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This work is licensed under a Creative Common...
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Outline
 Motivation and Research Questions
...
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 Formal learning communities are students in...
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Social Media Usage for Informal Learning
Lear...
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Research Questions
Connecting advanced comput...
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Networked learning & community of practice: l...
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Community of Practice and Technology
Digital ...
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Overview of Research Answers
Systematic workf...
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Technical Contributions
 The metamodel of in...
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Workflow of Community Learning Analytics
Con...
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Learning
resource
Learning
goal
Acceptance
S...
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Modeling: A General Learning Community Model...
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Resource
dependency
Agent
Dependee
Depender
...
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Refinement: A General Agent-based Model of
A...
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Monitoring: Mediabase Cube
 Mediabase Cube ...
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Analysis Workflow
interactions of learners G...
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Detection
 Define time intervals based on e...
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 Emotional analysis Pennbaker et al. 2007, ...
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Overview of Case Studies
 Modeling Learning...
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Modeling Learning Communities
in Learning Fo...
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Modeling Learning Communities
in Learning Fo...
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Architecture for
Community Learning Analytic...
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How to Realize Continuous Support of Informa...
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Strategies:
Reciprocity
only
High
Reciprocit...
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40% follow life cycle of self-regulated lear...
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21 i* experts evaluated i* models of
learnin...
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Competence Management Support for
European T...
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How to Support Self-Monitoring of Learners?
...
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Estimation of Quality of Project Participati...
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Accelerating Community Detection and
Evoluti...
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Contributions and Conclusions
Modeling Refin...
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Contributions in Informal Learning Context
...
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Limitations and Follow-up Research
 Refinem...
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Acknowledgements
 To my supervisors
 To my...
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References
Fabian Abel, Ilknur Celik, Claudi...
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References
Styliani Kleanthous and Vania Dim...
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References
Strohmaier, Markus, and Kröll, Ma...
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Modeling Communities in Information Systems: Informal Learning Communities in Social Media

  1. 1. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 1/36 TeLLNet This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. M. Sc. Zinayida Kensche (née Petrushyna) Doctoral Thesis Defense Chair of Information Systems and Databases RWTH Aachen University Aachen November 17, 2015 Modeling Communities in Information Systems: Informal Learning Communities in Social Media
  2. 2. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 2/36 TeLLNet Outline  Motivation and Research Questions  Background and Context of Informal Learning  Continuous Support of Community Life Cycle  Test cases – Modeling Informal Learning Communities in Learning Forums – Competence Management in Lifelong Learning Communities  Conclusion and Outlook
