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Transcript of 2012 03 16 (uc3m) emadrid rklamma rwth au analitica aprendizaje mundo movil perspectiva sistemas...
TeLLNet
Learning Analytics in a Mobile World A Community Information
Systems Perspective
Ralf Klamma RWTH Aachen University
Advanced Community Information Systems (ACIS) [email protected]
This work by Ralf Klamma is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported.
TeLLNet
Agenda
ACIS
@ R
WTH
Comm
unity
Infor
matio
n Sys
tems
Lear
ning A
nalyt
ics
LA U
se C
ases
Conc
lusion
s & O
utloo
k
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Abstract With the increasing availability of smart phones and tablets as well as
growing mobile bandwidth, mobile learning offers by the means of apps and electronic books become a commodity. In this presentation I motivate by examples that professional communities need learning support beyond the commodity level. Learning analytics in such settings is more than simple assessment strategies but need a deep understanding of interactions between learners and systems, learner and learning resources as well as learners among each others. Such a perspective is delivered by community information systems serving the needs of mobile communities. The meaningful combination of quantitative and qualitative assessment strategies supports the understanding of learner goals, learning processes and community reflection. Case studies from ongoing EU research projects like ROLE, GALA and TELMAP will support the argumentation.
TeLLNet
RWTH Aachen University
• 1,250 spin-off businesses have created around 30,000 jobs in the greater Aachen region over the past 20 years.
• IDEA League
• Germany’s Excellence Initiative: 3 clusters of excellence, a graduate school and the institutional strategy “RWTH Aachen 2020: Meeting Global Challenges”
• 260 institutes in 9 faculties as Europe’s leading institutions for science and research
• Currently around 31,400 students are enrolled in over 100 academic programs
• Over 5,000 of them are international students hailing from 120 different countries
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Responsive Open
Community Information
Systems
Community Visualization
and Simulation
Community Analytics
Community
Support
Web Analytics W
eb E
ngin
eerin
g
Advanced Community Information Systems (ACIS)
Requirements Engineering
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ROLE: Self- and Community Regulated Learning Processes
Based on Fruhmann, Nussbaumer, Albert, 2010
The Horizon Report – 2011 Edition
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Communities of Practice
Community of practice (CoP) as the basic concept for community information systems
Communities of practice are groups of people who share a concern or a passion for something they do and who interact regularly to learn how to do it better (Wenger, 1998)
Usability & sociability (Preece, 2000)
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Learning Analytics Support Interdisciplinary multidimensional model of learning networks
– 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 Wiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat …
i*-Dependencies (Structural, Cross-media)
Members (Social Network Analysis: Centrality,
Efficiency)
network of artifacts Microcontent, Blog entry, Message, Burst, Thread,
Comment, Conversation, Feedback (Rating)
network of members
Communities of practice
Media Networks
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ROLE Social RE – i* Strategic Rationale
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MobSOS: Mobile Service Oracle for Success
Dominik Renzel, Ralf Klamma Semantic Monitoring and Analyzing Context-aware Collaborative Multimedia Services 2009 IEEE International Conference on Semantic Computing, 14-16 September 2009 / Berkeley, CA, USA
Context-Aware Usage/Error Statistics Social Network Analysis Service Quality Analysis Visualizations Set of MobSOS Widgets & Services interactive data mining visualizations
TeLLNet
MediaBase: Cross Media SNA
Collection of Social Software artifacts with parameterized PERL scripts – Blogs & Wikis – Mails & Forums – Web pages
Database support by IBM DB2, eXist, Oracle, ...
Web Interface based on Firefox Plugin, Plone, Drupal, LAS, ... – www.learningfrontiers.eu – www.prolearn-academy.org
Strategies of visualization – Tree maps – Cross-media graphs
Klamma et al.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe, EC-TEL 2006
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Case I: Preparation for English Language Tests
Urch Forums (formerly TestMagic) – Community on preparation for English
language tests – 120,000+ threads, 800,000+ posts,
100,000+ users over 10 years – Social Network Analysis, Machine
Learning and Natural Language Processing
What are the goals of learners? – Intent Analysis (Phases 1 & 2)
What are their expressions? – Sentiment Analysis (Phases 3 & 4)
Refinement – 12881 cliques with avg. size 5 and
avg. occurrence of 14
Thread 1 Thread 2
Thread 3
User of clique Non-clique User in thread Clique-user missing in thread
Time
Petrushyna, Kravcik, Klamma: Learning Analytics for Communities of Lifelong Learners: a Forum Case. ICALT 2011
TeLLNet
Self-Regulated Learning Phases Can Be Observed
1 week / step
Phase 1 and 2 (low sentiment, questioner, lot of intents) Phase 3 (increasing sentiment, conversationalist) Phase 4 (high sentiment, answering person)
Different users
40% of „footprints“ of cliques align with model for phases
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Case II: YouTell - A Web 2.0 Service for Collaborative Storytelling
Collaborative storytelling Web 2.0 Service Story search and “pro-
sumption”
Tagging Ranking/Feedback Expert finding Recommending
Klamma, Cao, Jarke: Storytelling on the Web 2.0 as a New Means of Creating Arts Handbook of Multimedia for Digital Entertainment and Arts, Springer, 2009
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Knowledge-Dependent Learning Behaviour in Communities
Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs, WISMA 2010, Barcelona, Spain, May 19-20, 2010
Expert finding algorithm: Knowledge value of community sorted by keywords Community behaviors: experts spent more time on the services Experts prefers semantic tags while amateurs uses “simple” tags frequently Community tags: experts use more precise tags
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Case III: TeLLNet - SNA for European Teachers‘ Life Long Learning
How to manage and handle 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?
Song, Petrushyna, Cao, Klamma: Learning Analytics at Large: The Lifelong Learning Network of 160, 000 European Teachers. EC-TEL 2011
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Analysis and Visualization of Lifelong Learner Data
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Advanced Community Information Systems
• Network Models
• Network Analysis
• Actor Network Theory
• Communities of Practice
• Game Theory • Community
Detection • Web Mining • Recommender
Systems • Multi Agent
Simulation
Web
Ana
lytics
• Advanced Web & Multimedia Technologies • XMPP • HTML5 • MPEG-7
• Web Services • RESTful • LAS
• Cloud Computing
• Mobile Computing
Web
Eng
ineer
ing
• MediaBase • MobSOS • TellNeT
• Requirements Bazaar
• yFiles SNA • Widgets
• LAS & Services
• youTell
Responsive Open
Community Environments
Community Visualization & Simulation
Community Analytics
Community Support
Social Requirements Engineering
• Agent and Goal Oriented i* Modeling • Participatory Community Design
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Conclusions & Outlook Learning Analytics (LA) in lifelong & mobile learner communities is
based on network and data analysis methods LA framework based on modeling & reflection support
– MediaBase: Data Management for LA – MobSOS: Establishment of LA dashboard and widget collections for
mobile learning communities Case studies
– ROLE: Goal and sentiment mining for self-regulated learners Identification of Learning Phases
– YouTell: Expert vs. amateurs in collaborative storytelling communities Expert Finding Services
– TellNet: Analysis and visualization of large learner networks Performance Indicators and Visual Analytics