Survey data on Families' perceptions of ed-tech corporations, educational digital platforms and children's rights.

Children's rights Educational digital platforms Survey data Technological corporations

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Apr 2023
Historique:
received: 15 12 2022
revised: 29 01 2023
accepted: 21 02 2023
entrez: 20 3 2023
pubmed: 21 3 2023
medline: 21 3 2023
Statut: epublish

Résumé

This data article describes the dataset of the project "edDIT: Technological corporations, digital educational platforms and guarantee of children's rights with a gender approach". This study has analysed the impact of the use of corporate digital platforms in public schools in Catalonia. A series of data were collected through an online survey, with a total sample of 2347 parents/caregivers. The description of the data contained in this article is divided into two main parts. The first one is a descriptive analysis of all the items included in the survey and has been carried out using tables and figures. The second one refers to the construction of scales. Three scales were constructed and included in the data set: 'Opinions about Educational Digital Platforms', 'Concerns about the use of the data generated on the utilisation of the digital platform' and 'Parental Engagement'. The scales were created using Confirmatory Factor Analysis (CFA) and Multigroup Confirmatory Analysis (MG-CFA). This dataset will be relevant for researchers in different fields, in particular for those interested in digital inclusion public policies and educational policies.

Identifiants

pubmed: 36936640
doi: 10.1016/j.dib.2023.109017
pii: S2352-3409(23)00135-X
pmc: PMC10014256
doi:

Types de publication

Journal Article

Langues

eng

Pagination

109017

Informations de copyright

© 2023 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Références

Data Brief. 2021 Feb 01;35:106813
pubmed: 33604430
MethodsX. 2022 Aug 05;9:101808
pubmed: 36034522

Auteurs

Ainara Moreno-González (A)

Universitat de Barcelona, Spain.

Diego Calderón-Garrido (D)

Universitat de Barcelona, Spain.

Lluís Parcerísa (L)

Universitat de Barcelona, Spain.

Pablo Rivera-Vargas (P)

Universitat de Barcelona and Universidad Andrés Bello (Chile).

Judih Jacovkis (J)

Universitat de Barcelona, Spain.

Classifications MeSH