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Coordination and Support Action        European Commission Seventh Framework Project (IST-257822)Mediabase Ready and First...
Amendment History Version          Date                Editor                     Description/Comments      1.0   30 Sept....
Contents1     Introduction ..................................................................................................
5.3.2            TEL Blog Clusters ..........................................................................................
FiguresFigure 1: Concept map underlying of the TEL-Map Mediabase metamodel. ............................................. ...
TablesTable 1: Uses of social network analysis and topic mining in the TEL-Map Mediabase. ........................ 10Table...
1 IntroductionThe European Framework Programmes (FP) for Research and Technological Development are a keypillar of the Eur...
•   Continuous analysis: As indicated in the title, this deliverable was conceived to present a         first analysis rep...
•   Community as a sub-network of the whole network, representing trustful relations among its        members;    •   Proc...
TEL Social Media                                       Blogosphere                                                 part of...
Web Data Sources         European Community              DBLP              Publisher          Information Pages           ...
users to provide links to their favorite TEL-related feeds, either RSS or Atom feeds. This          module offers several ...
•   CSV Data Exporter: Includes a set of scripts that export data contained in the databases into        CSV format (CSV =...
To enable the calculation of SNA metrics for the data in TEL-Map Mediabase, the entities stored in theMediabase need to be...
time, when new nodes join the graph, to see whether these new nodes inter-connect tightly        with the existing ones.  ...
•   Building on the topic mining approach of selected TEL conferences in D4.1, we filtered the        results for sources ...
•   Most important TEL conferences and journals: identifying the most important outlets for       publishing TEL research ...
•   Most authoritative web sources referenced in TEL blogs: in addition to citing sources in the        blogosphere, blogg...
Information that was not available in CORDIS includes the geographical coordinates of projectmembers. These locations were...
Looking at eContentplus in comparison to FP6 and FP7, there is a strong emphasis on content andmetadata, while still inclu...
•   Interactive visualization of geographical distribution of project consortia to complement the        social network me...
The visualization of project connections in Figure 5 exposes one node that could be labeled as thecurrent “epicenter” of T...
FP7                                                                                                                       ...
•    Cluster C3 includes projects related development, content, competence, tools and testing. In           this cluster t...
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
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Mediabase Ready and First Analysis Report

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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

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  1. 1. Coordination and Support Action European Commission Seventh Framework Project (IST-257822)Mediabase Ready and First Analysis ReportDeliverable D4.3Editor: Michael Derntl (RWTH Aachen University)Contributors: Adam Cooper, Manh Cuong Pham, Ralf Klamma, Dominik RenzelDissemination level: PublicDelivery date: 2011-09-30Work Package WP4: Weak Signals Analysis – Emerging RealityDissemination Level PublicStatus Version 1.0 — FinalDate September 30, 2011
  2. 2. Amendment History Version Date Editor Description/Comments 1.0 30 Sept. 2011 Michael Derntl Final version Contributors Name Institution RoleMichael Derntl RWTH Aachen University Editor/AuthorAdam Cooper University of Bolton (CETIS) AuthorRalf Klamma RWTH Aachen University AuthorManh Cuong Pham RWTH Aachen University AuthorDominik Renzel RWTH Aachen University AuthorPaul Lefrere The Open University (OU) ReviewerLampros Stergioulas Brunel University ReviewerChristian Voigt Zentrum für Soziale Innovation (ZSI) ReviewerDeliverable 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. 3. Contents1 Introduction .......................................................................................................... 12 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 ..................................................................................................................... 103 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.........................................................................................................284 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 .......................................................................................................... 435 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. 4. 5.3.2 TEL Blog Clusters .............................................................................................................. 49 5.3.3 Bursts ................................................................................................................................. 516 Embeddable Interactive Visualizations and Queries ............................................. 527 Key Findings for Weak Signals ............................................................................. 55 7.1 TEL Projects................................................................................................................................. 55 7.2 TEL Papers................................................................................................................................... 56 7.3 TEL Social Web............................................................................................................................ 578 Conclusion........................................................................................................... 57References ................................................................................................................. 58Appendix A: TEL Projects — Timeline ........................................................................ 60Appendix B: TEL Projects — SNA Metrics .................................................................... 61
