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A talk given in the Learning Networks seminar series at CELSTEC@OUNL

A talk given in the Learning Networks seminar series at CELSTEC@OUNL

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- 1. TeLLNet Learning Analytics - Social Network Analysis for Learning Communities Yiwei Cao RWTH Aachen University Advanced Community Information Systems (ACIS) cao@dbis.rwth-aachen.deLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
- 2. Advanced Community Information Systems (ACIS)TeLLNet Web Engineering Responsive Community Web Analytics Open Visualization Community and Information Simulation Systems Community Community Support AnalyticsLehrstuhl Informatik 5 Requirements(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-2 Engineering
- 3. Advanced Community Information Systems • LAS & Services • yFilesTeLLNet • youTell • Repast • SeViAnno • AERCS Responsive • Network • Advanced Community Models Open Web & Visualization Community • Network Multimedia & Simulation Environments Analysis Technologies Web Engineering • Actor Network Web Analytics • XMPP Theory • HTML5 • Communities of • MPEG-7 Community Community Practice • Web Support Analytics • Game Theory Services • Community • Requirements • MediaBase Detection • RESTful Bazaar • Web Mining • PALADIN • LAS • CAMRS • MobSOS • Recommender • Cloud Systems Computing • Multi Agent • Mobile Simulation Computing Social Requirements Engineering • Agent and Goal Oriented i* ModelingLehrstuhl Informatik 5(Information Systems) • Participatory Community Design Prof. Dr. M. Jarke I5-Cao-0412-3
- 4. AgendaTeLLNet Learning analytics Social network analysis (SNA) Case study – TeLLNet for eTwinning & CAfe – AERCS for the computer researcher community – TEL-Map Learning Frontiers Dashboard Demonstration of the prototypes Conclusions and discussionsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-4
- 5. TeLLNet Learning AnalyticsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-5
- 6. Learning Analytics for Self-Regulated LearningTeLLNet The Horizon Report – 2011 EditionLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-6 Based on (Fruhmann, Nussbaumer, Albert, 2010)
- 7. Learning Analytics Support Interdisciplinary multidimensional model of learning networksTeLLNet – Social network analysis (SNA) is defining measures for social relations – i* Framework is defining learning goals and dependencies in self-regulated learning CoP – Learning Analytics & Visualization for CoP social software Media Networks network of artifacts Wiki, Blog, Podcast, IM, Chat, Microcontent, Blog entry, Message, Burst, Thread, Email, Newsgroup, Chat … Comment, Conversation, Feedback (Rating) i*-Dependencies (Structural, Cross-media) network of membersLehrstuhl Informatik 5 Members (Social Network Analysis: Centrality,(Information Systems) Efficiency) Prof. Dr. M. Jarke Communities of practice I5-Cao-0412-7
- 8. Learning AnalyticsTeLLNet Data Visual Context Network Learning analysis analytics analysis analysis analytics Data analysis is a process of inspecting, cleaning, transforming, and modeling data in order to highlight useful information, to suggest conclusions, and to support decision making (Wikipedia) Visual analytics analytical reasoning facilitated by interactive visual interfaces (Wong & Thomas, 2004) Context analysis is a method to analyze the environment in which a business operates (Wikipedia), here: the learning business Network analysis basis of network science, including SNA, link analysis, etc. Learning analytics is the solution for large scale networkLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-8
- 9. Data AnalysisTeLLNet The mass of data Cleaning Modeling Management Cross-disciplinary Cross-media Cross-platformLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-9
- 10. Visual Analytics A video of a Tang poem as a Learning resourceTeLLNet Tang poem - Jingyesi Macro level annotation Semantic annotation Learner communityLehrstuhl Informatik 5 Meso level annotation Context annotation (location)(Information Systems) Prof. Dr. M. Jarke Micro level annotation I5-Cao-0412-10
- 11. Context AnalyticsTeLLNet SWOT analysis – Internal vs. external – Based on questionnaires, interviews, expert opinions, pilot study, feedback, etc. Trend analysis – Prediction techniques Competence analysis – Competence modeling – Competence managementLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke Content analysis I5-Cao-0412-11
- 12. Competence: Social Capital Human capital vs. social capital (Burt, 1992)TeLLNet – Human capital: the personal ability to perform tasks (e.g. talent, education, etc.) – Social capital: the social environment surrounding individuals Social capital as a property of – Individuals: positions in social network that are more efficient in performing tasks (i.e. local structure) – Groups: structure of members’ network that makes the group functions more efficient (i.e. structure of a sub-network)Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-12
- 13. Network AnalysisTeLLNet A community development model (Pham et al., 2011)Lehrstuhl Informatik 5 In which stage is the members’ network of a given group?(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-13 How does it relate to the performance of the group?
