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On the Use of Bayesian Probabilistic Matrix Factorization for Predicting Student Performance in Online Learning Environments

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dc.contributor.author Kim, Jinho es_ES
dc.contributor.author Park, Jung Yeon es_ES
dc.contributor.author Van den Noortgate, Wim es_ES
dc.date.accessioned 2020-06-08T09:05:05Z
dc.date.available 2020-06-08T09:05:05Z
dc.date.issued 2020-05-04
dc.identifier.isbn 9788490488119
dc.identifier.issn 2603-5871
dc.identifier.uri http://hdl.handle.net/10251/145618
dc.description.abstract Thanks to the advances in digital educational technology, online learning (or e-learning) environments such as Massive Open Online Course (MOOC) have been rapidly growing. In the online educational systems, however, there are two inherent challenges in predicting performance of students and providing personalized supports to them: sparse data and cold-start problem. To overcome such challenges, this article aims to employ a pertinent machine learning algorithm, the Bayesian Probabilistic Matrix Factorization (BPMF) that can enhance the prediction by incorporating background information on the side of students and/or items. An experimental study with two prediction settings was conducted to apply the BPMF to the Statistics Online data. The results shows that the BPMF with using side information provided more accurate prediction in the performance of both existing and new students on items, compared to the algorithm without using any side information. When the data are sparse, it is demonstrated that a lower dimensional solution of the BPMF would benefit the prediction accuracy. Lastly, the applicability of the BPMF to the online educational systems were discussed in the context of educational assessment. es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof 6th International Conference on Higher Education Advances (HEAd'20)
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Higher Education es_ES
dc.subject Learning es_ES
dc.subject Educational systems es_ES
dc.subject Teaching es_ES
dc.subject Digital educational technology es_ES
dc.subject Online learning es_ES
dc.subject Online educational System es_ES
dc.subject Machine learning es_ES
dc.subject Bayesian Probabilistic Matrix Factorization es_ES
dc.subject Student performance prediction es_ES
dc.title On the Use of Bayesian Probabilistic Matrix Factorization for Predicting Student Performance in Online Learning Environments es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/HEAd20.2020.11137
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Kim, J.; Park, JY.; Van Den Noortgate, W. (2020). On the Use of Bayesian Probabilistic Matrix Factorization for Predicting Student Performance in Online Learning Environments. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):751-759. https://doi.org/10.4995/HEAd20.2020.11137 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename Sixth International Conference on Higher Education Advances es_ES
dc.relation.conferencedate Junio 02-05,2020 es_ES
dc.relation.conferenceplace València, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/HEAD/HEAd20/paper/view/11137 es_ES
dc.description.upvformatpinicio 751 es_ES
dc.description.upvformatpfin 759 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.issue 30-05-2020
dc.relation.pasarela OCS\11137 es_ES


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