AI educational mobile app using deep learning approach.
Moving to Industrial Revolution (IR 4.0), the early education sector is not left behind. More of the teaching method is being digitized into a mobile application to assist and enhance the children’s understanding. On the other hand, most of the applications offer passive learning, in which the child...
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Politeknik Negeri Padang
2023
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Online Access: | http://eprints.utm.my/105943/1/NurHalizaAbdWahab2023_AiEducationalMobileAppUsingDeepLearningApproach.pdf http://eprints.utm.my/105943/ https://joiv.org/index.php/joiv/article/view/1247/742 |
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my.utm.1059432024-05-29T06:26:00Z http://eprints.utm.my/105943/ AI educational mobile app using deep learning approach. Mohd. Nasir, Haslinah Brahin, Noor Mohd. Ariff Ariffin, Farees Ezwan Mohd Sani Mispan, Mohd. Syafiq Abd. Wahab, Nur Haliza T58.5-58.64 Information technology Moving to Industrial Revolution (IR 4.0), the early education sector is not left behind. More of the teaching method is being digitized into a mobile application to assist and enhance the children’s understanding. On the other hand, most of the applications offer passive learning, in which the children complete the activity without interacting with the environment. This study presents an educational mobile application that uses a deep learning approach for interactive learning to enhance English and Arabic vocabulary. Android Studio software and Tensorflow tool were used for this application development. The convolution neural network (CNN) approach was used to classify the item of each category of vocab through image recognition. More than thousands of images each time were pre-trained for image classification. The application will pronounce the requested item. Then, the children will need to move around looking for the item. Once the item’s found, the children must capture the image through the camera’s phone for image detection. This approach can be integrated with teaching and learning techniques for fun learning through interactive smartphone applications. This study attained high accuracy of more than 90% for image classification. In addition, it helps to attract the children's interest during the teaching using the current technology but with the concept of ‘Play’ and ‘Learn’. In the future, this paper recommended the involvement of IoT platforms to provide widen applications. Politeknik Negeri Padang 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/105943/1/NurHalizaAbdWahab2023_AiEducationalMobileAppUsingDeepLearningApproach.pdf Mohd. Nasir, Haslinah and Brahin, Noor Mohd. Ariff and Ariffin, Farees Ezwan Mohd Sani and Mispan, Mohd. Syafiq and Abd. Wahab, Nur Haliza (2023) AI educational mobile app using deep learning approach. International Journal on Informatics Visualization, 7 (3). pp. 952-958. ISSN 2549-9904 https://joiv.org/index.php/joiv/article/view/1247/742 NA |
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T58.5-58.64 Information technology Mohd. Nasir, Haslinah Brahin, Noor Mohd. Ariff Ariffin, Farees Ezwan Mohd Sani Mispan, Mohd. Syafiq Abd. Wahab, Nur Haliza AI educational mobile app using deep learning approach. |
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Moving to Industrial Revolution (IR 4.0), the early education sector is not left behind. More of the teaching method is being digitized into a mobile application to assist and enhance the children’s understanding. On the other hand, most of the applications offer passive learning, in which the children complete the activity without interacting with the environment. This study presents an educational mobile application that uses a deep learning approach for interactive learning to enhance English and Arabic vocabulary. Android Studio software and Tensorflow tool were used for this application development. The convolution neural network (CNN) approach was used to classify the item of each category of vocab through image recognition. More than thousands of images each time were pre-trained for image classification. The application will pronounce the requested item. Then, the children will need to move around looking for the item. Once the item’s found, the children must capture the image through the camera’s phone for image detection. This approach can be integrated with teaching and learning techniques for fun learning through interactive smartphone applications. This study attained high accuracy of more than 90% for image classification. In addition, it helps to attract the children's interest during the teaching using the current technology but with the concept of ‘Play’ and ‘Learn’. In the future, this paper recommended the involvement of IoT platforms to provide widen applications. |
format |
Article |
author |
Mohd. Nasir, Haslinah Brahin, Noor Mohd. Ariff Ariffin, Farees Ezwan Mohd Sani Mispan, Mohd. Syafiq Abd. Wahab, Nur Haliza |
author_facet |
Mohd. Nasir, Haslinah Brahin, Noor Mohd. Ariff Ariffin, Farees Ezwan Mohd Sani Mispan, Mohd. Syafiq Abd. Wahab, Nur Haliza |
author_sort |
Mohd. Nasir, Haslinah |
title |
AI educational mobile app using deep learning approach. |
title_short |
AI educational mobile app using deep learning approach. |
title_full |
AI educational mobile app using deep learning approach. |
title_fullStr |
AI educational mobile app using deep learning approach. |
title_full_unstemmed |
AI educational mobile app using deep learning approach. |
title_sort |
ai educational mobile app using deep learning approach. |
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Politeknik Negeri Padang |
publishDate |
2023 |
url |
http://eprints.utm.my/105943/1/NurHalizaAbdWahab2023_AiEducationalMobileAppUsingDeepLearningApproach.pdf http://eprints.utm.my/105943/ https://joiv.org/index.php/joiv/article/view/1247/742 |
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13.18916 |