  5. 5. FiguresFigure 1: Concept map underlying of the TEL-Map Mediabase metamodel. ............................................. 4Figure 2: TEL-Map Mediabase components overview model. .................................................................... 5Figure 3: Data model of TEL projects. ........................................................................................................ 12Figure 4: Word clouds of project descriptions. .......................................................................................... 14Figure 5: FP7 TEL projects graph visualization. ........................................................................................ 15Figure 6: Project consortium progression between FP6, FP7, and eContentplus projects. .................... 17Figure 7: Visualization of the FP7 collaboration graph.............................................................................. 19Figure 8: Center region cut-out of the FP7 collaboration graph. ..............................................................20Figure 9: Word cloud of the 20 word stems with highest frequency in the FP7 project descriptions .... 21Figure 10: Partner collaborations spanning FP6, FP7, and eContentplus projects. ................................ 22Figure 11: Local clustering of organizations plotted against (a) PageRank and (b) degree. .................... 24Figure 12: Overall development of collaboration network since 2004. .................................................... 26Figure 13: Impact of newly launched projects the collaboration network................................................ 26Figure 14: Impact of organizations on collaboration. ................................................................................ 27Figure 15: Development of the ratio of projects coordinated by novice organizations ............................ 27Figure 16: Google Map overlay with organizations involved in TEL projects. .........................................28Figure 17: Data model for TEL papers and events. ....................................................................................30Figure 18: Word cloud of most frequent terms in TEL conference paper titles. ...................................... 31Figure 19: Development model for conference communities.................................................................... 32Figure 20: Cumulative annual (co-)author figures of selected TEL conferences over the last 10 years. 33Figure 21: Co-authorship network visualization for the TEL conferences. .............................................. 33Figure 22: Co-authorship network measures of five conferences in TEL................................................. 35Figure 23: Most frequent terms in papers of top TEL authors in 2010. ................................................... 37Figure 24: Complete co-authorship network in the core TEL venues. ..................................................... 37Figure 25: Co-authorship network of the “inner circle” of authors in the core TEL venues. ..................38Figure 26: Citation network measures of five conferences in TEL. .......................................................... 44Figure 27: Relational model of the TEL blogosphere. ............................................................................... 45Figure 28: Number of blogs added to and blog entries indexed in the TEL-Map Mediabase. ................ 46Figure 29: TEL blogs link network visualization, excluding self-references. ........................................... 47Figure 30: Top 100 word stems appearing in 2011 blog entries of the top 20 blogs................................ 49Figure 31: Colored TEL blog clusters. .........................................................................................................50Figure 32: Bursty terms appearing only in 2011. ....................................................................................... 51Figure 33: Bursty terms with rising frequency over the last three years. ................................................. 52Figure 34: Visualization of the same SQL query as a table (left) and as a graph (right). ........................ 53Figure 35: SQL query visualization as an annotated timeline. .................................................................. 54
  6. 6. TablesTable 1: Uses of social network analysis and topic mining in the TEL-Map Mediabase. ........................ 10Table 2: Overview of the 77 TEL Projects in the TEL-Map Mediabase..................................................... 13Table 3: TEL project clusters in FP6, FP7, and eContentplus (ECP) and the word clouds of their projectdescriptions. ................................................................................................................................................. 18Table 4: Top 30 organizations involved in TEL projects by PageRank. The numbers in square bracketsnext to the values represent the rank of that value among all 604 organizations.................................... 23Table 5: Strongest partnership bonds over all TEL projects in FP6, FP7 and eContentplus. ................. 25Table 6: Selection of conferences relevant to the TEL community. .......................................................... 31Table 7: Fifteen most prolific authors at conferences and journals with a broad TEL scope. Namesmarked with an asterisk (*) indicate authors currently based in Europe. ................................................ 36Table 8: Top 15 TEL authors by betweenness centrality. .......................................................................... 39Table 9: Top ten co-author pairs in core TEL venues. ............................................................................... 39Table 10: Betweenness centrality of authors of papers identified in D4.1. ...............................................40Table 11: Summary of structural-semantic analysis: themes and matching papers. ............................... 41Table 12: Top twenty blog sources by PageRank. The number in square bracket indicates the blog’soverall rank for the respective metric. .......................................................................................................48Table 13: Clusters of TEL blogs indexed in Mediabase.............................................................................. 49
