Kids’ Atlas application to Learn about Geography and Maps
Abstract Geography is the study of local and spatial variations in physical and human events on Earth. Studies of the world's geography have grown together with human developments and revolutions. Atlases often present geographic features and boundaries of areas; an atlas is a compilation of different Earth maps or Earth regions, such as the Middle East, and the continents of Asia, and North America. Most teachers still use classical methods of teaching. Geographical concepts and map-reading skills are the most common aspects of learning that early-stage students find challenging. Hence, the objective of this application is to develop a geography application for children between the ages of 9 and 12 years that would allow them to learn maps. Nowadays, smartphones and mobile apps are drawing closer to becoming acceptable learning tools. To facilitate this, Kids’ Atlas is an android application, the main purpose of which is to help children to learn easily and test their knowledge. The application improves learning through entertainment by adding technologies that will help children to learning geography. It captures their attention to learn by visualizing objects and allows them to interact more effectively than traditional methods teaching by visualizing the 3D items. The application intends to improve the individual’s ability to understand by providing a training section containing simple quizzes, listening/voice recognition capability, and it has the ability to search for a country by voice recognition and zooming for searched country. The methodology involves a set of software development phases, beginning with the planning; analyze data, design, implementation, testing and maintenance phases. The result of this project is a geography learning application that assists children to enjoy learning geography. The result has shown positive indicators that improve children’s ability and knowledge of geography. Learning geography also becomes enjoyable; encouraging and motivating children to continue learning. This project contributes to the growth of education in early childhood, which is essential to shape the nation for the future. Therefore, this project is significant and relevant, as it contributes to the knowledge society for Saudi Arabia.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Abbasi, M. U., Rashad, A., Basalamah, A., & Tariq, M. (2019). Detection of Epilepsy Seizures in Neo-Natal EEG Using LSTM Architecture. IEEE Access, 7, 179074-179085.
Abedin, M. Z., Akther, S., & Hossain, M. S. (2019, September). An Artificial Neural Network Model for Epilepsy Seizure Detection. In 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) (pp. 860-865). IEEE.
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 100, 270-278.
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907.
Bhagat, P. N., Ramesh, K. S., & Patil, S. T. (2019). An automatic diagnosis of epileptic seizure based on optimization using Electroencephalography Signals. Journal of Critical Reviews, 6(5), 200-212.
Choi, G., Park, C., Kim, J., Cho, K., Kim, T. J., Bae, H., ... & Chong, J. (2019, January). A novel multi-scale 3D CNN with deep neural network for epileptic seizure detection. In 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-2). IEEE.
Huang, C., Chen, W., & Cao, G. (2019, November). Automatic Epileptic Seizure Detection via Attention-Based CNN-BiRNN. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 660-663). IEEE.
Lian, J., Zhang, Y., Luo, R., Han, G., Jia, W., & Li, C. (2020). Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network. IEEE Access, 8, 40008-40017.
Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., ... & Ge, Z. (2020, February). Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study. In Proceedings of the Australasian Computer Science Week Multiconference (pp. 1-8).
Mao, W. L., Fathurrahman, H. I. K., Lee, Y., & Chang, T. W. (2020, January). EEG dataset classification using CNN method. In Journal of Physics: Conference Series (Vol. 1456, No. 1, p. 012017). IOP Publishing.
Thanaraj, K. P., Parvathavarthini, B., Tanik, U. J., Rajinikanth, V., Kadry, S., & Kamalanand, K. (2020). Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis. arXiv preprint arXiv:2003.04534.
Wei, Z., Zou, J., Zhang, J., & Xu, J. (2019). Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomedical Signal Processing and Control, 53, 101551.
Yeola, L. A., & Satone, M. P. (2019). Deep Neural Network for the Automated Detection and Diagnosis of Seizure using EEG Signals.
http://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition downloaded, Jan 2020.
Abedin, M. Z., Akther, S., & Hossain, M. S. (2019, September). An Artificial Neural Network Model for Epilepsy Seizure Detection. In 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) (pp. 860-865). IEEE.
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 100, 270-278.
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., & Elger, C. E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6), 061907.
Bhagat, P. N., Ramesh, K. S., & Patil, S. T. (2019). An automatic diagnosis of epileptic seizure based on optimization using Electroencephalography Signals. Journal of Critical Reviews, 6(5), 200-212.
Choi, G., Park, C., Kim, J., Cho, K., Kim, T. J., Bae, H., ... & Chong, J. (2019, January). A novel multi-scale 3D CNN with deep neural network for epileptic seizure detection. In 2019 IEEE International Conference on Consumer Electronics (ICCE) (pp. 1-2). IEEE.
Huang, C., Chen, W., & Cao, G. (2019, November). Automatic Epileptic Seizure Detection via Attention-Based CNN-BiRNN. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 660-663). IEEE.
Lian, J., Zhang, Y., Luo, R., Han, G., Jia, W., & Li, C. (2020). Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network. IEEE Access, 8, 40008-40017.
Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., ... & Ge, Z. (2020, February). Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study. In Proceedings of the Australasian Computer Science Week Multiconference (pp. 1-8).
Mao, W. L., Fathurrahman, H. I. K., Lee, Y., & Chang, T. W. (2020, January). EEG dataset classification using CNN method. In Journal of Physics: Conference Series (Vol. 1456, No. 1, p. 012017). IOP Publishing.
Thanaraj, K. P., Parvathavarthini, B., Tanik, U. J., Rajinikanth, V., Kadry, S., & Kamalanand, K. (2020). Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis. arXiv preprint arXiv:2003.04534.
Wei, Z., Zou, J., Zhang, J., & Xu, J. (2019). Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain. Biomedical Signal Processing and Control, 53, 101551.
Yeola, L. A., & Satone, M. P. (2019). Deep Neural Network for the Automated Detection and Diagnosis of Seizure using EEG Signals.
http://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition downloaded, Jan 2020.
Aljojo, N., Ameen Banjar, Mashael Khayyat, Basma Alharbi, Areej Alshutayri, Amani Jamal, Azida Zainol, Dana Waggas, Ghydaa Saleh, Rahaf Alshehri, & Shoroug Aljuaid. (2020). Kids’ Atlas application to Learn about Geography and Maps. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 9(2), 33–48. https://doi.org/10.14201/ADCAIJ2020923348
Most read articles by the same author(s)
- Areej Alshutayria, Nahla Aljojo, Basma Alharbia, Ameen Banjarb, Atheer Alshehria, Mashaiel Alargoubia, Ola Barradha, Rahaf Helabia, An Interactive Mobile Application to Request the Help of the Nearest First Aider by the Injured , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 10 No. 1 (2021)
- Nahla Aljojo, Predicting Financial Risk Associated to Bitcoin Investment by Deep Learning , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 11 No. 1 (2022)
- Amani Jamal , Asmaa Munshi , Nahla Aljojo, Talal Qadah , Azida Zainol , Digital Information Needs for Understanding Cell Divisions in the Human Body , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 9 No. 2 (2020)
Downloads
Download data is not yet available.
+
−