PERCEPTIONS, BELIEFS, AND ATTITUDES OF FIRST YEAR THIRD-LEVEL STUDENTS: AN EMPIRICAL STUDY OF PORTUGUESE, RUSSIAN, POLISH, FINNISH, AND IRISH STUDENTS

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This work is cataloged in the Scientific Portal. Officially published in 12th International Technology, Education and Development Conference (2018). Referenced as: "Brown, Ken; Lopes, Ana Paula; Soares, Filomena; Larionova, Viola; Cellmer, Anna; Hurme, Jarkko Hurme; Brown, K.; et al".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.21125/inted.2018.0301

Bibliographic & Editorial Details

Venue / Journal / Book 12th International Technology, Education and Development Conference

Publisher IATED

Official Identifiers

DOI: 10.21125/inted.2018.0301

ISSN: 2340-1079

Autores & Descoberta de Ciência ID

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

Autor(es) da Instituição

A

Ana Paula Ferreira Fernandes Lopes Investigador

Ciência ID: 8B17-B2C1-6036

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F

Filomena Maria da Silva Pereira Baptista Soares Investigador

Ciência ID: 661E-1FDB-58FB

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Coautores da Obra

Autores discriminados na citação da publicação:

Brown, Ken; Lopes, Ana Paula; Soares, Filomena; Larionova, Viola; Cellmer, Anna; Hurme, Jarkko Hurme; Brown, K.; et al

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

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PERCEPTIONS, BELIEFS, AND ATTITUDES OF FIRST YEAR THIRD-LEVEL STUDENTS: AN EMPIRICAL STUDY OF PORTUGUESE, RUSSIAN, POLISH, FINNISH, AND IRISH STUDENTS 🔓

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Bibliographic & Editorial Details

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Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

PERCEPTIONS, BELIEFS, AND ATTITUDES OF FIRST YEAR THIRD-LEVEL STUDENTS: AN EMPIRICAL STUDY OF PORTUGUESE, RUSSIAN, POLISH, FINNISH, AND IRISH STUDENTS 🔓

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

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

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