RTU Research Information System
Latviešu English

Publikācija: Modeling of Sharing E-Learning Resources Using Collaborative Tagging Services

Publication Type Full-text conference paper published in other conference proceedings
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language Modeling of Sharing E-Learning Resources Using Collaborative Tagging Services
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Daiga Bekere
Sarma Cakula
Keywords collaborative tagging, knowledge item, tagging algorithm, tagging service
Abstract E-learning resources are developed by people having very different agendas. Without common metainformation framework it is quite impossible to find them across organizations. This paper focused on collaboration among servers containing E-learning resources in different educational institutions for providing sharing and reusing of E-learning materials. The goal of the paper is to model collaboration between distributed tagging services, storing knowledge items such as bookmarks or index-cards and promote sharing and reusing of them. This paper considers a case when each service is situated separately – in different educational institutions, so each service is used by certain user community. The research idea is to use self learning SVM (Support Vector Machine) based algorithm and graph theory for tagging service collaboration. Users should retain freedom to classify E-learning materials as they see fit, but they may benefit of being nudged in the right direction, e.g. given prompts about possible annotations and warned about mistakes or misspellings. Model has short-range tested in real environment.
Reference Bekere, D., Cakula, S. Modeling of Sharing E-Learning Resources Using Collaborative Tagging Services. In: Proceedings of CSMW 2009, Austria, Klagenfurt, 9-10 April, 2009. Klagenfurt: CSMW, 2009, pp.13-20. ISBN 978-3-9500593-4-2.
ID 6856