Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for Mathematics

Resume

This work is cataloged in the Scientific Portal. Officially published in 9th International Conference on Computer Supported Education (2017). Referenced as: "Oliveira, Marisa; Barreiras, Alcinda; Marcos, Graça; Ferreira, Hermínia; Azevedo, Ana; Vaz de Carvalho, Carlos".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.5220/0006389806310638

Bibliographic & Editorial Details

Venue / Journal / Book 9th International Conference on Computer Supported Education

Publisher SCITEPRESS - Science and Technology Publications

Pagination Pages 631 - 638

Role Coauthor

Official Identifiers

DOI: 10.5220/0006389806310638

ISBN: 978-989-758-239-4

ISSN: 2184-5026

Autores & Descoberta de Ciência ID

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

Autor(es) da Instituição

A

Ana Isabel Rojão Lourenço Azevedo CEOS.PP

Ciência ID: D913-646F-CE08

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Coautores da Obra

Autores discriminados na citação da publicação:

Oliveira, Marisa; Barreiras, Alcinda; Marcos, Graça; Ferreira, Hermínia; Azevedo, Ana; Vaz de Carvalho, Carlos

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

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Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for Mathematics 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Collecting and Analysing Learners Data to Support the Adaptive Engine of OPERA, a Learning System for 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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