Modeling Communities in Information Systems: Informal Learning Communities in Social Media

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. Jarke

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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

http://dbis.rwth-aachen.de/cms/research/MiCIS


http://creativecommons.org/licenses/by-sa/3.0/




Prof. Dr. M. Jarke

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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






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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

Backgroundand context

Methodology

Conclusions and Outlook

TechnicalContribution

Test Cases






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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


Methodology



Test Cases






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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


Methodology



Test Cases






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TeLLNet

Networked learning & community of practice: learning in collaboration Wenger, 1998, Dillenbourg, 1999, Stahl, 2006

Learning Theories Recapitulation

1934 1954 19711972 1973 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


Methodology



Test Cases






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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 interestsWenger, 1998

Data management Klamma, 2010

Community analytics Yu, 2009

Conceptual modeling Klamma, 2013

correspond to CoP dimensions andactors in media networks

Motivation


Methodology



Test Cases






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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


Methodology



Test Cases






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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 networkanalysis 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


Methodology



Test Cases






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TeLLNet Workflow of Community Learning Analytics

Co

nti

nu

ou

s re

qu

irem

en

ts

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 modelsSmith 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


Methodology



Test Cases






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TeLLNet

Learningresource

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 beextended to describethe rationale of agents

+ point out dependencies betweenhuman and non-human agents

+ emphasize agents, their types and roles

+ indicate intentions in social networks

+ models can be createdusing XML-based format

- too abstract

- before applying i* modeling training is required

Motivation


Methodology



Test Cases






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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

Goaldependency

Mutual engagement Shared repertoire Joint enterprises

Motivation


Methodology



Test Cases

ProcessProcess

ArtifactArtifact

initializes

D

D

MediumMedium hostsD D

consists of DD

influences

D

D






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TeLLNet

Resource dependency

Agent

Dependee

Depender

Task dependency

Agent

Goaldependency

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


Methodology



Test Cases






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TeLLNet

Refinement: A General Agent-based Model ofAn Informal Learning Community in Media

Society 𝑆𝑜𝑐 = 𝐴, 𝐴𝑐𝑡𝐴 = {𝐴1… 𝐴𝑛} is a set of agents

𝐴𝑐𝑡 is a set of predefined actions of agents 𝐴

𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡𝑠𝑡 = 𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡1…𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡𝑘𝑡 are created by agents A

with 𝐴𝑐𝑡 at 𝑡

𝑅 𝑡 ∈ 𝐴 × 𝐴 × ℝ+ are social relations, where 𝑡 is a time point

𝐴𝜃(𝑡)

𝐶𝑡, where 𝐶𝑡 = 𝐶1… 𝐶𝑚𝑡⊆ 𝐶 , 𝐶𝑡 is a set of communities

𝑀𝑒𝑑𝑖𝑎 = {𝑀𝑒𝑑𝑖𝑢𝑚1, … 𝑀𝑒𝑑𝑖𝑢𝑚𝑟}, where

𝐴𝑟𝑡𝑖𝑓𝑎𝑐𝑡𝑠𝑡𝜗(𝑡)

𝑀𝑒𝑑𝑖𝑢𝑚𝑖

𝑆 = 𝑆1… 𝑆𝑑 is a set of strategies of agents, where S = d ∈ Ν𝑆 = 𝑅𝑒𝑐𝑖𝑝𝑟𝑜𝑐𝑖𝑡𝑦, 𝑃𝑟𝑒𝑓𝑒𝑟𝑒𝑛𝑡𝑖𝑎𝑙 𝑎𝑡𝑡𝑎𝑐ℎ𝑚𝑒𝑛𝑡

Connecting with known agents Rich get richer

Not a Web 2.0 Web 2.0Barabasi & Albert, 1999RQ2

Motivation


Methodology



Test Cases






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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 tablesRQ2

Klamma, 2010

Motivation


Methodology



Test Cases






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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


Methodology



Test Cases

RQ3






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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 𝐶𝑖𝑗 , 𝐶𝑟𝑘 = max𝐶𝑖𝑗⋂𝐶𝑟𝑘

𝐶𝑖𝑗,𝐶𝑖𝑗⋂𝐶𝑟𝑘

𝐶𝑟𝑘≥ 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


Methodology



Test Cases






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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


Methodology



Test Cases

RQ3






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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

CommunityMedium (Forum)

usesn 1

Community Media (Project,E-mail, Blog)

uses1 n

TeLLNet

CommunityMedium

(Wiki)usesn 1

originatesfrom

Country 11

Motivation


Methodology



Test Cases






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TeLLNet


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., 2014Petrushyna 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


