Toward a Fully Automatic Learner Modeling Based on Web Usage Mining with Respect to Educational Preferences and Learning Styles

Mohamed Koutheair Khribi, Mohamed Jemni, Olfa Nasraoui, Sabine Graf, Kinshuk
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="" style="color: black;">2013 IEEE 13th International Conference on Advanced Learning Technologies</a> </i> &nbsp;
In this paper, we describe a fully automatic learner modeling approach in learning management systems, taking into account the learners' educational preferences including their learning styles. We propose a composite learner model made of three components: the learner's profile, learner's knowledge, and learner's educational preferences. The learner's profile represents the learner's general information such as identification data, the learner's knowledge captures the learner's interests on
more &raquo; ... ted learning objects, and the learner's educational preferences are composed of the learner's preferences (in terms of the specific attributes of the visited learning objects) and his/her learning style. In the proposed approach, all the learner model components are automatically detected, without requiring any explicit feedback. All the basic learners' information is inferred from the learners' online activities and usage data, based on web usage mining techniques and a literature-based approach for the automatic detection of learning styles in learning management systems. Once learner models are built, we apply a hierarchical multi-level model based collaborative filtering approach, in order to gather learners with similar preferences and interests in the same groups.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1109/icalt.2013.123</a> <a target="_blank" rel="external noopener" href="">dblp:conf/icalt/KhribiJNGK13</a> <a target="_blank" rel="external noopener" href="">fatcat:hfrork3dtzbn3himsnoyj2jp6i</a> </span>
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