. It has the strongest
focus on content of all clusters; yet it includes not only eContentplus projects.
It is evident that eContentplus projects are spread over all clusters, indicating that this funding
programme (a) did not disrupt collaboration structures in TEL and (b) was definitely relevant for a
topic focus on educational content. Moreover, projects of all funding schemes are represented in all
clusters, indicating a coherent research agenda since the first FP6 projects.
Table 3: TEL project clusters in FP6, FP7, and eContentplus (ECP) and the word clouds of their
project descriptions.
ALICE [FP7], APOSDLE [FP6], COSPATIAL
[FP7], ECIRCUS [FP6], ECUTE [FP7], eViP
[ECP], GALA [FP7], I-MAESTRO [FP6],
C0
IMREAL [FP7], KALEIDOSCOPE [FP6],
MATURE [FP7], MIRROR [FP7], NEXT-
TELL [FP7], SIREN [FP7], TARGET [FP7]
80DAYS [FP7], CITER [ECP], DYNALEARN
[FP7], EduTubePlus [ECP], ELEKTRA
[FP6], ICLASS [FP6], Intergeo [ECP],
C1 LEACTIVEMATH [FP6], LiLa [ECP],
LOGOS [FP6], METAFORA [FP7], MIROR
[FP7], PALETTE [FP6], PROLEARN [FP6],
PROLIX [FP6], RE.MATH [FP6]
ATGENTIVE [FP6], E-LEGI [FP6],
EUROGENE [ECP], ICAMP [FP6], iCOPER
[ECP], INTELLEO [FP7], JEM [ECP], KP-
C2 LAB [FP6], L2C [FP6], LEAD [FP6], LT4EL
[FP6], LTFLL [FP7], mEducator [ECP],
OpenScout [ECP], ROLE [FP7], STELLAR
[FP7], TEL-MAP [FP7], XDELIA [FP7]
COOPER [FP6], GRAPPLE [FP7],
IDSPACE [FP7], MACE [ECP], Math-Bridge
C3
[ECP], TELCERT [FP6],
TENCOMPETENCE [FP6], UNFOLD [FP6]
ARGUNAUT [FP6], ARISE [FP6],
ARISTOTELE [FP7], CONNECT [FP6],
C4 COSMOS [ECP], OpenScienceResources
[ECP], Organic.Edunet [ECP], SCY [FP7],
UNITE [FP6], VEMUS [FP6]
ASPECT [ECP], CALIBRATE [FP6],
EdReNe [ECP], ELU [FP6], EMAPPS.COM
C5
[FP6], ITEC [FP7], KeyToNature [ECP],
MELT [ECP], MGBL [FP6], TERENCE [FP7]
– 18 –