- 14. TeLLNet Social Network AnalysisLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-14
- 15. Centrality Given the network G=(V,E), where V is the set of nodes and E is theTeLLNet set of edges Betweenness σ u (i, j ) B (u ) = ∑ u ≠ i ≠ j σ (i, j ) where: σ u ( i , j ): number of shortest paths between nodes i and j that pass through node u σ ( i , j ): total number of shortest paths between nodes i and j Local clustering coefficient {v, w ∈ N(u) : (v, w) ∈ E } C(u) = ( N(u) ( N ( u ) − 1 )) / 2Lehrstuhl Informatik 5 where: N ( u ) is the set of neighbors of node u(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-15
- 16. Qualify the Stage of Network Density: fraction of actual edges in the networkTeLLNet {v, w ∈ V : (v, w) ∈ E } , n is the number of nodes D= n 2 Global clustering coefficient 3× number of triangles D= number of connected triples Maximum betweenness: highest betweenness of nodes Largest connected component: fraction of nodes in largest connected component For large member networksLehrstuhl Informatik 5 - Diameter: the longest shortest path between any pair of nodes(Information Systems) Prof. Dr. M. Jarke - Average shortest path length I5-Cao-0412-16
- 17. Network Characteristics: Connectivity & Degree distribution Connectivity: measured by degreeTeLLNet Degree zi ≡ N i = { j ∈ N : ij ∈ L} , i where N 1 is first(-order) neighbor Second-order neighbor, where “geodesic” distance = 2 i i N2 ≡ { j ∈ N {i} : ∃k ∈ N , s.t.ik ∈ L ∧ kj ∈ L} N 1 Second-order degree: z ≡N i 2 i 2Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-17
- 18. Important Types of Degree Distribution For any network Γ, its (kth-order) degree distributionTeLLNet p(·) specifies 1 p(k ) = {i ∈ N : zi = k} for each k = 0, n 1, …, n-1 Binomial distribution with density Poisson distribution with density Geometric distribution with density Power-law distribution with densityLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-18
- 19. Power-Law Distribution −γTeLLNet p(k ) = Ak (k = 1, 2, ... ) Here: A = 1 / R (γ ) ∞ where R (γ ) ≡ ∑k −γ is the Riemann Zeta function k =1 and normalizes the distribution This degree distribution is scale-free if −γ p(αk ) = α p(k ) For any α and kLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-19
- 20. From Small World Model to Scale-Free Networks The “small world” proposed by Watts and StrogatzTeLLNet – Reconciles local structure (high clustering) – Presents typical internode proximity (low average distances) – Does not account for the heterogeneity of many real-world networks – Does not accommodate diversity of social networks due to low values of the “rewiring probability” Barabási and Albert embodies an explicit dynamic process of network formation with – Growth: the network is formed through the successive arrival of new nodes that, upon entry, link to some of the preexisting nodes – Preferential attachment: the (stochastic) mechanism used by new nodes in establishing their links is biased in favor of those thatLehrstuhl Informatik 5 are more highly connected at the time of their entrance(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-20
- 21. Forming Networks Considering Growth AloneTeLLNet Considering growth alone – Growing set of nodes – Unbiased linking Growth along can not be the only factor for network evolvement – If random linking is unbiased, the induced networks display a geometric degree distribution ( so-called exponential networks) – They are not qualitatively very different from the PoissonLehrstuhl Informatik 5 networks obtained in a stationary context(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-21
- 22. Scale-Free Networks Scale-free networks are in the sense that the degree distribution isTeLLNet power-law distributed: P(k ) ∝ k −γ The degree distribution is scale invariant only if the preferential attachment rule is perfectly linear; otherwise the degree is distributed according to a stretched exponential function The diameter of Barabási-Albert networks [Bollobás & Riordan, 2004] ˆ d ∝ ln(n) / ln(ln(n)) The clustering coefficient of a Barabási-Albert model is five times larger than those of a random graph with comparable size and order. It decreases with the network orderLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-22
