Steg08 Ckma08

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Published on September 22, 2008

Author: yiwei_cao

Source: slideshare.net

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Collaborative Storytelling in the Web 2.0 by Yiwei Cao, Ralf Klamma and Andrea Martini at STEG08 i.c.w. EC-TEL08

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

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

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

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

Del.icio.us

Digg

Wikipedia

Amazon

eBay

iGoogle

YouTube

Facebook

MySpace

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]

Creation and consumption of non-linear digital stories

Problems

Single-User

No feedback mechanism

Standalone installation on the client side

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

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

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

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

New Measure for Knowledge in a Community

Story-tellling Expert Finding Keywords Expert values Knowledge value of community sorted by keywords # Recommendations Expert Amateur

Knowledge value of community sorted by keywords

Conclusions Realization and evaluation of a Web 2.0 style storytelling environment for Communities of Practice YouTube like Web interface Integration of MIST as story editor and player Profile-based story search Expert finding Successful Evaluation Application of MobSOS Community System Success Model New measure for expert values in Communities of Practice Future Steps Improvements of performance and usability Use as a learning service in the ROLE project Integration with game engines for user generated games

Realization and evaluation of a Web 2.0 style storytelling environment for Communities of Practice

YouTube like Web interface

Integration of MIST as story editor and player

Profile-based story search

Expert finding

Successful Evaluation

Application of MobSOS Community System Success Model

New measure for expert values in Communities of Practice

Future Steps

Improvements of performance and usability

Use as a learning service in the ROLE project

Integration with game engines for user generated games

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