The European TEL Projects Community from a Social Network Analysis Perspective
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The European TEL Projects Community from a Social Network Analysis Perspective

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Presented at EC-TEL 2012, 7th European Conference on Technology Enhanced Learning

Presented at EC-TEL 2012, 7th European Conference on Technology Enhanced Learning
September 20, 2012
Saarbrücken, Germany

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The European TEL Projects Community from a Social Network Analysis Perspective The European TEL Projects Community from a Social Network Analysis Perspective Presentation Transcript

  • 7th European Conference on Technology Enhanced Learning (EC-TEL 2012) September 18-21, 2012 Saarbrücken, Germany The European TEL Projects Community from a Social Network Analysis Perspective Michael Derntl and Ralf Klamma RWTH Aachen University Advanced Community Information Systems (ACIS) Aachen, Germany derntl@dbis.rwth-aachen.deLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 1 These slides are licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
  • Motivation Collaborative projects are key in the R&D value chain – Cost a lot of (tax payers’) money – Drive research agenda and scientific community building – scientific events (e.g. EC-TEL, summer school) – researcher mobility, seed projects, R&D teams, associate partnerships, etc. – conducting, reporting, and disseminating research – product development and knowledge transfer Stakeholders have an interest in the collaboration structures of their scientific communityLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke – Key organizations, key projects, trends 2
  • Research Objectives Identify characteristics of the social network of funded R&D collaborations – Organizational collaboration – Project relationships – Central organizations and projects Analyze impact of projects on the collaboration landscape – Conceive impact measure – Find network parameters that may indicate impactLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 3 View slide
  • Related Work Several papers on collaboration networks in FP1-6 [2, 3, 4] with both one-mode and two-node networks Community detection in collaboration networks [6, 7, 8], e.g. location, topics, org. type Analysis of multimodal networks of NoEs (e.g. in STELLAR) [1] Findings – complex scale-free networks; small diameter, high clusteringLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke – “oligarchic core” of organizations [5] 4 View slide
  • Data set: Project timeline 9 # Started Projects 8 7 6 5 4 3 2 1 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 eTEN (39) – eLearning FP6 (32) – TEL eContentplus (19) – Educ.Lehrstuhl Informatik 5(Information Systems) FP7 (26) – TEL Prof. Dr. M. Jarke 5
  • Data set: Project timeline 116 projects 9 350 # Started Projects EC Funding (Million Euro) 829 organizations 8 300 17progamme: 81% 250 26progammes: 14% 5 200 3 programmes: 4% 4 150 All programmes: 1% -- IMC, Open U, 3 WU Wien, KU Leuven, U Hannover, 100 U2Duisburg-Essen, Giunti 50 1 0 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 eTEN (39) – eLearning FP6 (32) – TEL eContentplus (19) – Educ.Lehrstuhl Informatik 5(Information Systems) FP7 (26) – TEL Prof. Dr. M. Jarke 6
  • Projects as Social Networks Projects Organizations [4] Project consortium progression – Nodes: Projects ROLE IMC, RWTH, OU, ZSI – Edges: Overlap of consortia TEL-Map (directed, weighted) Organizational collaboration The Open STELLAR, EUROGENE, – Nodes: Organizations University ROLE, PROLEARN, iCOPER, ASPECT – Edges: Collaboration in multiple KULehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke projects (undirected, weighted) Leuven 7
  • Consortium Progression – Project Network Edge between project P1 and P2 – P2 started at least t time units after P1 – At least k overlapping partners in the consortia – Edge direction: P1 P2 – Edge weight: function of overlap Thresholds that filter for continued collaboration in successive projects? k=2 t = 3 monthsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 8
  • Consortium Progression k = 2, m = 3 months Nodes 85 Edges 257 Diameter 4 Clustering coeff. 0.2 Avg. degree 6.05 Avg. weighted degree 16.9 Avg. path length 1.78 Node size proportional to weighted degree Node color represents cluster [10]Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 9
  • Consortium Progression k = 2, m = 3 months Nodes 85 Edges 257 Diameter 4 Clustering coeff. 0.2 Avg. degree 6.05 Avg. weighted degree 16.9 Avg. path length 1.78 Node size proportional to weighted degree Node color represents cluster [10]Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 10
  • Project Impact on the Landscape Measure impact of project consortium members on sustaining and shaping the social TEL project ties after the project start relative to opportunity. , ∩ Impact ∙ Cumulative fraction , of successor projects ∈ filled up with ps Successor projects relative to opportunity members , projects starting t time units after p and having at least k partners overlap with p all potential successor projects of p after t time unitsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke consortium members of p 11
  • Top 15 Projects by Impact Filters • Started at least 1y before most recent project batch (10/2010) • Top 15 6 FP6, 3 FP7, 3 ECP, 2 eTEN Top instruments: 6 STREP, 3 NoE, 2 BPNLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 12
  • Correlations Impact correlates positively with Pearson correlation *p < .1 **p < .05 ***p < .01 – Funding**, Consortium size*** – Betweenness centrality*** , (Weighted) in-degree** by size* No correlation with PageRank, authority, hub, closeness centrality, clustering coefficient Promising (running or ended in last 12 months): Funding m€ ▼wdin wdin/C OpenDiscoverySpace 7.65 74 (26) 1.45 GALA 5.65 55 (20) 1.77 OpenScout 2.80 52 (17) 2.89 STELLAR 4.99 41 (14) 2.56 ROLE 6.60 35 (12) 2.19Lehrstuhl Informatik 5 TEL-Map 2.13 31 (10) 3.10(Information Systems) Prof. Dr. M. Jarke iTEC 9.45 20 (5) 0.75 13
  • Organizational Collaboration Collaboration is the fertile soil for R&D output in CPs Follow-up proposals / projects Shapes the research agenda Graph: – Edge between O1 and O2 if both participated in at least one project The Open STELLAR, EUROGENE, – Weight: number of projects University ROLE, PROLEARN, iCOPER, ASPECT – Direction: none KULehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke – Nodes: organizations Leuven 14
  • Organizational Collaboration NetworkLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 15
  • Organizational Collaboration NetworkLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 16
  • Collaboration Network Properties Nodes 852 Clustering coefficient 0.89 Edges 12 021 Avg. degree 28.2 Avg. weighted degree 30 Diameter 6 Avg. path length 2.68Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 17
  • Frequent Collaboration Pairs National Centre For Wirtschaftsuniversitaet 4.0Scientific Research Demokritos Wien 4.0 Giunti Labs S.R.L. 5.0 4.0 imc Information Multimedia Katholieke Universiteit 5.0 The Open University 5.0 Communication AG 5.0 6.0 Leuven Ecole Polytechnique 4.0 Federale De Lausanne 4.0 4.0 5.0 Politecnico Di Milano Jyvaskylan Yliopisto Gottfried Wilhelm Leibniz Open Universiteit 4.0 Universitaet Hannover 6.0 Nederland 5.0 4.0 4.0 Deutsches Forschungszentrum Universitaet 4.0 Fuer Kuenstliche Intelligenz Gmbh Duisburg-Essen 4.0 4.0 Bundesministerium Fuer Atos Origin Sociedad Technische Universiteit Fraunhofer-Gesellschaft Univerza V Ljubljani Wissenschaft Und Forschung Anonima Espanola Eindhoven ... E.V 4.0 4.0 4.0 Ellinogermaniki Agogi Universitaet Graz EUN Partnership Aisbl Scholi Panagea Savva Ae 4.0 5.0 4.0 Bundesgymnasium Und The Provost Fellows Tiigrihuppe Sihtasutus Bundesrealgymnasium Schwechat ... Near Dublin 1. PROLEARN (FP6): 17 pairs 4. GRAPPLE (FP7): 8 pairs 2. ICOPER (ECP): 11 pairs 5. PROLIX (FP6): 6 pairs Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 3. OpenScout (ECP): 9 pairs 6. STELLAR (FP7), ROLE (FP7): 5 pairs 18
  • Projects Space @ LearningFrontiers.euLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 19
  • Projects Space @ LearningFrontiers.euLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 20
  • Projects Space @ LearningFrontiers.euLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 21
  • DashboardLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke http://learningfrontiers.eu/?q=dashboard [9] 22
  • Summary Networks – Collaboration: low diameter, high clustering – Projects: low diameter, low clustering – Small “oligarchic core” of frequent collaborators – In line with previous research in FP1-6 Impact measure to account for time/size – Correlates with in-degree, funding, betweenness centrality – Networks (NoE and BPN) occupy 5 of top 8 spots – Projects to follow: ODS, GALA, iTEC, ROLE, TEL-Map, …Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 23 Explore data on learningfrontiers.eu
  • Limitations Collaboration ties rest on people, not organizations – EC deals with legal entities – Partners deal with people – People move on, legal entities merge and rebrand, etc. Consortium overlaps may be random Edges don’t fade over time, connections do Data set – Selection of programmes; LLP missing – What is a “TEL related call”?Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke 24 – Missing associate memberships, seed projects, etc.
  • References 1. Voigt, C. (ed.): Deliverable D7.5, STELLAR Nework of Excellence (2011) 2. Barber, M., Krueger, A., Krueger, T., Roediger-Schluga, T.: Network of European Union–funded collaborative research and development projects. Physical Review E 73 (2006) 3. Roediger-Schluga, T., Barber, M.J.: R&D collaboration networks in the European Framework Programmes: data processing, network construction and selected results. International Journal of Foresight and Innovation Policy 4(3/4), 321–347 (2008) 4. Frachisse, D., Billand, P., Massard, N.: The Sixth Framework Program as an Affiliation Network: Representation and Analysis (2008), http://ssrn.com/abstract=1117966 5. Breschi, S., Cusmano, L.: Unveiling the texture of a European Research Area: emergence of oligarchic networks under EU Framework Programmes. International Journal of Technology Management 27(8), 747–772 (2004) 6. Lozano, S., Duch, J., Arenas, A.: Analysis of large social datasets by community detection. The European Physical Journal Special Topics 143(1), 257–259 (2007) 7. Scherngell, T., Barber, M.J.: Spatial interaction modelling of cross-region R&D collaborations: empirical evidence from the 5th EU framework programme. Papers in Regional Science 88(3), 531–546 (2009) 8. Roediger-Schluga, T., Dachs, B.: Does technology affect network structure? – A quantitative analysis of collaborative research projects in two specific EU programmes. UNU-MERIT Working Paper Series 041 (2006) 9. Derntl, M., Erdtmann, S., Klamma, R.: An Embeddable Dashboard for Widget-Based Visual Analytics on Scientific Communities. In: I-KNOW 2012. ACM (2012) 10. Blondel, V. D., Guillaume, J., Lambiotte, R., Lefevre, E.: Fast unfolding of communities in large networks. Journal ofLehrstuhl Informatik 5 Statistical Mechanics: Theory and Experiment 2008 (10)(Information Systems) Prof. Dr. M. Jarke 25