Discovering Student Preferences in E-Learning

Nowadays modeling user's preferences is one of the most challenging tasks in e-learning systems that deal with large volumes of information. The growth of on-line educational resources including encyclopaedias, repositories, etc., has made it crucial to "filter" or "sort" the information shown to the student, so that he/she can make a better use of it. To find out the student's preferences, a commonly used approach is to implement a decision model that matches some relevant characteristics of the learning resources with the student's learning style. The rules that compose the decision model are, in general, deterministic by nature and never change over time. In this paper, we propose to use adaptive machine learning algorithms to learn about the student's preferences over time. First we use all the background knowledge available about a particular student to build an initial decision model based on learning styles. This model can then be fine-tuned with the data generated by the student's interactions with the system in order to reflect more accurately his/her current preferences.