Methodology



Test Cases






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TeLLNet


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., 2014Petrushyna et al., 2015

Motivation


Methodology



Test Cases






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TeLLNet

Architecture for Community Learning Analytics Framework

Motivation


Methodology



Test Cases






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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 learnwant 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


Methodology



Test Cases






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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


Methodology



Test Cases






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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 1Thread 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


Methodology



Test Cases






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TeLLNet

21 i* experts evaluated i* models oflearning communities: social network analysis (71%) and

intent analysis (90%) are helpful forcreating i* models

community stakeholders canunderstand community situationsbetter using i* models (86%)

emphasizing community requirements for developers (86%)

i* models can be abstract and notstraightforward

Training is required before stakeholderscan use models

Evaluation of Community Analytics Techniques

SocialNetwork Analysis

Community DetectionandEvolution

IntentAnalysis

NamedEntitiesRetrieval

Motivation


Methodology



Test Cases






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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


Methodology



Test Cases






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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 𝑒𝑚𝑎𝑖𝑙𝑠𝑜𝑢𝑡 + 𝐶𝑒𝑛𝑡𝑟𝑎𝑙𝑖𝑡𝑦𝑜𝑢𝑡𝑑𝑒𝑔𝑟𝑒𝑒 + 𝑁 𝑝𝑟𝑜𝑗𝑏𝑙𝑜𝑔𝑃𝑜𝑠𝑡𝑡 +

𝑁 𝑏𝑙𝑜𝑔𝐶𝑜𝑚𝑡 + 𝑁 𝑝𝑟𝑖𝑧𝑒𝐶𝑜𝑚𝑡 + 𝑁 𝑝𝑟𝑜𝑗𝐶𝑜𝑚𝑡 ],

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


Methodology



Test Cases






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TeLLNet

Estimation of Quality of Project Participation Using Community Analysis

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

Fre

quency

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 60 700

0.2

0.4

0.6

0.8

1

Degre

e


(b) Quality labels and degree

Blog

Contact

Project

Wall

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

Betw

eenness


(c) Quality labels and betweenness

Blog

Contact

Project

Wall

0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1

Clu

ste

ring


(d) Quality labels and clustering

Blog

Contact

Project

Wall

Quality labels (QL) are prizes according to eTwinningambassadors (active stakeholders)

Number of QL correlates positively with betweennessof teachers in project networks

Pham et al.,2012

Motivation


Methodology



Test Cases






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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 implementationis efficient for bignetworks with > 1Kedges

GPU implementationallows detection ofhuge communitiesusing just ONE! PC

Motivation


Methodology


Technical Contribution

Test Cases

GPU

CPU

10K 25K 50K 100K

2.5K

2K

1.5K

1K

0.5K

Seconds

Edges






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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




Test Cases

Methodology






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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




Test Cases

Methodology






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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




Test Cases

Methodology






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TeLLNet Acknowledgements

To my supervisors

To my family

To my colleagues and friends

To my students

TEE






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Andrew Hilts and Eric Yu: Intentional Modeling of Social Media Design Knowledge for Government-Citizen Communication. In Martin Atzmueller, Andreas Hotho, Markus Strohmaier, and Alvin Chin, editors, Analysis of Social Media and Ubiquitous Data, volume 6904 of Lecture Notes in Computer Science, pp. 20–36. Springer Berlin Heidelberg, 2011.

Matthias Jarke, Ralf Klamma, Gerhard Lakemeyer, and Dominik Schmitz: Continuous, Requirements-Driven Support for Organizations, Networks, and Communities. In Proceedings of the 3rd International i* Workshop, Recife, Brazil, February 11-12, 2008, pp 47–50

Roula Karam, Piero Fraternali, Alessandro Bozzon, and Luca Galli: Modeling End-Users as Contributors in Human Computation Applications. In Alberto Abelló, Ladjel Bellatreche, and BoualemBenatallah, editors, Proceedings of Model and Data Engineering (MEDI) 2012, Poitiers, France , 3-5 October, pp. 3–15. Springer Berlin Heidelberg, 2012.

Ralf Klamma: Werkzeuge und Modelle für die Übergreifende Untersuchung von Social Software. i-com, 9(3), pp. 12–20, 2010.

Ralf Klamma, Zinayida Petrushyna. Pattern-Based Competence Management: On the Gap between Intentions and Reality, In Proceedings of 11th IFIP WG 5.5 Working Conference on Virtual Enterprises (PRO-VE), St. Etienne, France, October 11-13, 2010, pp. 364-371

Ralf Klamma and Zinayida Petrushyna. The Troll Under the Bridge: Data Management for Huge Web Science Mediabases. In Proceedings of the 38. Jahrestagung der Gesellschaft für Informatik e.V. (GI), die INFORMATIK, pages 923–928. Köllen Druck+Verlag GmbH, Bonn, 2008.

