Publicação
The application of artificial intelligence in recommendation systems reinforced through assurance of learning in personalized environments of e-Learning
| dc.contributor.author | Bottaro Fresneda, Fernando Cesare | |
| dc.contributor.author | Santos, Arnaldo | |
| dc.contributor.author | Martins, Paulo | |
| dc.contributor.author | Reis, Leonilde | |
| dc.date.accessioned | 2026-02-16T15:52:51Z | |
| dc.date.available | 2026-02-16T15:52:51Z | |
| dc.date.issued | 2024 | en_US |
| dc.date.updated | 2026-02-11T17:01:20Z | |
| dc.description.abstract | Learning environments unquestionably enable learners to develop their pedagogical and scientific processes efficiently and effectively. Thus, con-sidering the impossibility of not having conditions of autonomy over the routine underlying the studies and, consequently, not having guarantees of the learning carried out makes the learners experience gaps in the domain of materials ade-quate to their actual needs. The paper's objective is to present the relevance of the applicability of Artificial Intelligence in Recommendation Systems, reinforced through the Assurance of Learning, oriented towards adaptive-personalized prac-tice in corporate e-learning contexts. The research methodology underlying the work fell on Design Science Research, as it is considered adequate to support the research, given the need to carry out the design phases, development, construc-tion, evaluation, validation of the artefact and, finally, communication of the re-sults. The main underlying results instigate the development of an Adaptive-Per-sonalized Learning framework for corporate e-learning, provided with models (methods and algorithms) of Artificial Intelligence and guided using the Assur-ance of (the) Learning process. It becomes central that learners can enjoy ade-quate academic development. In this sense, the framework has an implicit struc-ture that promotes the definition of personalized attributes, which involves rec-ommendations and customizations of content per profile, including training con-tent that will be suggested and learning activity content that will be continuously monitored, given the specific needs of learners. | eng |
| dc.description.version | N/A | |
| dc.identifier.authenticusid | P-010-2GN | en_US |
| dc.identifier.doi | 10.1007/978-3-031-45645-9_50 | en_US |
| dc.identifier.eid | 2-s2.0-85186760320 | en_US |
| dc.identifier.slug | cv-prod-3852072 | |
| dc.identifier.uri | http://hdl.handle.net/10400.2/21318 | |
| dc.identifier.wos | WOS:001259458100050 | en_US |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.rights.uri | N/A | |
| dc.subject | Artificial intelligence | |
| dc.subject | Recommendation systems | |
| dc.subject | Assurance of learning | |
| dc.subject | Adaptive-personalized learning | |
| dc.subject | Learning analytics | |
| dc.title | The application of artificial intelligence in recommendation systems reinforced through assurance of learning in personalized environments of e-Learning | eng |
| dc.type | book part | en_US |
| dspace.entity.type | Publication | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Bottaro Fresneda | |
| person.familyName | Santos | |
| person.familyName | Martins | |
| person.familyName | Reis | |
| person.givenName | Fernando Cesare | |
| person.givenName | Arnaldo | |
| person.givenName | Paulo | |
| person.givenName | Leonilde | |
| person.identifier | https://scholar.google.pt/citations?user=ik_0xPkAAAAJ&hl=pt-PT | |
| person.identifier.ciencia-id | 3211-438A-A256 | |
| person.identifier.ciencia-id | E614-2ECC-2D48 | |
| person.identifier.ciencia-id | B61D-AF04-BEF3 | |
| person.identifier.orcid | 0000-0002-8340-7900 | |
| person.identifier.orcid | 0000-0001-5139-6728 | |
| person.identifier.orcid | 0000-0002-3040-9080 | |
| person.identifier.orcid | 0000-0002-4398-8384 | |
| person.identifier.rid | L-5119-2014 | |
| person.identifier.scopus-author-id | 24721665200 | |
| rcaap.cv.cienciaid | 3211-438A-A256 | Arnaldo Manuel Pinto dos Santos | |
| rcaap.rights | openAccess | en_US |
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