1 / 16

COLLABORATIVE STORYTELLING IN THE WEB 2.0

COLLABORATIVE STORYTELLING IN THE WEB 2.0. Yiwei Cao, Ralf Klamma, and Andrea Martini Information Systems, RWTH Aachen University In Proceedings of the First International Workshop on Story-Telling and Educational Games (STEG'08) . AGENDA. Introduction State of the Art

milton
Download Presentation

COLLABORATIVE STORYTELLING IN THE WEB 2.0

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. COLLABORATIVE STORYTELLING IN THE WEB 2.0 Yiwei Cao, Ralf Klamma, and Andrea MartiniInformation Systems, RWTH Aachen University In Proceedings of the First International Workshop on Story-Telling and Educational Games (STEG'08) 

  2. AGENDA • Introduction • State of the Art • PESE - Personal Expert finding and Storytelling Environment • Implementation of the PESE Prototype • Evaluation of the PESE Prototype • Conclusionsand Outlook

  3. INTRODUCTION • Prosuming : media comsumers can become media producers • The idea for PESE (Personalized Storytelling Environment) is to combine both worlds, multimedia production knowledge and Web 2.0 production knowledge, shall be united for serving the knowledge sharing needs for a society. • Storytelling is an efficient means to fulfill learninggoals. Knowledge is exchanged within communities when stories are told.

  4. STATE OF THE ART • Non-linear story • a non-linear story can contain many different story paths. So there exist different story versions which can have different ends. • If more than a person is involved in the story creation process we talk about collaborative storytelling. • Digital storytelling • creating a story with help of digital media • can be used in the educational area because facts connected with emotions can be easier stored in the listener’s brain • MIST(media integrated storytelling platform) • Non-linear digital storytelling approach can be used as a cultural heritage learning platform. • a standalone Java application, does not support collaborative storytelling by different users.

  5. STATE OF THE ART • PESE • approach to collaborative storytelling within community of practice using Web 2.0 technologies. • Community of Practice (CoP) • The members are mutual engaged and interact with each other. • Joint enterprises in form of mutual accountability, interpretations. • a shared repertoire which is produced within this community. • Aim is to combine the advantages of a Community of Practice and of Web 2.0.

  6. STATE OF THE ART • Expert finding • describes the process of finding a qualified user in a specific context. This method can be used in learning environments. • system needs to have information • user itself specifies what he knows and what is interesting for the system. • Weight reducing function can be executed

  7. PESE: A WEB 2.0 SERVICE FOR COLLABORATIVE STORYTELLING • PESE enables communities to have joint enterprises ,to build a shared repertoire and to engage mutually. Therefore PESE builds a platform for a community of practice.

  8. PESE—ROLE MODEL • normal user : gets necessary rights to execute basis features like visiting, rating and searching for a story. • Experts : user who give a minimum number of good advices. • PESE technician : can aid users with administrative questions. • Storyteller : know how to tell a thrilling story. • Maven : characterized by good expertise • Administrators : have extended rights and are necessary for maintenance issues. • system admin • Story sheriffs • User admin

  9. PESE—ROLE MODEL • Bandits: is used to classify users. • According to their behavior,user can be a troll, a smurf, a hustler or a munchkin respectively. • Connector : knows many people and has a big contact network • Domain lord : has on the one hand a great expertise and is on the other hand an excellent storyteller • Producer : create, edit and manage stories. • production leader • Author • media producer • Director • Handyman

  10. PESE—TAGGING AND SEARCH • Because the often used plain tagging has many disadvantages , semantic tagging is used. • a profile based search is offered.

  11. PESE—EXPERT FINDING • User Profile: to know which user is the best fitting expert • Story data are generated when a user visits or edits a story. • Expert data are created with given/ received expert advices. • Personal data represent the user knowledge the user has acquired in the real world. These data are typed in by the user itself. • Tag vectors will be weighted summed up and normalized. • The final value of each tag represents the users knowledge assigned to the related tag. A value near to zero implies that the user only knows few, where as a value near 1 implies expertise at this topic.

  12. PESE—FEEDBACK • Feedback delivers fundamental knowledge for executing the profile based search and defining the user’s expert status. • The visualization of feedback results (i.e. average ratings, tag clouds)help user to get an impression of the experts/story’s quality. • feedback techniques • Explicit :fill out a questionnaire. • Implicit : In PESE the following user behavior will be considered • The more one user visits one story the more interesting it is. • The more a story is visited by all users, the more popular it is. • The integrated mailbox service offers the possibility to handle all necessary user interaction of the PESE community. • ask an expert, give an expert advice • invite a new team member, etc.

  13. PESE PROTOTYPE IMPLEMENTATION • PESE is realized as client/server system and is integrated in the LAS system. • The LASserver handles the user management and all database interactions. • New servicesextend the basisLAS features and fulfill all functionality needed by PESE. • The story service for the managementof story projects and searching for stories.(extend from MIST) • The expert service computation and management of the expert data vectors. • The mailbox servicemanages the mailbox system. • The PESE userservice offers the possibility to add and edit user specific data.(Extend from LAS) • 註LAS: A lightweight applicationserver for mpeg-7 services in community engines

  14. PESE PROTOTYPE EVALUATION • The PESE prototype has been installed on an web server and was available for every interested user. • The user were requested to test PESE for one month. At the end of that period they were asked to fill out a questionnaire. • In parallel statistical data about the PESE usage where collected by MobSOS. This is a mobile multimedia testbed and is able to test, evaluate and analyze web services.

  15. PESE PROTOTYPE EVALUATION • How often LAS services have been called. • Some results of the questionnaire

  16. CONCLUSIONS AND OUTLOOK • PESE combines ideas of multimedia production with the Web 2.0 idea of prosuming users. • In particular, PESE presented an integrated user model where one can find classic roles of multimedia production like producers and directors on the one side and Web 2.0 roles like story sheriffs and domain lords on the other side. • planned improvements : • The physical layout of servers for the environment is rather complex and can be simplified. • But also research topics like mobilization of the storytelling system and geo-spatial visualization of storytellers, e.g. for massive multimedia sharing and ubiquitous gaming are on our screen.

More Related