TY - GEN
T1 - Application of Artificial Neural Network to Estimate Students Performance in Scholastic Assessment Test
AU - Ghazali, Shatha Al
AU - Harous, Saad
AU - Turaev, Sherzod
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The applications of artificial intelligence in education became a very attractive topic especially during the COVID-19 pandemic due to the high level of uncertainty surrounded the decision making process within the educational institutions. The objective of this study is to create a model that is able to predict the student's score in the SAT test based on the student's performance in the internal assessments of the school and other demographic attributes. The sample includes 37 students of both genders from a private school in the United Arab Emirates (UAE). The findings suggest that it is possible to implement artificial neural networks to estimate the student's performance in the SAT exam based on internal school data. The model accuracy is 87.4 % however, some attributes can be identified as noise data and can be further removed to increase the accuracy. Scholastic Assessment Test Artificial Neural Network Machine learning Students performance.
AB - The applications of artificial intelligence in education became a very attractive topic especially during the COVID-19 pandemic due to the high level of uncertainty surrounded the decision making process within the educational institutions. The objective of this study is to create a model that is able to predict the student's score in the SAT test based on the student's performance in the internal assessments of the school and other demographic attributes. The sample includes 37 students of both genders from a private school in the United Arab Emirates (UAE). The findings suggest that it is possible to implement artificial neural networks to estimate the student's performance in the SAT exam based on internal school data. The model accuracy is 87.4 % however, some attributes can be identified as noise data and can be further removed to increase the accuracy. Scholastic Assessment Test Artificial Neural Network Machine learning Students performance.
UR - http://www.scopus.com/inward/record.url?scp=85146910758&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146910758&partnerID=8YFLogxK
U2 - 10.1109/CICN56167.2022.10008315
DO - 10.1109/CICN56167.2022.10008315
M3 - Conference contribution
AN - SCOPUS:85146910758
T3 - Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
SP - 166
EP - 170
BT - Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Y2 - 4 December 2022 through 6 December 2022
ER -