Ralf Klamma: Community Learning Analytics – Challenges and Opportunities. In Jhing-Fa Wang and Rynson W. H. Lau, editors, Advances in Web-Based Learning - ICWL 2013: Proceedings of ICWL 2013, volume 8167 of Lecture Notes in Computer Science, pp. 284–293, Kenting, Taiwan, October 6-9, 2013.






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TeLLNet ReferencesStyliani Kleanthous and Vania Dimitrova: Semantic enhanced approach for modelling cognitive relationships in virtual communities. In J. Vassileva, M. Tzagarakis, and V. Dimitrova, editors, Proceedings of Workshop on Adaptation and Personalisation in Social Systems: Groups, Teams, Communities: Proceedings of Workshop on Adaptation and Personalisation in Social Systems: Groups, Teams, Communities held at 11th International Conference on UM07, Corfu, Greece, July 25-29, 2007.

Styliani Kleanthous and Vania Dimitrova: Analyzing Community Knowledge Sharing Behavior. In Paul de Bra, Alfred Kobsa, and David Chin, editors, Proceedings of User Modeling, Adaptation, and Personalization: Proceedings of UMAP, volume 6075 of Lecture Notes in Computer Science, pp. 231–242. Springer Berlin Heidelberg, 2010.

Julian Krenge, Zinayida Petrushyna, Milos Kravcik, Ralf Klamma: Identification of Learning Goals in Forum-based Communities, In Proceedings of 11th IEEE International Conference on Advanced Learning Technologies (ICALT), Athens, GA, USA, 6-8 July, 2011, pp. 307-309

Kröll, M., and Strohmaier, M. “Analyzing human intentions in natural language text.” Proceedings of the fifth international conference on Knowledge capture (2009): 197–198. http://portal.acm.org/citation.cfm?id=1597735.1597780.

Madanmohan TR, Navelkar S (2004) Roles and knowledge management in online technology communities: an ethnography study. International Journal of Web Based Communities 1: 71‐89

Mark E. J. Newman and Michelle Girvan: Finding and evaluating community structure in networks. PHYSICAL REVIEW E, 69, 2004.

Helmut M. Niegemann, Steffi Domagk (2005) ELEN project Evaluation Report

Reihaneh Rabbany k., Mansoureh Takaffoli, and Osmar R. Zaiane: Social Network Analysis and Mining to Support the Assessment of On-line Student Participation. SIGKDD Explor. Newsl., 13(2), pp. 20–29, 2012.

G. Pask and B.C.E. Scott. Learning strategies and individual competence. International Journal of Man-Machine Studies, 4:217–253, 1972.

Seymour Papert. Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc, 1980.

James W. Pennebaker, Cindy K. Chung, Molly. Ireland, Amy. Gonzales, and Roger J. Booth. The Development and Psychometric Properties of LIWC2007, 2007.

Zinayida Petrushyna, Ralf Klamma: No Guru, No Method, No Teacher: Self-Oberservation and Self-Modelling of E-Learning Communities. In Proceedings of 3rd European Conference on Technology Enhanced Learning, (EC-TEL), Maastricht, The Netherlands, pp. 354-365, September, 2008

Zinayida Petrushyna, Ralf Klamma, Milos Kravcik: Designing During Use: Modeling of Communities of Practice, In Proceedings of 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), Dubai, U.A.E., 13-16 April 2010, pp. 612-617

Zinayida Petrushyna, Alexander Ruppert, Ralf Klamma, Dominik Renzel, Matthias Jarke: i*-REST: Light-Weight Modeling with RESTful Web Services. Published in F. Dalpiaz, J. Horkoff, editors, Proceedings of the Seventh International i* Workshop co-located with the 26th International Conference on Advanced Information Systems Engineering (CAiSE), Thessaloniki, Greece, June 16-17, 2014, CEUR Workshop Proceedings 1157, paper 15.

Jean Piaget. The child’s conception of the world. Paladin, St. Albans, Great Britain, 1973.

Zinayida Petrushyna, Ralf Klamma, Milos Kravcik. On Modeling Learning Communities. In Proceedings of 10th European Conference of Technology Enhanced Learning (EC-TEL), Toledo, Spain, September 16-18, 2015, pp. 254-267

Manh Cuong Pham, Yiwei Cao, Zinayida Petrushyna, and Ralf Klamma. Learning Analytics in a Teachers’ Social Network. In et al. Hodgson, editor, Proceedings of the Eighth International Conference on Networked Learning (NLC 2012), 2012.

