IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v8y2025i3d10.1007_s42001-025-00384-w.html
   My bibliography  Save this article

A comparative study of automated undergraduate engineering admission prediction in an Indian university using machine learning

Author

Listed:
  • Meenakshi Gupta

    (Manav Rachna University)

  • Alpana

    (Amity University)

  • Prinima Gupta

    (Manav Rachna University)

  • Neeraj Varshney

    (GLA University)

Abstract

Most of the students who want to consider a career in engineering or technology aim to get admission to top engineering colleges and universities. Engineering entrance exams are considered one of the toughest exams not only in India but all over the world. For the same, students enroll themselves in various coaching institutes, which promise admission. It has been noted that even with the most rigorous tutoring and extensive study sessions, the majority of students are unable to pass engineering entrance tests, which prevents them from being admitted to the college of their choice. The purpose and novelty of this study are to show how different factors, including secondary and senior secondary percentages, preparation methods, family background, school board type, drop cases, and aid, affect engineering admissions and to offer strategies for increasing the increasing the overall chances of being admitted to the best Indian colleges.The dataset used in this work is the Indian University in the northern region specializes in engineering which has more than 1400 records samples with different attributes that are used to decide whether a student will get admission or not. The outcome of the proposed methodology is evaluated using the classification methods for engineering admission prediction and classification. It has been shown that the proposed model (random forest classifier) attained an accuracy of 87% and worked appropriately for the chosen admission data set. Hence, the random forest model may support the mentioned factors affecting the screening of engineering admission to support students as well as parents for better and early-stage college prediction and corrective measures for universities too.

Suggested Citation

  • Meenakshi Gupta & Alpana & Prinima Gupta & Neeraj Varshney, 2025. "A comparative study of automated undergraduate engineering admission prediction in an Indian university using machine learning," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-22, August.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00384-w
    DOI: 10.1007/s42001-025-00384-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-025-00384-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-025-00384-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Edin Osmanbegovic & Mirza Suljic, 2012. "Data Mining Approach For Predicting Student Performance," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 10(1), pages 3-12.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anjeela Jokhan & Aneesh A. Chand & Vineet Singh & Kabir A. Mamun, 2022. "Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    2. Deepti Aggarwal & Sonu Mittal & Vikram Bali, 2021. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 38-49, July.
    3. Alisa Bilal Zorić, 2020. "Predicting Students’ Academic Performance Based on Enrolment Data," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 6(4), pages 54-61, October.
    4. Bilal Zorić, Alisa, 2019. "Predicting Students’ Success Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 58-66, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    5. January D. Febro & Jocelyn Barbosa, 2017. "Mining student at risk in higher education using predictive models," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(4), pages 117-132.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00384-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.