Title :
Static and dynamic user type identification in adaptive e-learning with unsupervised methods
Author :
Lemnaru, Camelia ; Firte, Adina Anca ; Potolea, Rodica
Author_Institution :
Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania
Abstract :
A key factor in modern e-learning systems is the correct identification of the user learning style, to provide appropriate content presentation to each individual user. Moreover, a continuous user monitoring is essential in assessing the progress made during the learning process and controlling the desired evolution. In this paper we present a strategy for integrating the static and the dynamic user models, in a previously proposed e-learning system. Also, we assess the static user models through unsupervised learning techniques and establish that a 3-type model is more appropriate, validating previous analyses performed by a domain expert.
Keywords :
computer aided instruction; unsupervised learning; 3-type model; adaptive e-learning; dynamic user type identification; static user type identification; unsupervised learning techniques; user learning style; Adaptation models; Adaptive systems; Analytical models; Data models; Electronic learning; Face;
Conference_Titel :
Intelligent Computer Communication and Processing (ICCP), 2011 IEEE International Conference on
Conference_Location :
Cluj-Napoca
Print_ISBN :
978-1-4577-1479-5
Electronic_ISBN :
978-1-4577-1481-8
DOI :
10.1109/ICCP.2011.6047838