PROJECT GGULIVRR: GENERIC GAME FOR UBIQUITOUS LEARNING IN INTERACTIVE VIRTUAL AND REAL REALITIES

Resume

This work is cataloged in the Scientific Portal. Officially published in EDULEARN20 (2020). Referenced as: "José C. Morais; João Rocha Monteiro; AKERLUND, H; G. Audemard; H. Bollaert; V. Hayenne-Cuvillon; A. Hlobaz; et al".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/doi.org/10.21125/edulearn. 2020

Keywords

Serious Games International Projects.

Bibliographic & Editorial Details

Venue / Journal / Book EDULEARN20

Publisher IATED

Pagination Pages 22899 - 22905

Publication Venue / Conference Palma de Maiorca, Spain

Role Coauthor

Official Identifiers

DOI: doi.org/10.21125/edulearn. 2020

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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Coautores da Obra

Autores discriminados na citação da publicação:

José C. Morais; João Rocha Monteiro; AKERLUND, H; G. Audemard; H. Bollaert; V. Hayenne-Cuvillon; A. Hlobaz; et al

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

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PROJECT GGULIVRR: GENERIC GAME FOR UBIQUITOUS LEARNING IN INTERACTIVE VIRTUAL AND REAL REALITIES 🔓

Resume

Keywords

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

PROJECT GGULIVRR: GENERIC GAME FOR UBIQUITOUS LEARNING IN INTERACTIVE VIRTUAL AND REAL REALITIES 🔓

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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