HOW TWO DIFFERENT LEVEL INSTITUTIONS CAN COOPERATE FOR THE SAME PURPOSE: TO MOTIVATE STUDENTS TO LEARN MATHEMATICS

Resume

This work is cataloged in the Scientific Portal. Officially published in INTED2020 Proceedings (2020). Referenced as: "Babo, Lurdes; Azevedo, José; Torres, Cristina; Lopes, Ana Paula; Camilo, Alice; Melo, Fernando; Garcia, Helena".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.21125/inted.2020.1849

Bibliographic & Editorial Details

Venue / Journal / Book INTED2020 Proceedings

Publisher IATED

Pagination Pages 26984 - 26995

Publication Venue / Conference Valencia, Spain

Official Identifiers

DOI: 10.21125/inted.2020.1849

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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C

Cristina Maria Dias Pereira Torres Investigador

Ciência ID: 111C-A2B8-EBB4

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Coautores da Obra

Autores discriminados na citação da publicação:

Babo, Lurdes; Azevedo, José; Torres, Cristina; Lopes, Ana Paula; Camilo, Alice; Melo, Fernando; Garcia, Helena

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

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HOW TWO DIFFERENT LEVEL INSTITUTIONS CAN COOPERATE FOR THE SAME PURPOSE: TO MOTIVATE STUDENTS TO LEARN MATHEMATICS 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

HOW TWO DIFFERENT LEVEL INSTITUTIONS CAN COOPERATE FOR THE SAME PURPOSE: TO MOTIVATE STUDENTS TO LEARN MATHEMATICS 🔓

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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