Collaborative Storytelling in the Web 2.0 - Presentation Transcript
Collaborative Storytelling in the Web 2.0 Yiwei Cao , Ralf Klamma, and Andrea Martini Informatik 5 (Information Systems), RWTH Aachen University 16.09.2008 Maastricht, the Netherlands First Workshop on Story-Telling and Educational Gaming (STEG ‘08)
Agenda
Introduction
State of the Art: Web 2.0 and Community of Practice
PESE - Personal Expert finding and Storytelling Environment
PESE as Evolution of MIST into the Web 2.0
The PESE Concept
The PESE Story
The Profile Based Story Search
Implementation of the PESE Prototype
Evaluation of the PESE Prototype
Conclusions
Introduction - Motivation
New models of participation on the Web 2.0
Flickr.com, YouTube – the multimedia Web 2.0
Prosumers – amateurs and experts
From storytelling to educational gaming
Stories lay the foundation for successful games
Emotional identification of listeners/gamers
Presented at ICWL 2008: M. Spaniol, Y. Cao, R. Klamma, P. Moreno-Ger, B. Fernándaz Manjón, J. Luis Sierra, G. Toubekis: From Story-Telling to Educational Gaming: The Bamiyan Valley Case , in: Proceedings of 7th ICWL, Jinhua, China, August, 2008, pp. 253-264
State of the Art: Web 2.0 and “Communities of Practice” Web 2.0 “ The long tail” Collective intelligence Web as a platform [O‘ Reilly 05] Data is the next Intel Inside Users add value Cooperate, don’t control Some rights reserved Beyond a single device Expert finding Community of Practice Storytelling
Del.icio.us
Digg
Wikipedia
Amazon
eBay
iGoogle
YouTube
Facebook
MySpace
[Wenger 98] Mutual engagement A joint enterprise A shared repertoire Web as a platform “ The long tail” Collective intelligence
From MIST to PESE
Creation and consumption of non-linear digital stories
Problems
Single-User
No feedback mechanism
Standalone installation on the client side
MIST – Media Integrated Story-Telling [Spaniol et al. 06]
PESE: A Web 2.0 Service for Collaborative Storytelling
Concept of PESE MIST PESE Collaborative Storytelling Ranking Expert finding Search and consumption of stories
The PESE Story
Extending the MIST story
Using CSU (Central Story Unit)
Annotating stories by various users
Rating stories by answering different questions
Components of a story project
Begin and End
Team member with different production roles
Stories and associated keywords
Profile Based Story Search
Implementation of the PESE Prototype
Service oriented architecture
The LAS Framework
Evaluation of the PESE Prototype
Test and evaluation of the questionnaires
Evaluation of the profile based story search
Evaluation of the expert finding algorithms
Test bed: MobSOS: data collection [Klamma et al. 08]
Use SPSS: relevant data analysis and evaluation
Storytelling Expert Finding
New Measure for Knowledge in a Community
Expert value Mean: 0,2624 # Entries: 99.778 Frequency
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