TeLLNet

Learning Analytics in a Mobile World A Community Information

Systems Perspective

Ralf Klamma RWTH Aachen University

Advanced Community Information Systems (ACIS) klamma@dbis.rwth-aachen.de

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

TeLLNet

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

TeLLNet

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

TeLLNet

ROLE: Self- and Community Regulated Learning Processes

Based on Fruhmann, Nussbaumer, Albert, 2010

The Horizon Report – 2011 Edition

TeLLNet

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)

TeLLNet

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

TeLLNet

ROLE Social RE – i* Strategic Rationale

TeLLNet

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

TeLLNet

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

TeLLNet

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

TeLLNet

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

TeLLNet

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

TeLLNet

Analysis and Visualization of Lifelong Learner Data

TeLLNet

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

TeLLNet

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