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On the use of case-based planning for e-learning personalization
Authors
Alepis
Anaya
+57 more
Antonio Garrido
Backstrom
Baldiris
Bonisoli
Borrajo
Brusilovsky
Campagni
Caputi
Castillo
Champiri
Chen
Chrysafiadi
Clemente
Comi
Comi
de Oliveira
Essalmi
Felder
Fox
Garrido
Garrido
Garrido
Garrido
Garrido
Garrido
Garruzzo
Garruzzo
Gaudioso
Gerevini
Gerevini
Gerevini
Gerevini
Idris
Ivan Serina
Jeremic
Kurilovas
Kurilovas
Kyriacou
Leake
Liberatore
Lluvia Morales
Martinez
Messina
Morales
Natek
Nguyen
Park
Pena-Ayala
Polsani
Rosaci
Serina
Smyth
Spalazzi
Tonidandel
Ullrich
Vidal
Yang
Publication date
1 January 2016
Publisher
'Elsevier BV'
Doi
Cite
Abstract
This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 60, 1-15, 2016. DOI:10.1016/j.eswa.2016.04.030In this paper we propose myPTutor, a general and effective approach which uses AI planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students’ requirements. myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium/large e-learning communities. Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process. © 2016 Elsevier Ltd. All rights reserved.This work has been partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01, the MINECO and FEDER project TIN2014-55637-C2-2-R, the Mexican National Council of Science and Technology, the Valencian Prometeo project II/2013/019 and the BW5053 research project of the Free University of Bozen-Bolzano.Garrido Tejero, A.; Morales, L.; Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications. 60:1-15. https://doi.org/10.1016/j.eswa.2016.04.030S1156
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