- 23. The Small World Model In The Real World Clustering coefficient CTeLLNet Network n z measured Random graph Internet 6374 3.8 0.24 0.00060 World Wide Web 153127 35.2 0.11 0.00023 Power grid 4941 2.7 0.080 0.00054 Biology collaborations 1520251 15.5 0.081 0.000010 Mathematics collaborations 253339 3.9 0.15 0.000015 Film actor collaborations 449913 113.4 0.20 0.00025 Company directors 7673 14.4 0.59 0.0019 Word cooccurrence 460902 70.1 0.44 0.00015 Neural network 282 14.0 0.28 0.049 Metabolic network 315 28.3 0.59 0.090Lehrstuhl Informatik 5(Information Systems) Food web 134 8.7 0.22 0.065 Prof. Dr. M. Jarke I5-Cao-0412-23 [Newman et al., 2006]
- 24. Social Capital: Structural Hole vs. Closure Structural holes (Burt, 1992)TeLLNet - Nodes are positioned at the interface between groups (gatekeepers, e.g. node B) - Informational advantages: access to information from different parts of networks - Form novel ideas by combining information from different groups - Control the communication between groups Closure - Nodes are embedded in tightly-knit groups (e.g. node A) - More trust and security within coherent communities Social capital (Coleman, 1990) - Individuals and groups deriving benefits from social relationshipsLehrstuhl Informatik 5(Information Systems) - Network structural property: either structural hole or closure Prof. Dr. M. Jarke I5-Cao-0412-24
- 25. Identification of Individual Social Capital Given the network G=(V,E), where V is the set of nodes and E is theTeLLNet set of edges Structural holes: nodes with high betweenness σ u (i, j ) B (u ) = ∑ u ≠ i ≠ j σ (i, j ) where: σ u ( i , j ): number of shortest paths between nodes i and j that pass through node u σ ( i , j ): total number of shortest paths between nodes i and j Closures: nodes with high local clustering coefficient {v, w ∈ N(u) : (v, w) ∈ E } C(u) = ( N(u) ( N ( u ) − 1 )) / 2Lehrstuhl Informatik 5 where: N ( u ) is the set of neighbors of node u(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-25
- 26. Reading List to Social Network Analysis Social Network Analysis: Methods and Applications by StanleyTeLLNet Wasserman, Katherine Faust, Dawn Lacobucci Models and Methods in Social Network Analysis by Peter J. Carrington, John Scott, Stanley Wasserman Social Network Analysis: A Handbook by John P Scott Introducing Social Networks by Alain Degenne, Michel Forse The Development of Social Network Analysis: A Study in the Sociology of Science by Linton C. Freeman A longer reading list is at http://beamtenherrschaft.blogspot.com/2008/10/ social-network-analysis-and-complexity.htmlLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-26 Lecture at RWTH Aachen University: Web Science
- 27. TeLLNet Case Study I TeLLNet for eTwinning (Breuer et al., EC-TEL 2009, Song et al., EC-TEL 2011, Pham et al., NLC 2012)Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-27
- 28. TeLLNet - SNA for European Teachers’ Lifelong Learning How to manage and handleTeLLNet large scale data on social networks? How to analyse social network data in order to develop teachers’ competence, e.g. to facilitate a better project collaboration? How to make the network visualization useful for teachers’ lifelong learning?Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-28
