SlideShare a Scribd company logo
1 of 67
Download to read offline
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
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.
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
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
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
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
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–
•   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–
•   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–
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–
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–
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–
•   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–
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–
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–
•   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 –
•   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 –
•   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 –
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 –
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 –
•   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 –
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 –
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 –
•    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 –
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report
Mediabase Ready and First Analysis Report

More Related Content

Similar to Mediabase Ready and First Analysis Report

Usage patterns and information needs of journalists on the internet (1999)
Usage patterns and information needs of journalists on the internet (1999)Usage patterns and information needs of journalists on the internet (1999)
Usage patterns and information needs of journalists on the internet (1999)Social Media for Good
 
Book-2011 Kopetz Real-time systems Design principles for distributed embedded...
Book-2011 Kopetz Real-time systems Design principles for distributed embedded...Book-2011 Kopetz Real-time systems Design principles for distributed embedded...
Book-2011 Kopetz Real-time systems Design principles for distributed embedded...AlfredoLaura2
 
Flavio Felici Dissertation
Flavio Felici DissertationFlavio Felici Dissertation
Flavio Felici DissertationFlavio Felici
 
Flavio Felici Dissertation
Flavio Felici DissertationFlavio Felici Dissertation
Flavio Felici DissertationFlavio Felici
 
Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...
Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...
Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...Vassilis Valavanis
 
Moukalled et-al-fvm-open foam-matlab
Moukalled et-al-fvm-open foam-matlabMoukalled et-al-fvm-open foam-matlab
Moukalled et-al-fvm-open foam-matlabSonatrach
 
PATHS system architecture
PATHS system architecturePATHS system architecture
PATHS system architecturepathsproject
 
masteroppgave_larsbrusletto
masteroppgave_larsbruslettomasteroppgave_larsbrusletto
masteroppgave_larsbruslettoLars Brusletto
 
D3.2. Specification of Presentation Interfaces for The Three Scenarios
D3.2. Specification of Presentation Interfaces for The Three ScenariosD3.2. Specification of Presentation Interfaces for The Three Scenarios
D3.2. Specification of Presentation Interfaces for The Three ScenariosLinkedTV
 
Icts and society
Icts and societyIcts and society
Icts and societyamgpanizo
 
D4.2. User Profile Schema and Profile Capturing
D4.2. User Profile Schema and Profile CapturingD4.2. User Profile Schema and Profile Capturing
D4.2. User Profile Schema and Profile CapturingLinkedTV
 
Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...
Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...
Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...Gravitate Project
 
D3.1 Report on Shape Analysis and Matching and on Semantic Matching
D3.1 Report on Shape Analysis and Matching and on Semantic MatchingD3.1 Report on Shape Analysis and Matching and on Semantic Matching
D3.1 Report on Shape Analysis and Matching and on Semantic MatchingGravitate Project
 
Gravitate d3.1 report on shape analysis and matching and on semantic matching
Gravitate d3.1 report on shape analysis and matching and on semantic matchingGravitate d3.1 report on shape analysis and matching and on semantic matching
Gravitate d3.1 report on shape analysis and matching and on semantic matchingaavgoustinos
 

Similar to Mediabase Ready and First Analysis Report (20)

Usage patterns and information needs of journalists on the internet (1999)
Usage patterns and information needs of journalists on the internet (1999)Usage patterns and information needs of journalists on the internet (1999)
Usage patterns and information needs of journalists on the internet (1999)
 
Book-2011 Kopetz Real-time systems Design principles for distributed embedded...
Book-2011 Kopetz Real-time systems Design principles for distributed embedded...Book-2011 Kopetz Real-time systems Design principles for distributed embedded...
Book-2011 Kopetz Real-time systems Design principles for distributed embedded...
 
main
mainmain
main
 
Flavio Felici Dissertation
Flavio Felici DissertationFlavio Felici Dissertation
Flavio Felici Dissertation
 
Flavio Felici Dissertation
Flavio Felici DissertationFlavio Felici Dissertation
Flavio Felici Dissertation
 
Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...
Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...
Real-Time Vowel Synthesis - A Magnetic Resonator Piano Based Project_by_Vasil...
 