  7. 7. 1 IntroductionThe European Framework Programmes (FP) for Research and Technological Development are a keypillar of the European research area and act as the primary vehicle for the European Union to createand sustain growth, employment and global competitiveness [3]. FPs are complex frameworksdefining the specific research programmes and challenges to be tackled over a seven-year period with amulti-billion Euro budget. In FP7, the Cooperation programme, which also hosts the TechnologyEnhanced Learning (TEL) thematic area, received the largest share of the total FP7 funds. For thetwenty-six partly completed and partly running TEL projects in FP7 the European Commission hasprovided or will provide a total funding of more than one hundred million Euro. People andorganizations with a stake in TEL research and development are likely to be interested in knowingwhere this enormous amount of money went and what impact it has generated and is generating onthe TEL landscape. First and foremost, the European Commission itself is interested in what impactthe spending has generated over the years. In addition, there are many organizations and individualsin Europe that have a stake in TEL, e.g. technology providers, technology adopters, and highereducation 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 networkanalysis and visualization as well as topic mining.This deliverable reports on social network analysis and topic mining work performed in WP4, “WeakSignal Analysis—Emerging Reality,” to support weak signal analysis and the mapping of the currentTEL landscape. To achieve this, the deliverable outlines the conceptual foundations of the TEL-MapMediabase, 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 thefollowing 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. 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 usto propel the convergence of different analytical methods. For instance, this can be achieved in WP4 byfeeding results from one analytical method into another one in order to cross-validate and enrichexisting findings, but it also needs to happen between WPs, e.g. by informing WP5’s gap analysis andWP3’s scenario building. Gap analysis aims to explore why some technologies seem to be much moreprominent in TEL research than in TEL practice (e.g. consider the uptake of 3D worlds) and othertechnologies 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-depthanalysis of specific technologies by considering the spread of awareness of that technology acrossvarious communities as well as the use of synonyms referring to the same set of issues but underdifferent labels. Likewise, scenario building events (WP3) can be informed through weak signals asthey are early indicators of change that have the potential to alter the future of TEL adopters and TELproviders. 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 thesubsequent sections presents first analyses performed and results obtained in the TEL-Map Mediabasesources, i.e. TEL projects in Section 3, TEL publications in Section 4, and TEL blogosphere inSection 5. An embeddable, widget-based toolkit for enabling stakeholders to query and visuallyinteract with the data contained in the TEL-Map Mediabase is presented in Section 6. Section 7 drawskey findings from the analysis for weak signals collection from the core analysis sections, and Section 8wraps up the deliverable with a discussion of limitations and an outlook on upcoming work in WP4.2 The TEL-Map MediabaseTEL-Map Mediabase is an evolution of the established PROLEARN Mediabase. In this section we firstdescribe 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 coreeffort was the creation and maintenance of a media base for TEL in Europe, providing different targetaudiences like scientists, policy makers, and communities of practice with digital information obtainedfrom mailing lists, newsletters, blogs, RSS/Atom feeds, websites, and so forth [10]. In addition tocollecting large amounts of data, one key objective was the provision of easy-to-use end-user tools forextracting and presenting relevant information contained in the Mediabase, e.g. for cross-media socialnetwork analysis, self-observation and self-modeling of communities [18], collaborativeadministration and retrieval of media artifacts, etc. The key concepts in the metamodel of thePROLEARN Mediabase are (cf. [10], p. 248-9):1 http://www.prolearn-project.org –2–
  9. 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 TELblogosphere, 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 EuropeanTEL projects as well as publications in TEL-related conferences and journals.2.1 Conceptual Model of the TEL-Map MediabaseTEL-Map aims to empower stakeholders to find relevant projects and useful outputs as well as newcollaborators for TEL projects; it also aims at giving a rich overview of different types of actorsinvolved in the TEL domain (see DoW, p. 17-18). WP4 in particular focuses on analyses andvisualizations from social media items gathered and automatically crawled from relevant sources. Torealize these ambitious objectives, we have enhanced and extended the metamodel and the content ofthe existing PROLEARN Mediabase. This enhanced TEL-Map Mediabase additionally includesinformation on TEL projects and participants funded by the European Commission, as well as authorsand 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 indetail 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 wellas 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, thereare several concepts and their links in Figure 1 which are currently not or only partly represented inthe 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. 10. TEL Social Media Blogosphere part of has Blog Comment has TEL Papers refs refs published at Entry Publication Paper Venue refs 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 OverviewThe components of TEL-Map Mediabase are conceptually arranged in different groups or layers (seeFigure 2): the information to be used for weak signal analysis in the context of Mediabase is containedin 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 theircomponents are described in detail below.Importers. This layer includes services and processes that obtain relevant data from web sources andtransform 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. 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 registered2 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. 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 databaseschemas, 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 nativelyvia clients that connect to the database(s) using the database drivers, or via exporters. The exportersease the process of obtaining data for analysis by providing a set of predefined export formats.3 http://learningfrontiers.eu/?q=story/tel-project-landscape4 http://learningfrontiers.eu/?q=project_space –6–