Maren Scheffel, Katja Niemann, Abelardo Pardo, Derick Leony, Martin Friedrich, Kerstin Schmidt, Martin Wolpers, and Carlos Delgado Kloos: Usage pattern recognitionin student activities. In Carlos Delgado Kloos, Denis Gilet, Raquel Crespo-Garcia, Fridolin Wild, and Martin Wolpers, editors, Towards Ubiquitous Learning, volume 6964 of Lecture Notes in Computer Science, pp. 341–355. Springer, 2011.

Patrick Siehndel, Ricardo Kawase, Asmelash Teka Hadgu, and Eelco Herder. Finding Relevant Missing References in Learning Courses. In Proceedings of the 22Nd International Conference on WorldWide Web Companion, WWW ’13 Companion, pages 425–430, Republic and Canton of Geneva, Switzerland, 2013. International World Wide Web Conferences Steering Committee.

B.F Skinner. The science of learning and the art of teaching. Harvard Educational Review, pages 88–97, 1954.

Marc A. Smith, Peter Kollock (eds) (1999) Communities in Cyberspace. Routledge, London, New York

Ergang Song, Zinayida Petrushyna, Yiwei Cao, Ralf Klamma: Learning Analytics at Large: The Lifelong Learning Network of 160, 000 European Teachers, In Carlos Delgado Kloos, Denis Gillet, Raquel M. Crespo García, Fridolin Wild, Martin Wolpers (Eds.): Towards Ubiquitous Learning –Proceedings of 6th European Conference of Technology Enhanced Learning (EC-TEL), Palermo, Italy, September 20-23, 2011, pp. 398-411

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TeLLNet ReferencesStrohmaier, Markus, and Kröll, Mark. “Acquiring knowledge about human goals from Search Query Logs.” Information Processing & Management 48, no. 1 (2012): 63–82.

Hee-Joen Suh and Seung-Wook Lee: Collaborative Learning Agent for Promoting Group Interaction. ETRI, 28(4), pp. 461–474, 2006.

Kimberley Upton and Judy Kay. Narcissus: Group and Individual Models to Support Small Group Work. In Geert-Jan Houben, Gord McCalla, Fabio Pianesi, and Massimo Zancanaro, editors, User Modeling, Adaptation, and Personalization, volume 5535 of Lecture Notes in Computer Science, pp. 54–65. Springer Berlin Heidelberg, 2009.

Marta Tatu: Discovering Intentions in Text and Semantic Calculus: Intention Overview, Classification, Representation, Discovery and Interactions with Other Semantic Relations. VDM Verlag, Saarbrücken, Germany, Germany, 2008.

Tammara Combs Turner, Marc A. Smith, Danyel Fisher et al. (2005) Picturing Usenet: Mapping Computer-Mediated Collective Action. Journal of Computer-Mediated Communication 10: 7

Katrien Verbert, Nikos Manouselis, Hendrik Drachsler, and Erik Duval. Dataset-driven research to support learning and knowledge analytics. Educational Technology &Society, 15(3), pp. 133–148, 2012.

Lev Vygotsky. Thought and Language. MIT Press, Cambridge, MA, 1934/1986.

Etienne Wenger: Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press, Cambridge, UK, 1998.

Martin Wolpers, Jad Najjar, Katrien Verbert, and Erik Duval: Tracking Actual Usage:the Attention Metadata Approach. Educational Technology & Society, 10(3), pp. 106– 121, 2007.

Eric Siu-Kwong Yu: Social Modeling and i*. In Conceptual Modeling: Foundations and Applications: Essays in Honor of John Mylopoulos, edited by Alex. Borgida, Akmal B. Chaudhri, Paolo Giorgini and Eric Siu-Kwong Yu, pp. 99–121. Springer Berlin Heidelberg, 2009.

Yiwen Zhang and Mohan Tanniru: An Agent-Based Approach to Study Virtual Learning Communities. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Big Island, HI, USA, 3-6 January, 2005.

Yuzhou Zhang, Jianyong Wang, Yi Wang, and Lizhu Zhou: Parallel Community Detection on Large Networks with Propinquity Dynamics. In Proceedings of the 15th ACM SIGKDD Conference on Knowledge and Discovery and Data Mining, Paris, France — June 28 - July 01, 2009, pp. 997–1005.

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Modeling Communities in Information Systems: Informal Learning Communities in Social Media

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