Communication Theory
Communication TheoryCommunication Theory
Communication Theory
 
Moukalled et-al-fvm-open foam-matlab
Moukalled et-al-fvm-open foam-matlabMoukalled et-al-fvm-open foam-matlab
Moukalled et-al-fvm-open foam-matlab
 
PATHS system architecture
PATHS system architecturePATHS system architecture
PATHS system architecture
 
Master's Thesis
Master's ThesisMaster's Thesis
Master's Thesis
 
EMBAThesis_MaSu_Aug2008
EMBAThesis_MaSu_Aug2008EMBAThesis_MaSu_Aug2008
EMBAThesis_MaSu_Aug2008
 
masteroppgave_larsbrusletto
masteroppgave_larsbruslettomasteroppgave_larsbrusletto
masteroppgave_larsbrusletto
 
thesis_online
thesis_onlinethesis_online
thesis_online
 
D3.2. Specification of Presentation Interfaces for The Three Scenarios
D3.2. Specification of Presentation Interfaces for The Three ScenariosD3.2. Specification of Presentation Interfaces for The Three Scenarios
D3.2. Specification of Presentation Interfaces for The Three Scenarios
 
Icts and society
Icts and societyIcts and society
Icts and society
 
thesis
thesisthesis
thesis
 
D4.2. User Profile Schema and Profile Capturing
D4.2. User Profile Schema and Profile CapturingD4.2. User Profile Schema and Profile Capturing
D4.2. User Profile Schema and Profile Capturing
 
Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...
Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...
Gravitate Deliverable 3.1: Report on Shape Analysis and Matching and on Seman...
 
D3.1 Report on Shape Analysis and Matching and on Semantic Matching
D3.1 Report on Shape Analysis and Matching and on Semantic MatchingD3.1 Report on Shape Analysis and Matching and on Semantic Matching
D3.1 Report on Shape Analysis and Matching and on Semantic Matching
 
Gravitate d3.1 report on shape analysis and matching and on semantic matching
Gravitate d3.1 report on shape analysis and matching and on semantic matchingGravitate d3.1 report on shape analysis and matching and on semantic matching
Gravitate d3.1 report on shape analysis and matching and on semantic matching
 

More from Ralf Klamma

An Augmented Reality Framework for Gamified Learning
An Augmented Reality Framework for Gamified LearningAn Augmented Reality Framework for Gamified Learning
An Augmented Reality Framework for Gamified LearningRalf Klamma
 
The Legacy of ROLE - Where are we at the workplace?
The Legacy of ROLE - Where are we at the workplace?The Legacy of ROLE - Where are we at the workplace?
The Legacy of ROLE - Where are we at the workplace?Ralf Klamma
 
Gamification of Community Information Systems
Gamification of Community Information SystemsGamification of Community Information Systems
Gamification of Community Information SystemsRalf Klamma
 
The Legacy and the Future of Research Networks in Technology-Enhanced Learning
The Legacy and the Future of Research Networks in Technology-Enhanced LearningThe Legacy and the Future of Research Networks in Technology-Enhanced Learning
The Legacy and the Future of Research Networks in Technology-Enhanced LearningRalf Klamma
 
DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...
DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...
DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...Ralf Klamma
 
Learning Analytics: Trends and Issues of the Empirical Research of the Years ...
Learning Analytics: Trends and Issues of the Empirical Research of the Years ...Learning Analytics: Trends and Issues of the Empirical Research of the Years ...
Learning Analytics: Trends and Issues of the Empirical Research of the Years ...Ralf Klamma
 
