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Prediction of Students’ Performance with Artificial Neural Network Using Demographic Traits

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Recent Innovations in Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 855))

Abstract

Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the increase in the rate of poor graduates, it is necessary to design a system that helps to reduce this menace as well as reduce the incidence of students having to repeat due to poor performance or having to drop out of school altogether in the middle of the pursuit of their career. It is, therefore, necessary to study each one as well as their advantages and disadvantages, to determine which is more efficient in and in what case one should be preferred over the other. The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution. The model was developed based on certain selected variables as the input. It achieved an accuracy of over 92.3%, showing Artificial Neural Network's potential effectiveness as a predictive tool and a selection criterion for candidates seeking admission to a university.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Landmark University Omu Aran, Omu-Aran, Nigeria

    Adeniyi Jide Kehinde & Abidemi Emmanuel Adeniyi

  2. Center of ICT/ICE, Covenant University, Ota, Nigeria

    Roseline Oluwaseun Ogundokun

  3. Birla Institute of Technology Pilani, Hyderabad, India

    Himanshu Gupta

  4. Department of Computer Science and Communication, Ostfold University College, Halden, Norway

    Sanjay Misra

Authors
  1. Adeniyi Jide Kehinde
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  2. Abidemi Emmanuel Adeniyi
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  3. Roseline Oluwaseun Ogundokun
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  4. Himanshu Gupta
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  5. Sanjay Misra
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Corresponding author

Correspondence to Sanjay Misra .

Editor information

Editors and Affiliations

  1. KIET Group of Institutions, Ghaziabad, India

    Pradeep Kumar Singh

  2. Department of CSE, Central University of Jammu, Jammu and Kashmir, India

    Yashwant Singh

  3. Department of Computer Engineering, NIT Kurukshetra, Kurukshetra, India

    Jitender Kumar Chhabra

  4. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Zoltán Illés

  5. Faculty of Informatics, Eötvös Loránd University (ELTE), Budapest, Hungary

    Chaman Verma

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Kehinde, A.J., Adeniyi, A.E., Ogundokun, R.O., Gupta, H., Misra, S. (2022). Prediction of Students’ Performance with Artificial Neural Network Using Demographic Traits. In: Singh, P.K., Singh, Y., Chhabra, J.K., Illés, Z., Verma, C. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 855. Springer, Singapore. https://doi.org/10.1007/978-981-16-8892-8_46

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