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Improving the portability of predicting students’ performance models by using ontologies

One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the model...

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Detalles Bibliográficos
Autores principales: López-Zambrano, Javier, Lara, Juan A., Romero, Cristóbal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988260/
https://www.ncbi.nlm.nih.gov/pubmed/33776379
http://dx.doi.org/10.1007/s12528-021-09273-3
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author López-Zambrano, Javier
Lara, Juan A.
Romero, Cristóbal
author_facet López-Zambrano, Javier
Lara, Juan A.
Romero, Cristóbal
author_sort López-Zambrano, Javier
collection PubMed
description One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models’ excessive dependence on the low-level attributes used to train them, which reduces the models’ portability. To solve this issue, the use of high-level attributes with more semantic meaning, such as ontologies, may be very useful. Along this line, we propose the utilization of an ontology that uses a taxonomy of actions that summarises students’ interactions with the Moodle learning management system. We compare the results of this proposed approach against our previous results when we used low-level raw attributes obtained directly from Moodle logs. The results indicate that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. The main contribution of this paper is to show that the ontological models obtained in one source course can be applied to other different target courses with similar usage levels without losing prediction accuracy.
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spelling pubmed-79882602021-03-24 Improving the portability of predicting students’ performance models by using ontologies López-Zambrano, Javier Lara, Juan A. Romero, Cristóbal J Comput High Educ Article One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models’ excessive dependence on the low-level attributes used to train them, which reduces the models’ portability. To solve this issue, the use of high-level attributes with more semantic meaning, such as ontologies, may be very useful. Along this line, we propose the utilization of an ontology that uses a taxonomy of actions that summarises students’ interactions with the Moodle learning management system. We compare the results of this proposed approach against our previous results when we used low-level raw attributes obtained directly from Moodle logs. The results indicate that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. The main contribution of this paper is to show that the ontological models obtained in one source course can be applied to other different target courses with similar usage levels without losing prediction accuracy. Springer US 2021-03-24 2022 /pmc/articles/PMC7988260/ /pubmed/33776379 http://dx.doi.org/10.1007/s12528-021-09273-3 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
López-Zambrano, Javier
Lara, Juan A.
Romero, Cristóbal
Improving the portability of predicting students’ performance models by using ontologies
title Improving the portability of predicting students’ performance models by using ontologies
title_full Improving the portability of predicting students’ performance models by using ontologies
title_fullStr Improving the portability of predicting students’ performance models by using ontologies
title_full_unstemmed Improving the portability of predicting students’ performance models by using ontologies
title_short Improving the portability of predicting students’ performance models by using ontologies
title_sort improving the portability of predicting students’ performance models by using ontologies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7988260/
https://www.ncbi.nlm.nih.gov/pubmed/33776379
http://dx.doi.org/10.1007/s12528-021-09273-3
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