A Short Swim through the Personal Learning Pool
A Short Swim through the Personal Learning PoolA Short Swim through the Personal Learning Pool
A Short Swim through the Personal Learning PoolRalf Klamma
 
Scaling up digital learning support for smart workforce development in cluste...
Scaling up digital learning support for smart workforce development in cluste...Scaling up digital learning support for smart workforce development in cluste...
Scaling up digital learning support for smart workforce development in cluste...Ralf Klamma
 
Scaling Community Information Systems
Scaling Community Information SystemsScaling Community Information Systems
Scaling Community Information SystemsRalf Klamma
 
Technical Challenges for Realizing Learning Analytics
Technical Challenges for Realizing Learning AnalyticsTechnical Challenges for Realizing Learning Analytics
Technical Challenges for Realizing Learning AnalyticsRalf Klamma
 
Technology-Enhanced Learning at the Workplace – From islands of automation to...
Technology-Enhanced Learning at the Workplace – From islands of automation to...Technology-Enhanced Learning at the Workplace – From islands of automation to...
Technology-Enhanced Learning at the Workplace – From islands of automation to...Ralf Klamma
 
ACIS Annual Report 2014
ACIS Annual Report 2014ACIS Annual Report 2014
ACIS Annual Report 2014Ralf Klamma
 
Blueprint for Software Engineering in Technology Enhanced Learning Projects
Blueprint for Software Engineering in Technology Enhanced Learning ProjectsBlueprint for Software Engineering in Technology Enhanced Learning Projects
Blueprint for Software Engineering in Technology Enhanced Learning ProjectsRalf Klamma
 
Navigation Support in Evolving Communities by a Web-based Dashboard
Navigation Support in Evolving Communities by a Web-based DashboardNavigation Support in Evolving Communities by a Web-based Dashboard
Navigation Support in Evolving Communities by a Web-based DashboardRalf Klamma
 
Community Learning Analytics – A New Research Field in TEL
Community Learning Analytics – A New Research Field in TELCommunity Learning Analytics – A New Research Field in TEL
Community Learning Analytics – A New Research Field in TELRalf Klamma
 
Do Mechanical Turks Dream of Big Data?
Do Mechanical Turks Dream of Big Data?Do Mechanical Turks Dream of Big Data?
Do Mechanical Turks Dream of Big Data?Ralf Klamma
 
Advanced Community Information Systems Group (ACIS) Annual Report 2013
Advanced Community Information Systems Group (ACIS) Annual Report 2013Advanced Community Information Systems Group (ACIS) Annual Report 2013
Advanced Community Information Systems Group (ACIS) Annual Report 2013Ralf Klamma
 
Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...
Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...
Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...Ralf Klamma
 
Keynote Learning Layers Developer Camp 2013
Keynote Learning Layers Developer Camp 2013Keynote Learning Layers Developer Camp 2013
Keynote Learning Layers Developer Camp 2013Ralf Klamma
 
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web Ralf Klamma
 

More from Ralf Klamma (20)

An Augmented Reality Framework for Gamified Learning
An Augmented Reality Framework for Gamified LearningAn Augmented Reality Framework for Gamified Learning
An Augmented Reality Framework for Gamified Learning
 
The Legacy of ROLE - Where are we at the workplace?
The Legacy of ROLE - Where are we at the workplace?The Legacy of ROLE - Where are we at the workplace?
The Legacy of ROLE - Where are we at the workplace?
 
Gamification of Community Information Systems
Gamification of Community Information SystemsGamification of Community Information Systems
Gamification of Community Information Systems
 
The Legacy and the Future of Research Networks in Technology-Enhanced Learning
The Legacy and the Future of Research Networks in Technology-Enhanced LearningThe Legacy and the Future of Research Networks in Technology-Enhanced Learning
The Legacy and the Future of Research Networks in Technology-Enhanced Learning
 
DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...
DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...
DevOpsUse for Large-Scale Social Requirements Engineering @ SIG WELL - EC-TEL...
 