- 29. Data Set Data #data entries Description Project 23641 Schools from at least two schools from at least two different European countries create aTeLLNet project and use ICT to carry out their work. Contact 769578 Teachers are able to explore other teachers profiles and add them into their own contact list. It is suggested to use forum and other media to contact the other teachers before taking them as a contact. Project diary 20963 Blog for project reports Project diary post 49604 Each blog entry in project diary Project diary 7184 Comments added to blog entries in project diary comment My journal 38496 Message posted on teachers wall which is part of teachers profile message Teacher 146105 Registered teachers working in European schools and, namely "eTwinner" Quality label 8042 Awarded first to projects. Then the project-involved schools and teachers are awarded accordingly. They are assigned by each country or on the European level: National Quality Label and European Quality Label Prize 1384 eTwinning Prizes are awarded to schools. They are of European level and are called European eTwinning Prizes Institution 91077 Various European schools: pre-school, primary, secondary and upper schoolsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke Statistics on eTwinning data (as of 11.11.2011) I5-Cao-0412-29
- 30. eTwinning Network Network #nodes #edges Description Project 37907 804856 Nodes are teachers (eTwinners) and there is a connection (edge) between twoTeLLNet (26%) (0.11%) teachers if they collaborated in at least one project. Edges in the network are undirected and weighted by the number of projects in which the two teachers collaborate. Contact 109321 573602 Nodes are teachers and there is an edge between two teachers if at least one (75%) (0.01%) teacher is in the contact list of the other. Edges are undirected and unweighted. Project diary 3264 3436 Nodes are teachers and there is an edge between two teachers if one teacher has (2.2%) (0.06%) commented on at least one blog post created by the other. Edges are directed and weighted by the number of comments. My journal 23919 30048 Nodes are teachers and there is an edge between two teachers if one teacher has (16%) (0.01%) posted or commented on the wall of the other. Edges are directed and weighted by the number of messages. Teacher networks statistics (as of 11.11.2011) Data is processed, transformed and loaded into Oracle data warehouse Networks are aged for time series analysis Network parameters are computed using Oracle store proceduresLehrstuhl Informatik 5(Information Systems) Projects are considered as groups to study group social capital Prof. Dr. M. Jarke I5-Cao-0412-30
- 31. eTwinning Network Information VisualizationTeLLNet • Teacher network 2008 as exampleLehrstuhl Informatik 5 •Cooperation among countries(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-31
- 32. Analysis and Visualization of Lifelong Learner Data Performance Data on Projects Network Structures and PatternsTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-32
- 33. System Architecture of Prototype CAfeTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-33
- 34. Self-monitoring of Teacher Network in CAfe Target usersTeLLNet – European teachers (teachers‘ workshops) – Administrators & policy-makersLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-34
- 35. Self-Monitoring of Competence ManagementTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-35
- 36. Self-Monitoring of Competence Management Community level ->TeLLNet Teacher levelLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-36
- 37. Properties of Teacher Networks: The Power Law Degree DistributionTeLLNetLehrstuhl Informatik 5 −α(Information Systems) Prof. Dr. M. Jarke Degree distribution of eTwinning networks follow the power law with the formula y = ax I5-Cao-0412-37
- 38. Teachers’ Social CapitalTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-38 Structural hole as a form of social capital in eTwinning networks
- 39. Projects Achievement and Non-structural PropertiesTeLLNet Number of countries and languages used somehow correlate to the quality Number of teachers and institutions: effect on small projects (less than 30 members)Lehrstuhl Informatik 5(Information Systems) Subject has no effect Prof. Dr. M. Jarke I5-Cao-0412-39
