Data analysis and learning analytics for measure effects of gamification in a math online project

Resume

This work is cataloged in the Scientific Portal. Officially published in {INTED (2017). Referenced as: "Lopes, Ana Paula; babo, lurdes; Azevedo, Jose Manuel; Torres, Cristina; Ana Paula Lopes; Lurdes Babo; José Azevedo; Cristina Torres".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.21125/inted.2017.1896

Keywords

Learning Analytics E-Learning Student engagement Moodle Gamification

Bibliographic & Editorial Details

Venue / Journal / Book {INTED

Publisher {IATED

Official Identifiers

DOI: 10.21125/inted.2017.1896

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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J

José Manuel Monteiro Lopes de Azevedo Investigador

Ciência ID: D412-FBB4-852A

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Coautores da Obra

Autores discriminados na citação da publicação:

Lopes, Ana Paula; babo, lurdes; Azevedo, Jose Manuel; Torres, Cristina; Ana Paula Lopes; Lurdes Babo; José Azevedo; Cristina Torres

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Data analysis and learning analytics for measure effects of gamification in a math online project 🔓

Resume

Keywords

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Data analysis and learning analytics for measure effects of gamification in a math online project 🔓

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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