Modeling MOOC learnflow with Petri net extensions
Abstract
Modern higher education takes advantage of MOOC technology. Modeling an education process of Massive open online courses (MOOCs) as a dynamic and multi-agent process is one of the challenging tasks. In this paper, Petri net extensions are investigated in the context of the learnflow modeling. It is shown how a learnflow can be modeled with classical and Colored Petri nets. These extensions facilitate modeling distributed and multi-agent processes. However, existing Petri net extensions do not provide the ability to model an education process in the context of multi-course programs and adaptive learning. We propose \emph{Petri nets with reference data} (PNRDs) for modeling e-learning in MOOCs. PNRDs allow us to represent a model of the education process in a visual, clear and not overloaded form. Moreover, PNRDs enable us to display aspects of multi-course programs and dynamic changes in the MOOC education process. We also show how PNRDs can be used to model online student collaboration in project-based learning.
Keywords
Cite
@article{arxiv.2111.04419,
title = {Modeling MOOC learnflow with Petri net extensions},
author = {Irina A. Lomazova and Alexey A. Mitsyuk and Aliya M. Sharipova},
journal= {arXiv preprint arXiv:2111.04419},
year = {2021}
}