- 40. Projects Achievement and Structural PropertiesTeLLNet Project member networks: created using the previous project collaboration and wall messaging, reflect the early communication of project members High quality projects prefer the Bonding stage: consists of seperated densely connected groupsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke Form of social capital: structural hole I5-Cao-0412-40
- 41. Summary SNA & visualization as tools for Social capital in eTwinningTeLLNet competence development in Network learning networks – Both teachers and projects follow – Competence assessment is still structural hole limited in performance indication – The informational diversity is the Social capital defined in key success factor eTwinning Network Applications: recommendation – By SNA metrics tools – By a development model – Help teachers find projects, – Network structure of projects and contacts, etc. position of teachers: identified via – Help project organizers find, select networks created by several and invite project partners communication mechanisms (e.g.Lehrstuhl Informatik 5 message, project collaboration,(Information Systems) Prof. Dr. M. Jarke blog) I5-Cao-0412-41
- 42. TeLLNet Case Study II AERCS for Computer Scientist Community (Klamma et al., Complex 2009; Pham et al., ASONAM 2010; Pham et al. ???)Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-42
- 43. Data Set DBLP (http://www.informatik.uni-trier.de/~ley/db/)TeLLNet - 788,259 author’s names - 1,226,412 publications - 3,490 venues (conferences, workshops, journals) CiteSeerX (http://citeseerx.ist.psu.edu/) - 7,385,652 publications - 22,735,240 citations - Over 4 million author’s names Combination - Canopy clustering (McCallum, 2000) - Result: 864,097 matched pairs - On average: venues cite 2306 and are cited 2037 timesLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-43
- 44. AERCS - Recommendation of Venues for Young Computer ScientistsTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-44
- 45. TeLLNet Knowledge Network at Cluster LevelLehrstuhl Informatik 5(Information Systems) (Pham, Klamma, Jarke: Development of Computer Science Prof. Dr. M. Jarke Disciplines – A Social Network Analysis Approach, SNAM, 2011) I5-Cao-0412-45
- 46. Interdisciplinary Series: Top Betweenness CentralityTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-46
- 47. High Prestige Series: Top PageRankTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-47
- 48. Academic Community Development Development of theTeLLNet community: number of participants over years Continuity: participants by number of events attendedLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-48 ACM SIGMOD
- 49. Dynamic Networks: The VLDB CommunityTeLLNet VLDB 1990 VLDB 1995Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke VLDB 2000 VLDB 2006 I5-Cao-0412-49
- 50. Learning Analytics: EC-TEL Community among TEL Communities ICALT, ICWL, EC-TEL, IST, AIED (Pham, Derntl & Klamma, 2011)TeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-50
- 51. Community Visualizer for ICWLTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-51
- 52. Summary Series in computer scienceTeLLNet - Tend to be focused: developed main theme as core topic - Not so many series is successful in motivating authors to work on the main theme Conferences vs. journals - The same trend in the development of main topics - Conferences facilitate communication between participants: authors tend to collaborate cross communities Next questions: - How do series develop over time? - Can we detect the development patterns? - Can we identify good or bad development behavior? Applications: - To create awareness for conference/journal organizers and stakeholdersLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke - To give an overview of the community to researchers I5-Cao-0412-52
- 53. TeLLNet Case Study III Tel-Map (Derntl et al.: Mapping the European TEL Project Landscape Using Social Network Analysis and Advanced Query Visualization, ADVTEL 2011)Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-53
