Application of Artificial Neural Network to Estimate Students Performance in Scholastic Assessment Test

Shatha Al Ghazali, Saad Harous, Sherzod Turaev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages166-170
Number of pages5
ISBN (Electronic)9781665487719
DOIs
Publication statusPublished - 2022
Event14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 - Al-Khobar, Saudi Arabia
Duration: Dec 4 2022Dec 6 2022

Publication series

NameProceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

Conference

Conference14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Country/TerritorySaudi Arabia
CityAl-Khobar
Period12/4/2212/6/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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