

<?xml version="1.0" encoding="UTF-8"?>
<record>
  <title>Development of an Adaptive Collaborative Serious Game Based on Learning Style, Using Trace and Agent Technology</title>
  <journal>Journal of Digital Information Management</journal>
  <author>Hadya Boufera, Fatima Bendella, Karim Sehaba</author>
  <volume>16</volume>
  <issue>3</issue>
  <year>2018</year>
  <doi>https://doi.org/10.6025/jdim/2018/16/3/127-135</doi>
  <url>http://dline.info/fpaper/jdim/v16i3/jdimv16i3_3.pdf</url>
  <abstract>In the collaborative serious game, adaptation
is one of the main factors could be considered in the
phase of conception since they can contribute to improve
learning performance. Learning styles present one of the
most factors that affect learning outcomes. Game-based
learning (GBL) that takes into account learning styles in
the process of adaptation, it furnishes to learners a greater
benefit owing in the personalization of learning and
contents materials according to their proper learning styles.
In this paper, Adaptive system that personalize a
collaborative serious game based on learning style
proposed by Felder and Silverman is described. This
system used trace and agent technology. A model of trace
and group model are developed to extract adaptation
knowledge in order to form plan that will be executed by
Npc player. To evaluate a system, an experiment has been
conducted on group of students. From the experimental
results, it is concluded that the adaptive collaborative
serious game based on learning style improve learning
outcomes and effectiveness, and promote learning
motivation.</abstract>
</record>
