Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.15/760
Título: A hybrid recommender strategy on an expanded content manager in formal learning
Autor: Madeira, Filipe
Quaresma, Rui
Abreu, S.
Palavras-chave: Personalized Recommender Systems (PRS)
Collaborative filtering
Collaborative learning
Formal learning
Sequencing
Learner profile
Data: Nov-2012
Editora: IATED : International Association of Technology, Education and Development
Citação: MADEIRA, Filipe ; QUARESMA, Rui ; ABREU, S. - A hybrid recommender strategy on an expanded content manager in formal learning. Proceedings of ICERI2012 Conference, 5, Madrid, 2012. ISBN 978-84-616-0763-1. p. 304-314
Resumo: The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones. The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning. One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance. The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design.
Peer review: yes
URI: http://hdl.handle.net/10400.15/760
ISBN: 978-84-616-0763-1
Versão do Editor: http://library.iated.org/view/MADEIRA2012AHY
Aparece nas colecções:Comunicações em conferências e congressos_ESGTS

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