A Petri Net Model for Analysing E-Learning and Learning Difficulties

Full text for this resource is not available from the Research Repository.

Adam, Tas (2011) A Petri Net Model for Analysing E-Learning and Learning Difficulties. International Journal of Actor-Network Theory and Technological Innovation, 3 (4). pp. 11-21. ISSN 1942-535X (print) 1942-5368 (online)

Abstract

Petri Nets are tools for the modelling and analysis of the behaviour of systems and analysis of the Petri Net can then reveal important information about the structure and dynamic behaviour of the modelled system. In this article, the author argues that Petri Net concepts (when used qualitatively) are not fundamentally different from those of ANT. For example, the ‘places’ from Petri Nets bear a strong resemblance to the actors in ANT, and the ‘triggers’ or ‘transitions’, are somewhat analogous to ANT’s translations. In modelling, places represent conditions and transitions represent events. Tokens may model the resources or data items that are associated with a place or places. The original research that this article is based on was undertaken using an actor-network framework to develop a model for e-Learning for students with Learning Difficulties. This article explores the qualitative use of Petri Nets to supplement this ANT treatment.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/9401
DOI https://doi.org/10.4018/jantti.2011100102
Official URL http://www.igi-global.com/gateway/article/60411
Subjects Historical > FOR Classification > 0806 Information Systems
Historical > FOR Classification > 1303 Specialist Studies in Education
Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems
Historical > SEO Classification > 9301 Learner and Learning
Keywords ResPubID24327, e-learning models, learning difficulties, Petri Nets, socio-technical research, systems
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login