TY - GEN
T1 - Toward a fully automatic learner modeling based on web usage mining with respect to educational preferences and learning styles
AU - Khribi, Mohamed Koutheair
AU - Jemni, Mohamed
AU - Nasraoui, Olfa
AU - Graf, Sabine
AU - Kinshuk,
PY - 2013
Y1 - 2013
N2 - In this paper, we describe a fully automatic learner modeling approach in learning management systems, taking into account learners' educational preferences including learning styles. We propose a learner model with 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 implies the learner's interests on visited learning objects, and the learner's educational preferences are composed of the learner's preferences among visited learning objects and his/her learning style. In the proposed approach, all learner model components are automatically detected, without requiring explicit feedback. Indeed, 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.
AB - In this paper, we describe a fully automatic learner modeling approach in learning management systems, taking into account learners' educational preferences including learning styles. We propose a learner model with 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 implies the learner's interests on visited learning objects, and the learner's educational preferences are composed of the learner's preferences among visited learning objects and his/her learning style. In the proposed approach, all learner model components are automatically detected, without requiring explicit feedback. Indeed, 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.
KW - Collaborative Filtering
KW - Learner Modeling
KW - Learning Styles
KW - Recommender Systems
KW - Web Mining
UR - http://www.scopus.com/inward/record.url?scp=84885232135&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2013.123
DO - 10.1109/ICALT.2013.123
M3 - Published Conference contribution
AN - SCOPUS:84885232135
SN - 9780769550091
T3 - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
SP - 403
EP - 407
BT - Proceedings - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
T2 - 2013 IEEE 13th International Conference on Advanced Learning Technologies, ICALT 2013
Y2 - 15 July 2013 through 18 July 2013
ER -