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Static and dynamic user type identification in adaptive e-learning with unsupervised methods

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3 Author(s)
Camelia Lemnaru ; Technical University of Cluj-Napoca, Romania ; Adina Anca Firte ; Rodica Potolea

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.

Published in:

Intelligent Computer Communication and Processing (ICCP), 2011 IEEE International Conference on

Date of Conference:

25-27 Aug. 2011