  3. 3. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 3/36 TeLLNet  Formal learning communities are students in lectures  Informal learning communities are self-organized  Stakeholders care about their communities: – What are insights of informal learning communities? – Their success and failures? – Can communities learn from other communities? – How do communities evolve? Motivation Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  4. 4. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4/36 TeLLNet Social Media Usage for Informal Learning Learning Analytics Conceptual Modeling Formal learning: a MOOC Informal learning: forums, blogs, mailing lists, chats, social network sites Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  5. 5. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5/36 TeLLNet Research Questions Connecting advanced computer science tools and learning theories – the interdisciplinary character of the work Suh & Lee, 2006, Kleanthous & Dimitrova, 2007, 2010, Abel et al., 2011 Creating stereotype models and selecting suitable ones that describe community situations, needs, types, and future positions Zhang & Taniru, 2005, Li et al. 2008, Hilts & Yu, 2011, Fereira & Silva, 2012 Advanced computer science tools support communities by providing results of analytical investigation and estimation of community needs Wolpers et al., 2007, Kodinger et al., 2008, Upton & Kay, 2009, Dascalu et al., 2010, Scheffel et al. 2011, Karam et al., 2012, Verbert et al., 2012, Rabbany k. et al., 2012 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  6. 6. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6/36 TeLLNet Networked learning & community of practice: learning in collaboration Wenger, 1998, Dillenbourg, 1999, Stahl, 2006 Learning Theories Recapitulation 1934 1954 197119721973 1980 1986 1998 Social constructivism: social influence on learning Vygotsky, 1934/1986 Social learning/cognitive theory: society is pivotal for a learner Bandura, 1971, 1986 1999 2006 Cognitivism: individual style of learning Pask and Scott, 1972 Behaviorism: learning processes are guided interactions are shaped, Skinner, 1954 Cognitive constructivism: learning by discovery Piaget, 1973, Papert, 1980 Teaching machine Lack of social aspects of learning Cognitive processes Assimilating new and existing knowledge Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  7. 7. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7/36 TeLLNet Community of Practice and Technology Digital Media/ Community Information Systems Web 2.0 Processes/ i* Models/ Strategies (Cross-media Analysis) Members (Social Network Analysis, Community Detection & Evolution) Network of Artifacts (Emotional Analysis, Intent Analysis, Information Retrieval. Social Network Analysis) Network of Members Communities of practice Media Networks  Communities of Practice: collaborating, sharing same goals and interests Wenger, 1998  Data management Klamma, 2010  Community analytics Yu, 2009  Conceptual modeling Klamma, 2013 correspond to CoP dimensions and actors in media networks Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  8. 8. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8/36 TeLLNet Overview of Research Answers Systematic workflow for overall approach Petrushyna et al., 2014 Ground laying model for informal learning communities in digital media Petrushyna et al., 2010 Repository of model stereotypes Petrushyna et al., 2014 Simulation approach for refining online informal learning community models Tool set for modeling, monitoring and analyzing of informal learning communities in social media Petrushyna & Klamma, 2008, Klamma & Petrushyna, 2010, Krenge et al., 2011, Song et al., 2011, Petrushyna et al., 2014, Petrushyna et al., 2014a, Petrushyna et al., 2015 RQ1 RQ2 RQ2 RQ2 RQ3 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  9. 9. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 9/36 TeLLNet Technical Contributions  The metamodel of informal learning communities in digital media  The i*-REST service for modeling communities in i* Petrushyna et al., 2014  Professional and social competence modeling using social network analysis Song et al., 2011  The general agent-based model of informal learning communities  Community stereotype model repository Petrushyna et al., 2014  Mapping of i* models to Java based agents  Simulations of agent-based models of learning communities  A design of data cube appropriate for heterogenous data storage and rapid query processing Klamma and Petrushyna, 2008  The TargETLy service for community analysis Petrushyna et al., 2015, Krenge et al., 2011, Petrushyna et al., 2011  Implementation of community detection/evolution algorithms for large networks in distributive environment  The competence management support framework for lifelong learning communities Song et al., 2011  Estimation of learning quality using community analysis Pham et al., 2012 Modeling Refinement Monitoring Analysis Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  10. 10. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10/36 TeLLNet Workflow of Community Learning Analytics Continuous requirements  Maintenance of stored community digital traces  Defining user patterns, emotions, intents, concepts and topics of interest  Detecting communities and their evolution  Communities are represented by stereotype models Smith and Kollock, 1999, Cheung et al., 2005, Madanmohan and Siddhesh, 2004, Niegemann and Domagk , 2005, Fisher et al., 2006, Turner et al., 2005  Models reveal community requirements and insights  Stakeholders maintain communities operating suitable models  Simulations used to identify possible community changes Jarke et al., 2008 Petrushyna et al., 2014 RQ1 Modeling Refinement Monitoring Analysis Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  11. 11. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 11/36 TeLLNet Learning resource Learning goal Acceptance Support learning process Learner A Expert Community Learner Modeling: i* Modeling Approach for Informal Learning Community Modeling RQ2 Dependency resource Goal Softgoal Task Agent Role Depender Agent Dependee Agent + models can be extended to describe the rationale of agents + point out dependencies between human and non-human agents + emphasize agents, their types and roles + indicate intentions in social networks + models can be created using XML-based format - too abstract - before applying i* modeling training is required Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  12. 12. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 12/36 TeLLNet Modeling: A General Learning Community Model RQ2 Learner Community Learner A composes interacts Learner B creates space for knowledge sharing rules and policies limitations learns from Resource dependency Agent Dependee Depender Task dependency Agent Goal dependency Mutual engagement Shared repertoire Joint enterprises Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases ProcessProcess ArtifactArtifact initializes D D MediumMedium hostsD D consists of D D influences D D
  13. 13. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 13/36 TeLLNet Resource dependency Agent Dependee Depender Task dependency Agent Goal dependency Stereotypes of Learning Communities Communities can be represented by stereotype models Smith and Kollock, 1999, Madanmohan and Siddhesh, 2004, Cheung et al., 2005, Niegemann and Domagk , 2005, Turner et al., 2005, Fisher et al., 2006 RQ2  Teacher-oriented  Learner-oriented  Lifelong learners-oriented  Question-answer  Dispute  Innovative  Culture-sensitive  At workplace  Community of interest Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  14. 14. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14/36 TeLLNet Refinement: A General Agent-based Model of An Informal Learning Community in Media Society 𝑆𝑜𝑐 = 𝐴, 𝐴𝑐𝑡 𝐴 = {𝐴1 … 𝐴 𝑛} is a set of agents 𝐴𝑐𝑡 is a set of predefined actions of agents 𝐴 𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡𝑠 𝑡 = 𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡1 … 𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡 𝑘 𝑡 are created by agents A with 𝐴𝑐𝑡 at 𝑡 𝑅 𝑡 ∈ 𝐴 × 𝐴 × ℝ+ are social relations, where 𝑡 is a time point 𝐴 𝜃(𝑡) 𝐶 𝑡, where 𝐶 𝑡 = 𝐶1 … 𝐶 𝑚 𝑡 ⊆ 𝐶 , 𝐶 𝑡 is a set of communities 𝑀𝑒𝑑𝑖𝑎 = {𝑀𝑒𝑑𝑖𝑢𝑚1, … 𝑀𝑒𝑑𝑖𝑢𝑚 𝑟}, where 𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡𝑠 𝑡 𝜗(𝑡) 𝑀𝑒𝑑𝑖𝑢𝑚𝑖 𝑆 = 𝑆1 … 𝑆 𝑑 is a set of strategies of agents, where S = d ∈ Ν 𝑆 = 𝑅𝑒𝑐𝑖𝑝𝑟𝑜𝑐𝑖𝑡𝑦, 𝑃𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑡𝑖𝑎𝑙 𝑎𝑡𝑡𝑎𝑐ℎ𝑚𝑒𝑛𝑡 Connecting with known agents Rich get richer Not a Web 2.0 Web 2.0Barabasi & Albert, 1999 RQ2 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  15. 15. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15/36 TeLLNet Monitoring: Mediabase Cube  Mediabase Cube includes all actors of a learning community in dimensions + additional Time dimension  Results of analysis are stored in Facts tables RQ2 Klamma, 2010 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  16. 16. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16/36 TeLLNet Analysis Workflow interactions of learners Graph-based analysis Services responsible for mutual engagement dimension Services responsible for joint enterprises and shared repertoire dimensions texts of communities Language-based analysis Social Network Analysis Community Detection & Evolution Emotional Analysis Intent Analysis Information Retrieval Communities, patterns, emotions, interests, intents Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases RQ3
  17. 17. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17/36 TeLLNet Detection  Define time intervals based on events of communities 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙𝑗 = 𝑏𝑒𝑓𝑜𝑟𝑒𝑗, 𝑎𝑓𝑡𝑒𝑟𝑗 where j is an event  Modularity-based community detection Newman and Girvan, 2004  Propinquity algorithm Zhang et al. 2009 Evolution  Mapping of communities using modified Jaccard index 𝑆𝑖𝑚 𝐶𝑖 𝑗 , 𝐶𝑟 𝑘 = max 𝐶 𝑖 𝑗 ⋂𝐶 𝑟 𝑘 𝐶 𝑖 𝑗 , 𝐶 𝑖 𝑗 ⋂𝐶 𝑟 𝑘 𝐶 𝑟 𝑘 ≥ 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 Gliwa et al. 2012  Event extraction Asur et al. 2009  Community events: dissolve, form , merge, split, and continue  Node events: appear, disappear, join and leave Community Detection & Evolution RQ3 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  18. 18. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 18/36 TeLLNet  Emotional analysis Pennbaker et al. 2007, Calvo and D‘Mello 2010  Intent analysis Tatu, 2008, Kröll, 2009, Strohmaier et al., 2012  POS tagging + syntactic language patterns  Verb to verb pattern 𝑉𝐵1_𝑡𝑜_𝑉𝐵2, e.g., learn to calculate  Wh-adverb to verb pattern 𝑊𝑅𝐵_𝑡𝑜_𝑉𝐵, e.g., how to estimate  Learning Concepts and Topics Siehndel et al. 2013, d'Aquin and Jay, 2013  Named entities are arguments of information units Grishman and Sundheim, 1996  POS tagging + domain analysis  Linked Open Data Cloud Berners-Lee et al., 2006 Language-based Analysis Category Examples posemo awesome, super, negemo depress…, scary, anger aggress…, stupid…, cogmech infer…, problem…, insight explain…, reason…, Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases RQ3
