Title: Advances in personalised recommendation of learning objects based on the set covering problem using ontology

Authors: Clarivando Francisco Belizário Júnior; Fabiano Azevedo Dorça; Luciana Pereira de Assis; Alessandro Vivas Andrade

Addresses: Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Campus Santa Mônica, Bloco 1B, Sala 1B148, Av. João Naves de Ávila, 2.121, Bairro Santa Mônica, Uberlândia, 38400-902, Minas Gerais, Brazil ' Faculty of Computer Science (FACOM), Federal University of Uberlândia (UFU), Campus Santa Mônica, Bloco 1B, Sala 1B148, Av. João Naves de Ávila, 2.121, Bairro Santa Mônica, Uberlândia, 38400-902, Minas Gerais, Brazil ' Department of Computing, Federal University of Jequitinhonha and Mucuri Valleys (UFVJM), Campus JK. Rodovia MGT 367 – Km 583, No. 5000, Alto da Jacuba, Diamantina, 39100-000, Minas Gerais, Brazil ' Department of Computing, Federal University of Jequitinhonha and Mucuri Valleys (UFVJM), Campus JK. Rodovia MGT 367 – Km 583, No. 5000, Alto da Jacuba, Diamantina, 39100-000, Minas Gerais, Brazil

Abstract: Loop-based intelligent tutoring systems (ITSs) support the learning process using a step-by-step problem-solving approach. A limitation of ITSs is that few contents are compatible with this approach. On the other hand, recommendation systems can recommend different types of content but ignore the fine-grained concepts typical of the step-by-step approach. This work contributes to the solution of this state-of-the-art challenge by proposing an approach for the recommendation of learning objects from different areas of knowledge, considering the refined concepts of ITSs. To deal with this challenge, we formulate the learning object recommendation problem as the set covering problem that belongs to the NP-hard class problems. An exact algorithm and a greedy heuristic were properly adapted, resulting in a promising approach to solve these problems, as shown by the results. This resulted in more personalised content for students using collaborative filtering and an ontology that models their knowledge, learning styles and search parameters.

Keywords: learning objects recommendation; personalised recommendation; collaborative filtering; ontology; set covering; learning styles; intelligent tutoring systems; ITSs.

DOI: 10.1504/IJLT.2024.137898

International Journal of Learning Technology, 2024 Vol.19 No.1, pp.25 - 57

Received: 10 Jun 2022
Received in revised form: 25 Jan 2023
Accepted: 15 Mar 2023
Published online: 08 Apr 2024 *

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