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. 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. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
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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
Background
and context
Methodology
Conclusions
and Outlook
Technical
Contribution
Test Cases

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. 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. 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. 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. 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. 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. 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. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
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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. 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. 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. 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. Lehrstuhl Informatik 5
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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. Lehrstuhl Informatik 5
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Prof. Dr. M. Jarke
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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
Background
and context
Methodology
Conclusions
and Outlook
Technical
Contribution
Test Cases
RQ3

17. Lehrstuhl Informatik 5
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Prof. Dr. M. Jarke
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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 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

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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
Background
and context
Methodology
Conclusions
and Outlook
Technical
Contribution
Test Cases
RQ3

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Prof. Dr. M. Jarke
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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
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. 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. Lehrstuhl Informatik 5
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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. Lehrstuhl Informatik 5
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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. Lehrstuhl Informatik 5
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Prof. Dr. M. Jarke
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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 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. Lehrstuhl Informatik 5
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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. Lehrstuhl Informatik 5
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Prof. Dr. M. Jarke
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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 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

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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. Lehrstuhl Informatik 5
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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. Lehrstuhl Informatik 5
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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 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

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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

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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 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

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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
Background
and context
Conclusions
and Outlook
Technical
Contribution
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
Background
and context
Conclusions
and Outlook
Technical
Contribution
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
Background
and context
Conclusions
and Outlook
Technical
Contribution
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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TeLLNet
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