learning analytics for the lifelong long tail learner

Lehrstuhl Informatik 5(Informationssysteme)

Prof. Dr. M. JarkeI5-KL-111010-1

TeLLNet

GALA Learning Analytics for the Lifelong Long Tail Learner

Ralf KlammaRWTH Aachen University

Informatik 5 (DBIS)RWTH Aachen University

CELSTEC, Heerlen, The NetherlandsFebruary 24, 2011



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Agenda

Lear

ning A

nalyt

ics

ROLE

YouT

ell

AERC

S

TELL

NET

Conc

lusion

s and

Outl

ook



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Self- and Community Regulated Learning Processes

Based on [Fruhmann, Nussbaumer & Albert, 2010]

The Horizon Report – 2011 Edition



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Learning Communities: The Long Tail & Fragments

The Web is a scale-free, fragmented network– The power law (Pareto-Distribution etc.)– 95 % of users are located in the Long Tail (Communities)– Trust and passion based cooperation

IslandTendrils

IN Continent Central Core OUT Continent

Tunnels

[Barabasi, 2002]

[Anderson, 2006]



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Learning Analytics Support Interdisciplinary multidimensional model of learning networks

– Social network analysis (SNA) is defining measures for social relations

– Actor network theory (ANT) is connecting human and media agents– i* framework is defining strategic goals and dependencies– Theory of media transcriptions is studying cross-media knowledge

social softwareWiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat …

i*-Dependencies(Structural, Cross-media)

Members(Social Network Analysis: Centrality,

Efficiency)

network of artifactsMicrocontent, Blog entry, Message, Burst, Thread,

Comment, Conversation, Feedback (Rating)

network of members

Communities of practice

Media Networks



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MediaBase Collection of Social Software

artifacts with parameterized PERL scripts– Mailing lists– Newsletter– Web sites– RSS Feeds– Blogs

Database support by IBM DB2, eXist, Oracle, ...

Web Interface based on Firefox Plugin, Plone/Zope, Widgets, ...

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 forEnglish 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– Cliques are users who appear in

several threads together– 12881 cliques with avg. size 5 and

avg. occurrence of 14

Thread 1 Thread 2

Thread 3

User of cliqueNon-clique User in threadClique-user missing in thread

Time



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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 ArtsHandbook of Multimedia for Digital Entertainment and Arts, Springer, 2009



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Knowledge-DependentLearning 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: AERCS - Recommendation of Venues for Young Computer Scientists

DBLP (http://www.informatik.uni-trier.de/~ley/db/)

- 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 timesPham, Klamma, Jarke: Development of Computer Science Disciplines – A Social Network Analysis Approach, submitted to SNAM, 2011



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Properties of Collaboration and Citation Graphs of Venues



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Case IV: 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?



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Analysis and Visualization ofLifelong Learner Data

Performance Data on Projects Network Structures and Patterns



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Conclusions & Outlook Learning Analytics (LA) in lifelong learner communities is based on

network and data analysis methods LA framework based on modeling & reflection support Four case studies

– ROLE: Goal and sentiment mining for self-regulated learners Identification of Learning Phases

– YouTell: Expert vs. amateurs in collaborative storytelling communitiesExpert Finding Services

– AERCS: Recommendation services based on network analysisRecommendation Services

– TellNet: Analysis and visualization of large learner networksPerformance Indicators and Visual Analytics

Establishment of LA dashboard and widget collections for learning communities