Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network
Precision education adopted the principles of precision medicine. It applies algorithm, machine learning and data manipulation technique for prediction and ultimately will be referred to design individualized education intervention program. This study has reviewed various themes and applications in...
Published in: | 2021 International Conference of Technology, Science and Administration, ICTSA 2021 |
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Institute of Electrical and Electronics Engineers Inc.
2021
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2-s2.0-85105525420 Yusof M.H.M.; Khalid I.A. Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network 2021 2021 International Conference of Technology, Science and Administration, ICTSA 2021 10.1109/ICTSA52017.2021.9406525 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105525420&doi=10.1109%2fICTSA52017.2021.9406525&partnerID=40&md5=8800b80b1284a603baeae44c6c4dcfe7 Precision education adopted the principles of precision medicine. It applies algorithm, machine learning and data manipulation technique for prediction and ultimately will be referred to design individualized education intervention program. This study has reviewed various themes and applications in precision education research and subsequently design a prediction model by applying one of its technique which is deep learning in a field study conducted at Universiti Sultan Azlan Shah, Malaysia. The study is to predict student's performance in an English course taken by Bachelor of Usuluddin and Bachelor of Syariah students. Folder classification testing of the proposed deep learning model, when it was run against 3 testing dataset, it gives 93% accuracy. © 2021 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
author |
Yusof M.H.M.; Khalid I.A. |
spellingShingle |
Yusof M.H.M.; Khalid I.A. Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network |
author_facet |
Yusof M.H.M.; Khalid I.A. |
author_sort |
Yusof M.H.M.; Khalid I.A. |
title |
Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network |
title_short |
Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network |
title_full |
Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network |
title_fullStr |
Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network |
title_full_unstemmed |
Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network |
title_sort |
Precision Education Reviews: A Case Study on Predicting Student's Performance using Feed Forward Neural Network |
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2021 |
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2021 International Conference of Technology, Science and Administration, ICTSA 2021 |
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doi_str_mv |
10.1109/ICTSA52017.2021.9406525 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105525420&doi=10.1109%2fICTSA52017.2021.9406525&partnerID=40&md5=8800b80b1284a603baeae44c6c4dcfe7 |
description |
Precision education adopted the principles of precision medicine. It applies algorithm, machine learning and data manipulation technique for prediction and ultimately will be referred to design individualized education intervention program. This study has reviewed various themes and applications in precision education research and subsequently design a prediction model by applying one of its technique which is deep learning in a field study conducted at Universiti Sultan Azlan Shah, Malaysia. The study is to predict student's performance in an English course taken by Bachelor of Usuluddin and Bachelor of Syariah students. Folder classification testing of the proposed deep learning model, when it was run against 3 testing dataset, it gives 93% accuracy. © 2021 IEEE. |
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Institute of Electrical and Electronics Engineers Inc. |
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English |
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Conference paper |
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scopus |
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Scopus |
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1809677894714130432 |