- 54. Work contextTeLLNet Mapping and roadmapping for TEL Understanding the current TEL landscape Finding strong and weak signals for change at different levels Different methods, e.g. Delphi, Community modeling, Text analysis, Visual analytics, etc.Lehrstuhl Informatik 5(Information Systems) Here: Social network analysis and visualization Prof. Dr. M. Jarke I5-Cao-0412-54
- 55. Data Set Progr. Call # Projects (acronyms) Call 2005 4 CITER, JEM, MACE, MELTTeLLNet Call 2006 7 COSMOS, EdReNe, EUROGENE, eVip, Intergeo, KeyToNature, Organic.Edunet ECP Call 2007 3 ASPECT, iCOPER, EduTubePlus Call 2008 5 LiLa, Math-Bridge, mEducator, OpenScienceResources, OpenScout IST-2002- CONNECT, E-LEGI, ICLASS, KALEIDOSCOPE, LEACTIVEMATH, PROLEARN, 8 2.3.1.12a TELCERT, UNFOLD IST-2004- APOSDLE, ARGUNAUT, ATGENTIVE, COOPER, ECIRCUS, ELEKTRA, I-MAESTRO, FP6 2.4.10b 14 KP-LAB, L2C, LEAD, PALETTE, PROLIX, RE.MATH, TENCOMPETENCE IST-2004- ARISE, CALIBRATE, ELU, EMAPPS.COM, ICAMP, LOGOS, LT4EL, MGBL, UNITE, 10 2.4.13c VEMUS ICT-2007.4.1d 6 80DAYS, GRAPPLE, IDSPACE, LTFLL, MATURE, SCY ICT-2007.4.3d 7 COSPATIAL, DYNALEARN, INTELLEO, ROLE, STELLAR, TARGET, XDELIA FP7 ALICE, ARISTOTELE, ECUTE, GALA, IMREAL, ITEC, METAFORA, MIROR, ICT-2009.4.2b 13 MIRROR, NEXT-TELL, SIREN, TEL-MAP, TERENCELehrstuhl Informatik 5 Total: 77(Information Systems) a … Technology-enhanced learning and access to cultural heritage” c … Strengthening the Integration of the ICT research effort in an Enlarged Europe” Prof. Dr. M. Jarke I5-Cao-0412-55 b … Technology-Enhanced Learning d … Digital libraries and technology-enhanced learning”
- 56. Data setTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-56
- 57. TEL Projects as Social NetworksTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-57
- 58. GP – FP7 project progressionTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-58
- 59. FP6, FP7, eContentplus FP6, FP7, ECP projectsTeLLNet Central role of IPs and NoEs as sources and harbors of consortiaLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke eContentplus as “gap filler” I5-Cao-0412-59
- 60. Project ranking Local Progr- Start Authority Page- Weighted Close. Betw.TeLLNet PROJECT Hub Degree Clust. amme Year ▼ Rank Degree Centrality Centrality Coeff. GALA FP7 2010 .0546 [1] .0634 [1] .0338 [4] 42 [3] 79 [5] .6847 [4] .0585 [3] .3449 [74] OpenScout ECP 2009 .0442 [2] .0495 [2] .0287 [6] 37 [6] 72 [6] .6609 [6] .0310 [7] .4790 [56] TEL-MAP FP7 2010 .0416 [3] .0000 [75] .0207 [11] 31 [11] 51 [11] .6230 [11] .0147 [20] .5032 [50] STELLAR FP7 2009 .0403 [4] .0464 [3] .0324 [5] 42 [3] 81 [4] .6909 [3] .0390 [4] .4135 [71] ROLE FP7 2009 .0338 [5] .0386 [4] .0252 [8] 36 [7] 61 [8] .6552 [7] .0347 [6] .4540 [63] iCOPER ECP 2008 .0338 [5] .0386 [4] .0354 [3] 39 [5] 91 [3] .6667 [5] .0224 [12] .4764 [59] Math-Bridge ECP 2009 .0299 [7] .0340 [6] .0156 [18] 26 [15] 35 [17] .5891 [16] .0163 [15] .5446 [42] ASPECT ECP 2008 .0286 [8] .0309 [7] .0250 [9] 30 [12] 59 [9] .6179 [12] .0289 [8] .4989 [55] mEducator ECP 2009 .0260 [9] .0294 [8] .0135 [23] 24 [18] 28 [26] .5891 [16] .0234 [10] .5580 [40] ITEC FP7 2010 .0260 [9] .0294 [8] .0167 [16] 22 [22] 37 [16] .5758 [23] .0176 [14] .5022 [51] MIRROR FP7 2010 .0260 [9] .0294 [8] .0129 [25] 24 [18] 29 [23] .5802 [20] .0061 [30] .6051 [33]Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-60
- 61. Geo-mapping http://is.gd/fp7telmapTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-61
- 62. GO – Project Collaborations FP7 Each project creates ties among its consortium membersTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-62
- 63. Project collaborationsTeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-63
- 64. Top Collaborators in FP7 Technische Universität Graz, Austria (82 conn. in 7 projects)TeLLNet Open Universiteit Nederland, Netherlands (67 / 5) Aalto-Korkeakoulusaatio, Finland (66 / 3) Katholieke Universiteit Leuven, Belgium (63 / 4). ATOS Origin Sociedad Anonima Espanola, Spain (59 / 4)Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-64