  13. 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 theLearning Frontiers portal. While Figure 2 includes many example applications, the following list onlyfocuses 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 ApproachThis deliverable reports on first results of using social network analysis (SNA) and topic mining on thedata stored in the TEL-Map Mediabase. SNA contributes to the structural analysis of actors and theirrelationships and topic mining contributes to the semantic analysis of actors and relationshipsbetween actors. The combination of SNA and topic mining thus enables the structural-semanticanalysis of TEL sources.Social Network Analysis (SNA) is one of the work threads pursued in WP4 of TEL-Map to detectweak signals [23, 6] indicating future directions and insight into collaboration and communicationnetworks in different types of media and settings. SNA constitutes a rather new field of research andits application to digital libraries is very promising in terms of knowledge discovery [19, 20]. SNAdefines techniques used to compute metrics of different actors in a social network. These metricstypically represent the importance of actors within their network or neighborhood, e.g. their centrality,connectedness, etc. –7–
  14. 14. To enable the calculation of SNA metrics for the data in TEL-Map Mediabase, the entities stored in theMediabase 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 oneanother [2]. Any “actor” entity in the Mediabase can be modeled as a vertex, if it is connected to otheractors through any relationship of interest (modeled as edges) that can be obtained from theMediabase 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 socialnetwork 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 typicallyallows 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 toalign the vertices in a predefined shape (e.g. circular, organic, hierarchical, etc.). Graph visualizationgenerally provides a holistic, condensed view on the overall network.(2) Data querying: Interacting with graph visualizations will typically spawn more specific questionsand exploratory tasks [5]. Some of these explorations cannot be performed using the visualizationalone, e.g. the number of shortest paths through the network that lead through a particular node. Suchresults can be obtained by enabling querying into the graph data. We developed a web-based toolkit forenabling this (see Section 6).(3) SNA Metrics: SNA allows the computation of different metrics for the graph, its nodes and itsedges. 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. 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 aredescribed by word distributions and sometimes also time distributions (cf. [24]). In the context of thisdeliverable we use a simplified approach to topic mining that mainly focuses on term stems and theirfrequency 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 firststructural-semantic analyses reported in this deliverable, we focused on a “big picture” approach tocomplementing social network metrics with content analysis for different sources and actors in theTEL-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. 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 QuestionsThe combined results of SNA and topic mining can give rich insight into the available data and be usedto detect and explore potential signals (both strong and weak ones) in the data. The matrix in Table 1gives a brief overview of questions addressed by using SNA and topic mining on the different datasources in the TEL-Map Mediabase. Table 1: Uses of social network analysis and topic mining in the TEL-Map Mediabase. Social Network Analysis Topic MiningTEL 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 timeTEL 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 entriesIn the following, we elaborate more on the objectives and potential signals that can be identified bytackling 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. 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. 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 LandscapeThere currently exists no readily available, structured data set on TEL projects funded in recentprogrammes, with the exception of HTML factsheets offered on the web by the European Commissionas well as a load of project websites and deliverables produced by the project consortia. Turninginformation overload into an opportunity is the driving vision of visual analytics [7], and this sectionaims to achieve this vision in the context of TEL projects funded under FP6, FP7 and eContentplusprogrammes by applying SNA and information visualization methods on projects and collaborationswithin project consortia.3.1 Data SetData Model. The database used for the analyses in this paper was scraped from publicly availableproject information pages on CORDIS [4], i.e. the Community Research and Development InformationService offered by the European Commission, and other European Community project informationpages. The scraped data was captured according to the data model presented in Figure 3 and fed into arelational database. The data scraping was focused on TEL-related projects funded under FP6, FP7and 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. 