Learning Analytics: Trends and Issues of the Empirical Research of the Years ...
Learning Analytics: Trends and Issues of the Empirical Research of the Years ...Learning Analytics: Trends and Issues of the Empirical Research of the Years ...
Learning Analytics: Trends and Issues of the Empirical Research of the Years ...
 
A Short Swim through the Personal Learning Pool
A Short Swim through the Personal Learning PoolA Short Swim through the Personal Learning Pool
A Short Swim through the Personal Learning Pool
 
Scaling up digital learning support for smart workforce development in cluste...
Scaling up digital learning support for smart workforce development in cluste...Scaling up digital learning support for smart workforce development in cluste...
Scaling up digital learning support for smart workforce development in cluste...
 
Scaling Community Information Systems
Scaling Community Information SystemsScaling Community Information Systems
Scaling Community Information Systems
 
Technical Challenges for Realizing Learning Analytics
Technical Challenges for Realizing Learning AnalyticsTechnical Challenges for Realizing Learning Analytics
Technical Challenges for Realizing Learning Analytics
 
Technology-Enhanced Learning at the Workplace – From islands of automation to...
Technology-Enhanced Learning at the Workplace – From islands of automation to...Technology-Enhanced Learning at the Workplace – From islands of automation to...
Technology-Enhanced Learning at the Workplace – From islands of automation to...
 
ACIS Annual Report 2014
ACIS Annual Report 2014ACIS Annual Report 2014
ACIS Annual Report 2014
 
Blueprint for Software Engineering in Technology Enhanced Learning Projects
Blueprint for Software Engineering in Technology Enhanced Learning ProjectsBlueprint for Software Engineering in Technology Enhanced Learning Projects
Blueprint for Software Engineering in Technology Enhanced Learning Projects
 
Navigation Support in Evolving Communities by a Web-based Dashboard
Navigation Support in Evolving Communities by a Web-based DashboardNavigation Support in Evolving Communities by a Web-based Dashboard
Navigation Support in Evolving Communities by a Web-based Dashboard
 
Community Learning Analytics – A New Research Field in TEL
Community Learning Analytics – A New Research Field in TELCommunity Learning Analytics – A New Research Field in TEL
Community Learning Analytics – A New Research Field in TEL
 
Do Mechanical Turks Dream of Big Data?
Do Mechanical Turks Dream of Big Data?Do Mechanical Turks Dream of Big Data?
Do Mechanical Turks Dream of Big Data?
 
Advanced Community Information Systems Group (ACIS) Annual Report 2013
Advanced Community Information Systems Group (ACIS) Annual Report 2013Advanced Community Information Systems Group (ACIS) Annual Report 2013
Advanced Community Information Systems Group (ACIS) Annual Report 2013
 
Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...
Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...
Community Learning Analytics - Challenges and Opportunities - ICWL 2013 Invit...
 
Keynote Learning Layers Developer Camp 2013
Keynote Learning Layers Developer Camp 2013Keynote Learning Layers Developer Camp 2013
Keynote Learning Layers Developer Camp 2013
 
Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web Supporting Professional Communities in the Next Web
Supporting Professional Communities in the Next Web
 

Recently uploaded

Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...DianaGray10
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
 
IT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced ComputingIT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced ComputingMAGNIntelligence
 
Patch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 updatePatch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 updateadam112203
 
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptxEmil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptxNeo4j
 
Technical SEO for Improved Accessibility WTS FEST
Technical SEO for Improved Accessibility  WTS FESTTechnical SEO for Improved Accessibility  WTS FEST
Technical SEO for Improved Accessibility WTS FESTBillieHyde
 
Webinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - Tech
Webinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - TechWebinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - Tech
Webinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - TechProduct School
 
UiPath Studio Web workshop series - Day 2
UiPath Studio Web workshop series - Day 2UiPath Studio Web workshop series - Day 2
UiPath Studio Web workshop series - Day 2DianaGray10
 
