Conceptions on higher education students about use of technologies in the learning process: a comparative study

Resume

This work is cataloged in the Scientific Portal. Officially published in INTED2022 Conference (2022). Referenced as: "Justino Lourenço; José Morais; João Monteiro; Santos, José Duarte; Nelson Neves; Susana Sá".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.21125/inted.2022.1038

Bibliographic & Editorial Details

Venue / Journal / Book INTED2022 Conference

Pagination Pages 14113 - 14128

Role Coauthor

Official Identifiers

DOI: 10.21125/inted.2022.1038

Autores & Descoberta de Ciência ID

Identificação dos autores da instituição e deteção de coautores via FCT Ciência Vitae.

Autor(es) da Instituição

J

José Carlos Pereira de Morais Investigador

Ciência ID: D412-2DF0-6747

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J

José Duarte da Rocha Santos Investigador

Ciência ID: 811F-A8A7-0016

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S

Susana Emília Vaz Oliveira e Sá Investigador

Ciência ID: 8C1F-A213-2E27

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Coautores da Obra

Autores discriminados na citação da publicação:

Justino Lourenço; José Morais; João Monteiro; Santos, José Duarte; Nelson Neves; Susana Sá

Clique no botão acima para consultar a API da FCT e descobrir os perfis Ciência ID dos coautores.

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Conceptions on higher education students about use of technologies in the learning process: a comparative study 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Conceptions on higher education students about use of technologies in the learning process: a comparative study 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Other Related Publications

AI-Driven Predictive Model for Diabetes Risk Assessment - A Class-Balanced Machine Learning Approach with Web-Based Deployment

SiSchool: A QR-Based Mobile Device Control System for Educational Environments

From predicting dropout rates to responsible intervention: explainable machine learning for academic success in higher education

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