Title: Artificial neural network tactic to predict interest in majors in computing

Authors: Sahar Idwan; Shereen Ismail; Moh'd Sami Ashhab; Mohammed Awad; Izzeddin Matar

Addresses: Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' Department of Computer Science, University of North Dakota, Grand Forks, USA ' Department of Mechanical Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' Department of Computer Science and Engineering, School of Engineering, American University of Ras Al Khaimah, P.O. Box 10021, American University of Ras, Al Khaimah Road, Ras al Khaimah, UAE ' Software Engineering Department, Faculty of Information Technology, University of Petra, P.O. Box 961343 Amman 11196, Jordan

Abstract: In this paper, we will present the first study of using the neural network approach to predict aspects that influence high school students in selecting an information and communication technology (ICT) related major at their respective universities. A survey was distributed among high school students to determine the factors towards choosing related fields in ICT. We trained the neural network algorithm with the available data. The input to the network stems from six factors: curriculum, extra-curricular activities, decision-makers, teachers, importance of ICT or computing-related subjects at school, and infrastructure. The neural network predicts the high school student's behaviour towards choosing the ICT major at the university level. Simulation results show the importance of these factors in predicting the student's choice in majoring in ICT.

Keywords: artificial neural network; ANN; artificial intelligence; information and communication technology; ICT; computing majors.

DOI: 10.1504/IJCAET.2022.125715

International Journal of Computer Aided Engineering and Technology, 2022 Vol.17 No.3, pp.335 - 347

Received: 12 Dec 2019
Accepted: 03 Mar 2020
Published online: 27 Sep 2022 *

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