IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6241-d1116331.html

Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning

Author

Listed:
  • Ghazala Kausar

    (Department of English, National University of Modern Languages, Islamabad 44000, Pakistan)

  • Sajid Saleem

    (Department of Computer Science, National University of Modern Languages, Lalazar, Rawalpindi 46000, Pakistan)

  • Fazli Subhan

    (Department of Computer Science, National University of Modern Languages, Islamabad 44000, Pakistan
    Faculty of Computer and Information, Multimedia University, Cyberjaya 63100, Malaysia)

  • Mazliham Mohd Suud

    (Faculty of Computer and Information, Multimedia University, Cyberjaya 63100, Malaysia)

  • Mansoor Alam

    (Faculty of Computer and Information, Multimedia University, Cyberjaya 63100, Malaysia)

  • M. Irfan Uddin

    (Institute of Computing, Kohat University of Science and Technology, Kohat 26000, Pakistan)

Abstract

Computers have enabled diverse and precise data processing and analysis for decades. Researchers of humanities and social sciences are increasingly adopting computational tools such as artificial intelligence (AI) and machine learning (ML) to analyse human behaviour in society by identifying patterns within data. In this regard, this paper presents the modelling of teachers and students’ perceptions regarding gender bias in text books through AI. The data was collected from 470 respondents through a questionnaire using five different themes. The data was analysed with support vector machines (SVM), decision trees (DT), random forest (RF) and artificial neural networks (ANN). The experimental results show that the prediction of perceptions regarding gender varies according to the theme and leads to the different performances of the AI techniques. However, it is observed that when data from all the themes are combined, the best results are obtained. The experimental results show that ANN, on average, demonstrates the best performance by achieving an accuracy of 87.2%, followed by RF and SVM, which demonstrate an accuracy of 84% and 80%, respectively. This paper is significant in modelling human behaviour in society through AI, which is a significant contribution to the field.

Suggested Citation

  • Ghazala Kausar & Sajid Saleem & Fazli Subhan & Mazliham Mohd Suud & Mansoor Alam & M. Irfan Uddin, 2023. "Prediction of Gender-Biased Perceptions of Learners and Teachers Using Machine Learning," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6241-:d:1116331
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6241/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/6241/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ad. J. W. van de Gevel & Charles N. Noussair, 2013. "The Nexus between Artificial Intelligence and Economics," SpringerBriefs in Economics, Springer, edition 127, number 978-3-642-33648-5, January.
    2. Emma Dahlin, 2021. "Mind the gap! On the future of AI research," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 8(1), pages 1-4, December.
    3. Rickard Danell & Mikael Hjerm, 2013. "Career prospects for female university researchers have not improved," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 999-1006, March.
    4. Dennis P Wall & Rebecca Dally & Rhiannon Luyster & Jae-Yoon Jung & Todd F DeLuca, 2012. "Use of Artificial Intelligence to Shorten the Behavioral Diagnosis of Autism," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-8, August.
    5. Helen Shen, 2013. "Inequality quantified: Mind the gender gap," Nature, Nature, vol. 495(7439), pages 22-24, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Merlin Leunda Martiarena & Jesus Céspedes & Marta Varanda & Matilda Merkohasanaj & Bissanagha Antonio dos Santos & Marina Padrão Temudo, 2024. "The Role and Drivers of Cooperation in Managing Hydraulic Infrastructures for Sustainable Mangrove Rice Production in Guinea-Bissau," Sustainability, MDPI, vol. 17(1), pages 1-42, December.

    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. Aleksandra Cislak & Magdalena Formanowicz & Tamar Saguy, 2018. "Bias against research on gender bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 189-200, April.
    2. Feras A. Batarseh & Munisamy Gopinath & Anderson Monken, 2020. "Artificial Intelligence Methods for Evaluating Global Trade Flows," International Finance Discussion Papers 1296, Board of Governors of the Federal Reserve System (U.S.).
    3. Una Tellhed & Anna Jansson, 2018. "Communicating Gender-Equality Progress, Reduces Social Identity Threats for Women Considering a Research Career," Social Sciences, MDPI, vol. 7(2), pages 1-11, January.
    4. repec:osf:osfxxx:yr8me_v1 is not listed on IDEAS
    5. Letki, Natalia & Biały, Grzegorz & Sankowski, Piotr & Walentek, Dawid, 2022. "Streamlining for excellence discriminates against women: A study of research productivity of 2.7 mln scientists in 45 countries," OSF Preprints yr8me, Center for Open Science.
    6. Per Lunnemann & Mogens H. Jensen & Liselotte Jauffred, 2019. "Gender bias in Nobel prizes," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 5(1), pages 1-4, December.
    7. R J Shellock & C Cvitanovic & M C Mckinnon & M Mackay & I E van Putten & J Blythe & R Kelly & P Tuohy & K M Maltby & S Mynott & N Simmonds & M Bailey & A Begossi & B Crona & K A Fakoya & B P Ferreira , 2025. "Building leaders for the UN Ocean Science Decade: a guide to supporting early career women researchers within academic marine research institutions," Post-Print hal-05242435, HAL.
    8. Naudé, Wim & Dimitri, Nicola, 2021. "Public Procurement and Innovation for Human-Centered Artificial Intelligence," IZA Discussion Papers 14021, IZA Network @ LISER.
    9. repec:plo:pone00:0099900 is not listed on IDEAS
    10. Walton, Nigel & Nayak, Bhabani Shankar, 2021. "Rethinking of Marxist perspectives on big data, artificial intelligence (AI) and capitalist economic development," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    11. Natalia Restrepo & Alfonso Unceta & Xabier Barandiaran, 2021. "Gender Diversity in Research and Innovation Projects: The Proportion of Women in the Context of Higher Education," Sustainability, MDPI, vol. 13(9), pages 1-21, May.
    12. Kjersten Bunker Whittington & Molly M. King & Isabella Cingolani, 2024. "Structure, status, and span: gender differences in co-authorship networks across 16 region-subject pairs (2009–2013)," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(1), pages 147-179, January.
    13. Isabelle Vernos & Sonja Reiland & Daniele Alimonti & José García-Montalvo, 2020. "Gender Stereotype and the Scientific Career of Women: Evidence from Biomedical Research Centers," Working Papers 1212, Barcelona School of Economics.
    14. Feras Batarseh & Munisamy Gopinath & Ganesh Nalluru & Jayson Beckman, 2019. "Application of Machine Learning in Forecasting International Trade Trends," Papers 1910.03112, arXiv.org.
    15. Renée B. Adams & Tom Kirchmaier, 2016. "Women on Boards in Finance and STEM Industries," American Economic Review, American Economic Association, vol. 106(5), pages 277-281, May.
    16. Naudé, Wim & Dimitri, Nicola, 2018. "The Race for an Artificial General Intelligence: Implications for Public Policy," IZA Discussion Papers 11737, IZA Network @ LISER.
    17. Rens van de Schoot & Mara A Yerkes & Jolien M Mouw & Hans Sonneveld, 2013. "What Took Them So Long? Explaining PhD Delays among Doctoral Candidates," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-11, July.
    18. Tahereh Dehdarirad & Anna Villarroya & Maite Barrios, 2015. "Research on women in science and higher education: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(3), pages 795-812, June.
    19. Gries, Thomas & Naude, Wim, 2018. "Artificial intelligence, jobs, inequality and productivity: Does aggregate demand matter?," MERIT Working Papers 2018-047, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    20. Anestis Fotoglou & Ioanna Moraiti & Katerina Dona & Alexandra Katsimperi & Konstantinos Tsionakas & Zoi Karabatzaki & Athanasios Drigas, 2022. "IoT Applications help people with Autism," Technium Social Sciences Journal, Technium Science, vol. 31(1), pages 115-130, May.
    21. Nikolaos A. Kazakis, 2015. "The research activity of the current faculty of the Greek chemical engineering departments: a bibliometric study in national and international context," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 229-250, April.
    22. Jean-Philippe Deranty & Thomas Corbin, 2022. "Artificial Intelligence and work: a critical review of recent research from the social sciences," Papers 2204.00419, arXiv.org.

    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:gam:jsusta:v:15:y:2023:i:7:p:6241-:d:1116331. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.