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Author
Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin
Title
Mathematics learning opportunities when playing a Tower Defense Game
Type
Journal
Year
2015
Publication
International Journal of Serious Games
Abbreviated Journal
IJSG
Volume
2
Issue
4
Pages
57-71
Keywords
Tower Defense game; learning opportunities; mathematics; problem solving; game design
Abstract
A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.
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Notes
ADAS; 600.076
Approved
no
Call Number
Admin @ si @ HJG2015
Serial
2730
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Author
Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate
Title
Weather Classification by Utilizing Synthetic Data
Type
Journal Article
Year
2022
Publication
Sensors
Abbreviated Journal
SENS
Volume
22
Issue
9
Pages
3193
Keywords
Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems
Abstract
Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.
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21 April 2022
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MDPI
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IAM; 600.139; 600.159; 600.166; 600.145;
Approved
no
Call Number
Admin @ si @ MKE2022
Serial
3761
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