- 65. Project Collaborations FP6,7, ECP 605 organizationsTeLLNet in 77 projects creating 9K+ collaboration tiesLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-65
- 66. Top 10 Organizations Organization PR ▼ BC LC DC CC Funding*TeLLNet THE OPEN UNIVERSITY .0125 [1] .1209 [1] .2135 [603] 220 [1] .5421 [1] 3.55 [3] KATHOLIEKE UNIVERSITEIT LEUVEN .0090 [2] .0770 [2] .1701 [605] 149 [3] .5628 [5] 2.56 [5] OPEN UNIVERSITEIT NEDERLAND .0085 [3] .0411 [5] .2159 [602] 133 [7] .6014 [6] 3.45 [4] JYVASKYLAN YLIOPISTO .0080 [4] .0667 [3] .3168 [590] 170 [2] .5480 [2] 1.26 [39] DEUTSCHES FORSCHUNGSZENTRUM FUER .0066 [5] .0409 [6] .1892 [604] 107 [25] .5550 [17] 3.68 [1] KUENSTLICHE INTELLIGENZ GMBH ATOS ORIGIN SOCIEDAD ANONIMA ESPANOLA .0064 [6] .0237 [15] .4316 [565] 142 [5] .5335 [4] 1.33 [33] UNIVERSITAET GRAZ .0064 [7] .0229 [18] .4016 [574] 148 [4] .5279 [3] 2.03 [10] UNIVERSITEIT UTRECHT .0061 [8] .0204 [23] .4323 [564] 139 [6] .5279 [11] 1.62 [19] INESC ID - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES, INVESTIGACAO E .0061 [9] .0368 [7] .4741 [550] 130 [8] .5261 [19] 1.68 [16] DESENVOLVIMENTO EM LISBOA THE UNIVERSITY OF WARWICK .0058 [10] .0329 [8] .4754 [549] 129 [10] .5025 [10] 1.68 [17] PR = PageRank | BC = Betweenness centrality | LC = Local clustering coefficient | DC = Degree centrality | CC = Closeness centralityLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-66
- 67. Two Clustering Spheres Connectedness ofTeLLNet the neighborhood 137 / 605 (23%) are on the “higher sphere”. KALEIDOSCOPE (100%), STELLAR (94%), PROLEARN (91%),Lehrstuhl Informatik 5(Information Systems) RE.MATH (88%), GRAPPLE, ALICE, TEL-Map (80% each), ICOPER Prof. Dr. M. Jarke I5-Cao-0412-67 (74%) and IMREAL (72%).
- 68. Top partnership bonds Organizational pairing, e.g. OUNL + Hannover, OU +TeLLNet KUL (6), OU + OUNL / IMC / JYU (5), … The most important projects where the 22 strongest partnership pairs (4 or more projects) participated: 1. PROLEARN (FP6; 16 pairs), 2. ICOPER (eContentplus; 10 pairs), 3. OpenScout (eContentplus; 9 pairs), 4. GRAPPLE (FP7; 8 pairs), 5. STELLAR, ROLE (FP7; 5 pairs), andLehrstuhl Informatik 5 7. PROLIX (FP6, 5 pairs)(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-68
- 69. Want to Explore?TeLLNetLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-69
- 70. Summary IPs and NoEs and large ECP consortia are most centralTeLLNet projects (also: partnership bonds, clustering) “Multicultural” list of top organizations ECP as incubator for FP7 projects; strengthened weak ties. Research follows money. Two classes: clustered/loose neighborhood. Some achieve a clustering-paribus increase in SNA metrics Fresh blood is draining; bonds are growing stronger. We’re a family. SNA is capable of revealing clusters of organizations andLehrstuhl Informatik 5(Information Systems) projects that can be used as indicators of impact and Prof. Dr. M. Jarke I5-Cao-0412-70 sustainability
- 71. Demonstrations eTwinning CAfeTeLLNet AERCS: http://bosch.informatik.rwth-aachen.de:5080/AERCS/ Learning Frontiers Dashboard http://learningfrontiers.eu/?q=dashboard#Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-71
- 72. Conclusions Informal learning needs support of learning analyticsTeLLNet SNA is very useful for knowledge discovery Detection the development pattern of learner communities supports context analytics and visual analytics User interface design influences visual analyticsLehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-72
- 73. Interdisciplinary DiscussionsTeLLNet Learning analytics or just data mining in TEL? What are the roles of learner communities in learning analytics? How do communities of practice work in learning networks?Lehrstuhl Informatik 5(Information Systems) Prof. Dr. M. Jarke I5-Cao-0412-73

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