Wiki Course Builder, a System for Managing and Sharing Didactic Material and Concept Maps Cover Image

Wiki Course Builder, a System for Managing and Sharing Didactic Material and Concept Maps
Wiki Course Builder, a System for Managing and Sharing Didactic Material and Concept Maps

Author(s): Carlo De Medio, Carla Limongelli, Fabio Gasparetti, Filippo Sciarrone, Giovanni Adorni, Frosina Koceva, Ilaria Torre
Subject(s): Social Sciences, Education, Higher Education
Published by: European Distance and E-Learning Network
Keywords: New ICT and media applications in learning; Open content and resources

Summary/Abstract: In this article we present an evolution of Wiki course Builder, a system for building courses, sharing and sequencing learning material taken from Wikipedia pages. The system has been expanded through the implementation of two new modules to provide teachers with different tools that optimize the course creation process. Taking advantage of the user model implemented in the system starting from Grasha’s teaching styles, we have implemented a module that visualizes the graph of all the courses created by the teaching community and makes it possible to compare it with others. This graph can be filtered by macro categories of arguments (e.g. History, Philosophy...) and by teacher archetypes (e.g. expertize delegator ...). The second module is a graphical interface that makes it possible to design and build concept maps for the generation of different courses (alternative learning paths through the map). The comparison between these maps will enrich the model of the teacher who will receive recommendations more refined on the basis of the course method you prefer to make. For the future it will be interesting to extend the user model through the comparison of the concept maps generated by the creation of the course and the clustering of the teachers on the basis of this data. Furthermore, the study of the density of concepts within the materials and the complexity of learning difficulties would complete the user model by optimizing again the recommendation process.

  • Issue Year: 2018
  • Issue No: 2
  • Page Range: 93-99
  • Page Count: 7
  • Language: English