19. Information that was not available in CORDIS includes the geographical coordinates of projectmembers. These locations were semi-automatically obtained by invoking the Google Maps API andYahoo Maps API using the partner names and countries provided in the factsheets. Since some of thepartner names produced ambiguous geographical results, the geographical coordinates will not becorrect for some institutions. Also, the spelling of organization names and country names wasinconsistent in the project fact sheets in many cases; this was corrected manually (which still does notguarantee 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 allorganizations with similar rebrandings—are represented as separate entities. Likewise, organizationalmergers 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 followinganalyses, 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: 77a … Technology-enhanced learning and access to cultural heritageb … Technology-Enhanced Learningc … Strengthening the Integration of the ICT research effort in an Enlarged Europed … Digital libraries and technology-enhanced learningTopics and topic shifts. To give an indication of the topic focuses in these projects, Figure 4presents for FP6, FP7, and eContentplus a word cloud of the funded projects’ descriptions. It revealsan interesting difference between FP6 and FP7 projects. In FP6, we find many meta-concepts in thedescriptions like project, development, research, European, while descriptions of TEL projects in FP7expose 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 beginningto 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. 20. Looking at eContentplus in comparison to FP6 and FP7, there is a strong emphasis on content andmetadata, while still including heavy use of educational and learning as terms. Content is a termfound also in FP6 with some frequency, but it is missing in the top term list of FP7, probably showingthat 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 NetworksA TEL project—like any other collaborative type of project—can be modeled as a social network wherea number of partner organizations collaborate under coordination of a coordinating organization. Asocial network is modeled as a graph = , with being the set of vertices (or nodes) and beingthe 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. Functionrepresents the membership of any organization ∈ in the consortium of any project ∈ and isdefined as follows: , if ∈ participated or particiaptes in ∈ ∶ → , otherwise . The data model and these formal foundations enable powerful analyses and visualizations includingthe project network, the organizational partnership network, temporal relationships between projectconsortia, and the geographical mapping of organizations involved in projects. A selection of theseanalyses 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. 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 ProgressionThe project consortium progression graph =( , contains projects and their relationships witheach other based on overlapping consortia. The graph will show projects as nodes and an edge betweentwo 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 edgepoints from the project which started earlier to the project which started later.3.3.1 FP7 ProjectsA visualization of for the 26 FP7 projects is shown in Figure 5. The size of each node in thisvisualization is proportional to the betweenness centrality [2] of that node, and the weight of the edgewas determined by the number of partners that overlap between two project consortia. Thebetweenness centrality measure is an effective means of exposing nodes that act as “bridges” betweenotherwise distant nodes (or groups of nodes) by computing for each node the share of all shortestpaths 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. 22. The visualization of project connections in Figure 5 exposes one node that could be labeled as thecurrent “epicenter” of TEL projects in FP7. This node represents GALA, the network of excellence onserious 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 organizationsin its consortium which were already part of previous project consortia. Other projects that carried onmultiple consortium members to the GALA consortium are TARGET, GRAPPLE, and STELLAR.Another strong, currently running project is ROLE, which is a harbor for project consortiumpartnerships from previous projects, and also has overlaps with succeeding project consortia. If we hadcomputed 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 computationwould, however, statistically favor projects that have started in the middle between the begin date ofFP7 and the current date, since in this time window projects are more likely to have outgoingconsortium connections in addition the incoming ones.3.3.2 All TEL Projects – FP6, FP7, and eContentplusA graph of all TEL projects funded in FP6, FP7, and eContentplus is given in Figure 6. The graphincludes all 77 projects and a total of 712 connections between those projects. KALEIDOSCOPE is byfar the largest node, which can be attributed to the fact that this project had an extremely largeconsortium of 83 partner organizations, which is more than five times the typical consortium size. It isalso evident in this visualization that in addition to strong ties between FP6 and FP7 projects, theeContentplus projects have very strong connections to both FP6 and FP7. This can probably beexplained by the fact that eContentplus filled a “funding gap” in 2007 when FP6 funding was stallingfollowing the last FP6 projects launched in 2006, while FP7 funding was kicked off with the first TELprojects starting in 2008. In fact, in 2007 only eContentplus projects were launched with EC fundingin 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 andFP7 engaged in e-content focused R&D projects, could be interpreted as evidence for a “researchfollows money” attitude of researchers involved in TEL. That is, if there had not been funding fromeContentplus, organizations would likely have looked for funding opportunities in TEL-relatedprogrammes 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 inAppendix B.6 See http://learningfrontiers.eu/?q=story/tel-project-landscape&proj=GALA and http://www.learningfrontiers.eu/?q=tel_project/GALA – 16 –
  23. 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 ClustersThe project consortium progression graph was subjected to cluster analysis using the Louvainmethod described in [1]. This method first divides the nodes into local clusters, and then collapseseach clusters’ nodes into a single node. These two steps are applied repeatedly until the final set ofclusters 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. 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 fundingprogramme (a) did not disrupt collaboration structures in TEL and (b) was definitely relevant for atopic focus on educational content. Moreover, projects of all funding schemes are represented in allclusters, 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-BridgeC3 [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.COMC5 [FP6], ITEC [FP7], KeyToNature [ECP], MELT [ECP], MGBL [FP6], TERENCE [FP7] – 18 –

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