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dc.contributor.author | Martínez-Plumed, Fernando | es_ES |
dc.contributor.author | Hernández-Orallo, José | es_ES |
dc.date.accessioned | 2024-05-10T18:06:25Z | |
dc.date.available | 2024-05-10T18:06:25Z | |
dc.date.issued | 2023-08 | es_ES |
dc.identifier.issn | 1932-8540 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/204083 | |
dc.description.abstract | [EN] The concepts of innovation, creativity, problem solving, effective communication, autonomy and critical thinking are at the core of becoming a good data scientist. Adapting to new technological resources and tools is also an important skill, which also builds on the curious and inquisitive nature associated with data science, and is fuelled by rapidly changing data science ecosystems in industry. In this regard, Project-based learning (PBL) has clear benefits for engaging students in data science courses. However, the exploratory character of data science projects, which do not start with a clear specification of what to do, but some data to analyse, pose some challenges to the application of PBL. Our aim is to improve students' data science learning experiences and outcomes through the use of PBL. In this paper, we share our experiences with PBL and present an assessment rubric that focuses on value, innovation and narrative, which can be used as a scaffolding structure for data science courses. Our analysis of a PBL data science course at MSc level, together with data from student surveys, shows how the methodology and rubric align well with the exploratory nature of data science and the proactive, curious, and inquisitive skills required of data scientists. | es_ES |
dc.description.sponsorship | Thiswork was supported in part by valgrAI, the Norwegian Research Council Grant 329745 Machine Teaching for Explainable AI, the Future of LifeInstitute (FLI), under Grant RFP2-152, the EU (FEDER), and Spanish Grant RTI2018-094403-B-C32 through MCIN/AEI/10.13039/501100011033 and in part by CIPROM/2022/6 through Generalitat Valenciana, EU's Horizon 2020 Research and Innovation Programme under Grant 952215 (TAILOR), U.S. DARPA HR00112120007 (RECoG-AI), and Spanish Grant PID2021-122830OB-C42 (SFERA) through MCIN/AEI/10.13039/501100011033 and "ERDF a Way of Making Europe." | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
dc.relation.ispartof | IEEE-RITA: Latin-American Learning Technologies Journal | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Data science | es_ES |
dc.subject | Data mining | es_ES |
dc.subject | Computer science | es_ES |
dc.subject | Business | es_ES |
dc.subject | Technological innovation | es_ES |
dc.subject | Data models | es_ES |
dc.subject | Trajectory | es_ES |
dc.subject | Project based-learning | es_ES |
dc.subject | Assessment tools | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Training Data Scientists Through Project-Based Learning | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1109/RITA.2023.3302954 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094403-B-C32/ES/RAZONAMIENTO FORMAL PARA TECNOLOGIAS FACILITADORAS Y EMERGENTES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-122830OB-C42/ES/METODOS FORMALES ESCALABLES PARA APLICACIONES REALES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/952215/EU/Integrating Reasoning, Learning and Optimization/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//CIPROM%2F2022%2F6//TECNOLOGIAS DE APRENDIZAJE Y RAZONAMIENTO RAPIDO Y LENTO/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/RCN//329745/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/FLI//RFP2-152/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/DOD//HR00112120007/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.description.bibliographicCitation | Martínez-Plumed, F.; Hernández-Orallo, J. (2023). Training Data Scientists Through Project-Based Learning. IEEE-RITA: Latin-American Learning Technologies Journal. 18(3):295-304. https://doi.org/10.1109/RITA.2023.3302954 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1109/RITA.2023.3302954 | es_ES |
dc.description.upvformatpinicio | 295 | es_ES |
dc.description.upvformatpfin | 304 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 18 | es_ES |
dc.description.issue | 3 | es_ES |
dc.relation.pasarela | S\511798 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.contributor.funder | Future of Life Institute | es_ES |
dc.contributor.funder | Research Council of Norway | es_ES |
dc.contributor.funder | U.S. Department of Defense | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |