Authors:
Christos Athanasiadis
;
Enrique Hortal
;
Dimitrios Koutsoukos
;
Carmen Zarco Lens
and
Stylianos Asteriadis
Affiliation:
Maastricht University, Netherlands
Keyword(s):
Computer-based Education, E-learning, Personalization, Collaborative Filtering, Association Rules, Recommendation Systems.
Related
Ontology
Subjects/Areas/Topics:
Computer-Supported Education
;
e-Learning
;
e-Learning Hardware and Software
Abstract:
The growing prevalence of Internet during the last decades has made e-learning systems and Computer-based
Education (CBE) widely accessible to a great amount of people with different backgrounds and competences.
Due to these rapid advances in computer technologies, there has been a great shift from conventional, low interaction
and printed learning content to high-level, computerized interactions for Computer-based Education.
The above has led to the need for personalized systems, able to adapt their content for a variety of learner’s
abilities and skills. A key factor in content personalization is the degree to which the material itself keeps
learners engaged over the course of the interaction: a CBE system has to cater for enough flexibility and be
endowed with the ability to infer the degree to which the learner is engaged in the interaction and also be in
the position to take decisions regarding the triggering of those adaptation mechanics that will keep the learner
in a
state of high engagement, maximizing, thus, the knowledge acquisition. A straightforward approach in
content adaptation is the monitoring of levels of engagement, frustration and boredom in a learner and the
subsequent adaptation of challenge levels imposed by the learning material. In this paper, we investigate the
use of Collaborative Filtering, in order to build a content adaptation mechanism, based on recommendations
on learner affect states. We showcase results on an interface developed specifically for the purposes of this
research. The system’s objective is to offer optimized sessions to the learners and improve their knowledge
acquisition during the interaction with the system.
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