Levantamento de Requisitos para Jogos Educativos Infantis
September 22, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Clara Andrade Pimentel, Philipe de Freitas Melo, MarΓlia Lyra Bergamo
arXiv ID
1909.09926
Category
cs.HC: Human-Computer Interaction
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The use of games within the classroom is in constant growing, however, there are still demands for better solutions to problems such as reconciling leisure with class or the lack of attention of children. This work demonstrates methodologies used to survey and analyze requirements for the development of children's educational games, with the target audience of children from 5 to 9 years old. A list of basic requirements that can improve the development of educational games and provide a basis for further development of a game is raised. It is possible to continue the work by addressing theories of education, the process of gamification of education and other requirements gathering methods.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted