Strategies to assess web resources credibility: results of a case study in primary and secondary schools from Portugal

Resume

This work is cataloged in the Scientific Portal. Officially published in 1st European Conference on Information Literacy - ECIL 2013 (2013). Referenced as: "Terra, Ana Lúcia; Sá, Salvina".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.1007/978-3-319-03919-0_66

Keywords

Information credibility Information evaluation Young information behavior Information literacy

Bibliographic & Editorial Details

Venue / Journal / Book 1st European Conference on Information Literacy - ECIL 2013

Publisher Springer

Pagination Pages 1170 - 1176

Publication Venue / Conference Istambul, Turkey

Role Coauthor

Official Identifiers

DOI: 10.1007/978-3-319-03919-0_66

ISSN: 1865-0929

Autores & Descoberta de Ciência ID

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

Autor(es) da Instituição

A

Ana Lúcia Silva Terra Investigador

Ciência ID: BF11-FB23-53A5

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Coautores da Obra

Autores discriminados na citação da publicação:

Terra, Ana Lúcia; Sá, Salvina

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Strategies to assess web resources credibility: results of a case study in primary and secondary schools from Portugal 🔓

Resume

Keywords

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Strategies to assess web resources credibility: results of a case study in primary and secondary schools from Portugal 🔓

Resume

Keywords

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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