Improving the Portability of Predicting Students' Performance Models by Using Ontologies

Autor/inn/en	López-Zambrano, Javier; Lara, Juan A.; Romero, Cristóbal
Titel	Improving the Portability of Predicting Students' Performance Models by Using Ontologies
Quelle	In: Journal of Computing in Higher Education, 34 (2022) 1, S.1-19 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Romero, Cristóbal)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1042-1726
DOI	10.1007/s12528-021-09273-3
Schlagwörter	Learning Analytics; Prediction; Models; Semantics; Taxonomy; Integrated Learning Systems; Accuracy; Technology Transfer + Suchen Sie Ihr Suchwort? Vorhersage; Analogiemodell; Semantik; Taxonomie; Technologietransfer
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01