My key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAIMy key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAIVijayananda Mohire
 
Scenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenariosScenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenariosErol GIRAUDY
 
20140402 - Smart house demo kit
20140402 - Smart house demo kit20140402 - Smart house demo kit
20140402 - Smart house demo kitJamie (Taka) Wang
 
Top 10 Squarespace Development Companies
Top 10 Squarespace Development CompaniesTop 10 Squarespace Development Companies
Top 10 Squarespace Development CompaniesTopCSSGallery
 
Novo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNovo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNeo4j
 
Stobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through TokenizationStobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through TokenizationStobox
 
Introduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its applicationIntroduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its applicationKnoldus Inc.
 
The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)codyslingerland1
 
3 Pitfalls Everyone Should Avoid with Cloud Data
3 Pitfalls Everyone Should Avoid with Cloud Data3 Pitfalls Everyone Should Avoid with Cloud Data
3 Pitfalls Everyone Should Avoid with Cloud DataEric D. Schabell
 
Automation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projectsAutomation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projectsDianaGray10
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)IES VE
 
How to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptxHow to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptxKaustubhBhavsar6
 

Recently uploaded (20)

Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...Explore the UiPath Community and ways you can benefit on your journey to auto...
Explore the UiPath Community and ways you can benefit on your journey to auto...
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
IT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced ComputingIT Service Management (ITSM) Best Practices for Advanced Computing
IT Service Management (ITSM) Best Practices for Advanced Computing
 
Patch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 updatePatch notes explaining DISARM Version 1.4 update
Patch notes explaining DISARM Version 1.4 update
 
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptxEmil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
Emil Eifrem at GraphSummit Copenhagen 2024 - The Art of the Possible.pptx
 
Technical SEO for Improved Accessibility WTS FEST
Technical SEO for Improved Accessibility  WTS FESTTechnical SEO for Improved Accessibility  WTS FEST
Technical SEO for Improved Accessibility WTS FEST
 
Webinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - Tech
Webinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - TechWebinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - Tech
Webinar: The Art of Prioritizing Your Product Roadmap by AWS Sr PM - Tech
 
UiPath Studio Web workshop series - Day 2
UiPath Studio Web workshop series - Day 2UiPath Studio Web workshop series - Day 2
UiPath Studio Web workshop series - Day 2
 
My key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAIMy key hands-on projects in Quantum, and QAI
My key hands-on projects in Quantum, and QAI
 
Scenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenariosScenario Library et REX Discover industry- and role- based scenarios
Scenario Library et REX Discover industry- and role- based scenarios
 
20140402 - Smart house demo kit
20140402 - Smart house demo kit20140402 - Smart house demo kit
20140402 - Smart house demo kit
 
Top 10 Squarespace Development Companies
Top 10 Squarespace Development CompaniesTop 10 Squarespace Development Companies
Top 10 Squarespace Development Companies
 
Novo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4jNovo Nordisk's journey in developing an open-source application on Neo4j
Novo Nordisk's journey in developing an open-source application on Neo4j
 
Stobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through TokenizationStobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
Stobox 4: Revolutionizing Investment in Real-World Assets Through Tokenization
 
Introduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its applicationIntroduction to RAG (Retrieval Augmented Generation) and its application
Introduction to RAG (Retrieval Augmented Generation) and its application
 
The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)
 
3 Pitfalls Everyone Should Avoid with Cloud Data
3 Pitfalls Everyone Should Avoid with Cloud Data3 Pitfalls Everyone Should Avoid with Cloud Data
3 Pitfalls Everyone Should Avoid with Cloud Data
 
Automation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projectsAutomation Ops Series: Session 2 - Governance for UiPath projects
Automation Ops Series: Session 2 - Governance for UiPath projects
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)
 
How to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptxHow to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptx
 

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 –