Providing a Common Approach to Designing Dataset-Based Learning Activities Based on a Literature Review

Díaz-Pérez, Lucía (Universidad de Zaragoza) ; Lopez-Pellicer, Francisco J. (Universidad de Zaragoza) ; Brufau, Pilar (Universidad de Zaragoza) ; Lacasta, Javier (Universidad de Zaragoza) ; Trillo-Lado, Raquel (Universidad de Zaragoza) ; Yagüe-Fabra, José A. (Universidad de Zaragoza) ; Zarazaga-Soria, Francisco Javier (Universidad de Zaragoza)

Resumen: While there are references available in the literature regarding learning experiences with Dataset-Based Learning (DBL) approaches, there is a noticeable absence of a standardized model for designing DBL activities. This gap was identified in this work after performing a systematic literature review (SLR). In contrast to other active learning methodologies, the lack of a common framework for the DBL methodology makes it challenging to compare different DBL approaches. This paper highlights the knowledge gap in the methodology for designing DBL activities and aims to provide a common approach for sharing the view and details about what DBL entails in higher education and how to design a DBL activity. Additionally, we illustrate these concepts with three case studies in different engineering fields. Based on the SLR results and the review of additional literature, this work defines DBL as an active teaching methodology that focuses on using datasets to promote the learning and understanding of specific concepts and skills. These datasets should contain real data presented in different formats. As a common starting point, in a DBL lesson, the dataset not only provides information and context in the activity statement but also serves as the material to work with, and the solution to the activity is entirely extracted from the information contained in the dataset.

Idioma: Inglés

DOI: 10.3390/app132312704

Año: 2023

Publicado en: Applied Sciences (Switzerland) 13, 23 (2023), 12704 [25 pp.]
ISSN: 2076-3417

Factor impacto JCR: 2.5 (2023)

Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 44 / 181 = 0.243 (2023) - Q1 - T1
Categ. JCR: PHYSICS, APPLIED rank: 87 / 179 = 0.486 (2023) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 115 / 231 = 0.498 (2023) - Q2 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 258 / 439 = 0.588 (2023) - Q3 - T2

Factor impacto CITESCORE: 5.3 - Engineering (all) (Q1) - Computer Science Applications (Q2) - Materials Science (all) (Q2) - Fluid Flow and Transfer Processes (Q2) - Instrumentation (Q2) - Process Chemistry and Technology (Q3)

Factor impacto SCIMAGO: 0.508 - Engineering (miscellaneous) (Q2) - Fluid Flow and Transfer Processes (Q2) - Materials Science (miscellaneous) (Q2) - Instrumentation (Q2) - Process Chemistry and Technology (Q3) - Computer Science Applications (Q3)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T21-23R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T32-23R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T56-23R
Financiación: info:eu-repo/grantAgreement/ES/DGA/T59-23R
Financiación: info:eu-repo/grantAgreement/EUR/ERASMUS+KA226/2020-1-IT02-KA226-HE-095144

Tipo y forma: Article (Published version)

Área (Departamento): Área Mecánica de Fluidos (Dpto. Ciencia Tecnol.Mater.Fl.)
Área (Departamento): Área Ing. Procesos Fabricación (Dpto. Ingeniería Diseño Fabri.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)