  19. 19. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 19/36 TeLLNet Overview of Case Studies  Modeling Learning Communities in Learning Forums  Competence Management Support for European Teachers’ Communities  Cultural Analysis of Communities in 13 Wikipedia language projects Community Medium (Forum) usesn 1 Community Media (Project,E-mail, Blog) uses1 n TeLLNet Community Medium (Wiki) usesn 1 originates from Country 11 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  20. 20. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20/36 TeLLNet Modeling Learning Communities in Learning Forums  The language learning forum URCH  # posts ≈ 429.000 # users ≈ 21.000 # threads ≈ 68.000,  Other datasets with 10⁵ - 4,8x10⁵ edges for testing  User patterns (k-means clustering and SNA)  Intent analysis -> learning goals  Emotional analysis -> user attitude  Named entities of community texts Modeling Refinement Monitoring Analysis Petrushyna et al., 2014 Petrushyna et al., 2015  A community can be represented by a steeotype model or models from repository  Stakeholders can decide about changes they need to conduct in communities Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  21. 21. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21/36 TeLLNet Modeling Learning Communities in Learning Forums  The language learning forum URCH  # posts ≈ 429.000 # users ≈ 21.000 # threads ≈ 68.000,  Other datasets with 10⁵ - 4,8x10⁵ edges for testing  i* actors: users, threads, forums, user roles, topics of interest  Dependencies: user intents, user activities, actor dependencies  User patterns (k-means clustering and SNA)  Intent analysis -> learning goals  Emotional analysis -> user attitude  Named entities of community texts  Simulations using network strategies: reciprocity and preferential attachment  A number of possible community states in future Modeling Refinement Monitoring Analysis Petrushyna et al., 2014 Petrushyna et al., 2015 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  22. 22. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22/36 TeLLNet Architecture for Community Learning Analytics Framework Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  23. 23. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23/36 TeLLNet How to Realize Continuous Support of Informal Learning Communities? 01-10.12.2004# posts = 471 # users = 22 # adjacent nodes = 43 # high influence users = 13 # low influence users = 2 need to learn want to write take to solve started to take practice prepared to take beast trying to learn stuff # posts = 226 # users = 20 # adjacent nodes = 15 # high influence users = 4 # low influence users = 4 how to answer instructed to take writing supposed to answerplan to take GRE take to solve Petrushyna et al., 2015 08-17.12.2004 Models of a learning community in URCH forums Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  24. 24. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 24/36 TeLLNet Strategies: Reciprocity only High Reciprocity low PA 50% Reciprocity and 50% PA Can Model Simulations Predict Community Evolutions? initial 30 days later Simulated behaviors of learners differ according to strategies (reciprocity and preferential attachment (PA)) and activity probabilities (maps) Betweenness Closeness Clustering Degree Kolmogorov-Smirnov tests of measure distributions show a better correlation (<.5) between real and simulated community learners with >39 users Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  25. 25. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 25/36 TeLLNet 40% follow life cycle of self-regulated learning in cliques (tightly connected groups) while others need a support Estimation of Self-Regulated Theory Using Community Analysis Krenge et al., 2011 Nussbaumer et al., 2011 Thread 1 Thread 2 Thread 3 A user of a clique A non-clique user in a thread A clique-user missing in a thread Time Maintain Profile Select Resource Learn Reflect Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  26. 26. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 26/36 TeLLNet 21 i* experts evaluated i* models of learning communities:  social network analysis (71%) and intent analysis (90%) are helpful for creating i* models  community stakeholders can understand community situations better using i* models (86%)  emphasizing community requirements for developers (86%) i* models can be abstract and not straightforward Training is required before stakeholders can use models Evaluation of Community Analytics Techniques Social Network Analysis Community Detection and Evolution Intent Analysis Named Entities Retrieval Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  27. 27. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 27/36 TeLLNet Competence Management Support for European Teachers’ Communities Modeling Refinement Monitoring Analysis  Self-monitoring and self-reflection for teachers Kitsantas, 2002  Other stakeholders refine community situations based on monitoring and analysis  ≈164K teachers, ≈20K projects, ≈39K emails, ≈35K blog posts  Data transformation is required, e. g., ≈ 130K with wrong country value  Competence indicators for teachers, communities and stakeholders Song et al., 2011  Analysis of different media networks Pham et al., 2012  i* actors: project performance, activity, popularity, e-mail communicating skills, etc. eTwinning let European teachers cooperate with the means of projects, e-mails, blogs, comments, contact lists, walls, etc. Competence is the knowledge, skills, attitudes, … related to tasks McClelland, 1973 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  28. 28. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 28/36 TeLLNet How to Support Self-Monitoring of Learners? Reports for teachers and other stakeholder using competence indicators :  project performance (PP)  e-mail communication (EC)  blog writing (BW) PP EC BW CW A N Song et al., 2011 𝐴 𝑡 = 𝑁𝑝𝑟𝑜𝑗 𝑡 + 1 2 × [(𝑁 𝑒𝑚𝑎𝑖𝑙𝑠 𝑜𝑢𝑡 + 𝐶𝑒𝑛𝑡𝑟𝑎𝑙𝑖𝑡𝑦 𝑜𝑢𝑡𝑑𝑒𝑔𝑟𝑒𝑒 + 𝑁 𝑝𝑟𝑜𝑗 𝑏𝑙𝑜𝑔𝑃𝑜𝑠𝑡 𝑡 + 𝑁 𝑏𝑙𝑜𝑔𝐶𝑜𝑚 𝑡 + 𝑁 𝑝𝑟𝑖𝑧𝑒𝐶𝑜𝑚 𝑡 + 𝑁 𝑝𝑟𝑜𝑗𝐶𝑜𝑚 𝑡 ], where xxx𝐶𝑜𝑚 is a comment in a blog or devoted to a prize or a project Teacher 1 Teacher 2 Teacher 3 Teacher 4 Teacher 5 Teacher 6  comment writing (CW),  activity(A)  notability (N) 10 8 6 4 2 0 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  29. 29. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 29/36 TeLLNet Estimation of Quality of Project Participation Using Community Analysis 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 Frequency Number of quality labels (a) Quality labels and number of projects/blogs+blog posts/contacts/wall posts Blog Contact Project Wall 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 Degree Number of quality labels (b) Quality labels and degree Blog Contact Project Wall 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 Betweenness Number of quality labels (c) Quality labels and betweenness Blog Contact Project Wall 0 10 20 30 40 50 60 70 0 0.2 0.4 0.6 0.8 1 Clustering Number of quality labels (d) Quality labels and clustering Blog Contact Project Wall  Quality labels (QL) are prizes according to eTwinning ambassadors (active stakeholders)  Number of QL correlates positively with betweenness of teachers in project networks Pham et al.,2012 Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases
  30. 30. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 30/36 TeLLNet Accelerating Community Detection and Evolution on Single PC using GPU Dataset URCH STDocNet Number of snapshots 378 685 Number of edges ≈300K ≈480K GPU running time 30 min 22 min CPU running time > 4 h > 3h Dataset URCH STDocNet Number of snapshots 1 1 Number of edges 9110 1188 Number of nodes 857 263 GPU running time 30 min 1.5s CPU running time ≈2 h 4s GPU implementation is efficient for big networks with > 1K edges GPU implementation allows detection of huge communities using just ONE! PC Motivation Background and context Methodology Conclusions and Outlook Technical Contribution Test Cases GPU CPU 10K 25K 50K 100K 2.5K 2K 1.5K 1K 0.5K Seconds Edges
  31. 31. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 31/36 TeLLNet Contributions and Conclusions Modeling Refinement Monitoring Analysis  The workflow for Community Learning Analytics:  Toolset for modeling, refinement, monitoring and analysis of informal online learning communities  Support of informal online learning community stakeholders by integrating computer science approach with community of practice theory  A metamodel of learning communities and its stereotype models Motivation Background and context Conclusions and Outlook Technical Contribution Test Cases Methodology
  32. 32. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 32/36 TeLLNet Contributions in Informal Learning Context  The workflow proposes a structure for analytical investigation of informal learning communities  A toolset for validating learning theories’ assumptions  Justifying computer science approaches for community of practice analysis  Abstract modeling of informal learning communities emphasizing human and non-human agents  Validating existing theoretical community patterns Motivation Background and context Conclusions and Outlook Technical Contribution Test Cases Methodology
  33. 33. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 33/36 TeLLNet Limitations and Follow-up Research  Refinement of the toolset to perform near real-time monitoring, analysis and modeling Derntl et al., 2015  Extension of community analysis tools with other techniques, e.g. prediction models of student success  Involvement of new features and strategies for community simulation  The usage of heterogeneous media: SNSs, Twitter Motivation Background and context Conclusions and Outlook Technical Contribution Test Cases Methodology
  34. 34. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 34/36 TeLLNet Acknowledgements  To my supervisors  To my family  To my colleagues and friends  To my students TEE
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