Title: PC Med Learner: a personalised and collaborative e-learning materials recommendation system using an ontology-based data matching strategy
Authors: Ioana Ciuciu; Yan Tang Demey
Addresses: Grenoble Laboratory of Computer Science, Joseph Fourier University, 220 rue de la Chimie, BP 53, 38041 Grenoble, France ' Computer Science Department Semantics Technologies & Applications Research Laboratory, Free University of Brussels, 2 Pleinlaan, 1050 Brussels, Belgium
Abstract: It is important, in collaborative learning environments, to understand and assess the intrinsic knowledge of a learner and to share the knowledge within a learning community, in order to improve the learning process. This paper illustrates a framework and a method to recommend learning materials based on the learner's competencies and a domain ontology, in a collaborative setting. The approach is demonstrated in a learning scenario from medical organisations, when training their prentices. It aims at improving the learning processes by making personalised suggestions on the learning materials. The implementation of the system, the Personal and Collaborative Medical Learner (PC Med Learner) contains three main components: 1) a collaborative knowledge base; 2) an information visualisation tool; 3) an ontology-based data matching strategy, providing the evaluation methodology. Our approach can be adapted by corporate and educational organisations from various application domains, although we select the medical domain for the paper demonstration.
Keywords: collaborative learning; e-learning; electronic learning; evaluation; human-computer interaction; HCI; intelligent tutoring systems; ITS; personalised recommendations; ontology; data matching; recommender systems; intrinsic knowledge; recommended learning materials; medical learning; healthcare training; information visualisation.
International Journal of Knowledge and Learning, 2014 Vol.9 No.3, pp.194 - 218
Received: 06 Nov 2012
Accepted: 29 Mar 2013
Published online: 20 Apr 2015 *