IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i6p1488-d1100896.html
   My bibliography  Save this article

Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis

Author

Listed:
  • Daniel Doz

    (Faculty of Education, University of Primorska, Cankarjeva, 5-6000 Koper, Slovenia)

  • Darjo Felda

    (Faculty of Education, University of Primorska, Cankarjeva, 5-6000 Koper, Slovenia)

  • Mara Cotič

    (Faculty of Education, University of Primorska, Cankarjeva, 5-6000 Koper, Slovenia)

Abstract

Several factors affect students’ mathematics grades and standardized test results. These include the gender of the students, their socio-economic status, the type of school they attend, and their geographic region. In this work, we analyze which of these factors affect assessments of students based on fuzzy logic, using a sample of 29,371 Italian high school students from the 2018/19 academic year. To combine grades assigned by teachers and the students’ results in the INVALSI standardized tests, a hybrid grade was created using fuzzy logic, since it is the most suitable method for analyzing qualitative data, such as teacher-given grades. These grades are analyzed with a hierarchical linear regression. The results show that (1) boys have higher hybrid grades than girls; (2) students with higher socio-economic status achieve higher grades; (3) students from scientific lyceums have the highest grades, whereas students from vocational schools have the lowest; and (4) students from Northern Italy have higher grades than students from Southern Italy. The findings suggest that legislators should investigate appropriate ways to reach equity in assessment and sustainable learning. Without proper interventions, disparities between students might lead to unfairness in students’ future career and study opportunities.

Suggested Citation

  • Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1488-:d:1100896
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/6/1488/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/6/1488/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jorge Luis Andrade & José Luis Valencia, 2022. "A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    2. Contini, Dalit & Tommaso, Maria Laura Di & Mendolia, Silvia, 2017. "The gender gap in mathematics achievement: Evidence from Italian data," Economics of Education Review, Elsevier, vol. 58(C), pages 32-42.
    3. Vladimir Shepelev & Alexandr Glushkov & Tatyana Bedych & Tatyana Gluchshenko & Zlata Almetova, 2021. "Predicting the Traffic Capacity of an Intersection Using Fuzzy Logic and Computer Vision," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    4. Giofrè, D. & Cornoldi, C. & Martini, A. & Toffalini, E., 2020. "A population level analysis of the gender gap in mathematics: Results on over 13 million children using the INVALSI dataset," Intelligence, Elsevier, vol. 81(C).
    5. Marcos J. Villaseñor-Aguilar & José E. Peralta-López & David Lázaro-Mata & Carlos E. García-Alcalá & José A. Padilla-Medina & Francisco J. Perez-Pinal & José A. Vázquez-López & Alejandro I. Barranco-G, 2022. "Fuzzy Fusion of Stereo Vision, Odometer, and GPS for Tracking Land Vehicles," Mathematics, MDPI, vol. 10(12), pages 1-19, June.
    6. Pedro J. Correa-Caicedo & Horacio Rostro-González & Martin A. Rodriguez-Licea & Óscar Octavio Gutiérrez-Frías & Carlos Alonso Herrera-Ramírez & Iris I. Méndez-Gurrola & Miroslava Cano-Lara & Alejandro, 2021. "GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles," Mathematics, MDPI, vol. 9(21), pages 1-18, November.
    7. Maas, Cora J. M. & Hox, J.J.Joop J., 2004. "The influence of violations of assumptions on multilevel parameter estimates and their standard errors," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 427-440, June.
    8. Lingqin Xia & Guang Chen & Tao Wu & Yu Gao & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2022. "Optimal Intelligent Control for Doubly Fed Induction Generators," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    9. Hogan, Robert & Chamorro-Premuzic, Tomas & Kaiser, Robert B., 2013. "Employability and Career Success: Bridging the Gap Between Theory and Reality," Industrial and Organizational Psychology, Cambridge University Press, vol. 6(1), pages 3-16, March.
    10. Xiongfeng Deng & Yiqing Huang & Lisheng Wei, 2022. "Adaptive Fuzzy Command Filtered Finite-Time Tracking Control for Uncertain Nonlinear Multi-Agent Systems with Unknown Input Saturation and Unknown Control Directions," Mathematics, MDPI, vol. 10(24), pages 1-22, December.
    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. Todorka Glushkova & Vanya Ivanova & Boyan Zlatanov, 2024. "Beyond Traditional Assessment: A Fuzzy Logic-Infused Hybrid Approach to Equitable Proficiency Evaluation via Online Practice Tests," Mathematics, MDPI, vol. 12(3), pages 1-14, January.

    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. Daniel Doz & Mara Cotič & Darjo Felda, 2023. "Random Forest Regression in Predicting Students’ Achievements and Fuzzy Grades," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    2. Yongjun Lee & Byungwoon Park, 2022. "Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban Area," Mathematics, MDPI, vol. 10(3), pages 1-15, January.
    3. Di Tommaso, Maria Laura & Maccagnan, Anna & Mendolia, Silvia, 2018. "The Gender Gap in Attitudes and Test Scores: A New Construct of the Mathematical Capability," IZA Discussion Papers 11843, Institute of Labor Economics (IZA).
    4. Di Tommaso, Maria Laura & Contini, Dalit & De Rosa, Dalila & Ferrara, Francesca & Piazzalunga, Daniela & Robutti, Ornella, 2024. "Tackling the gender gap in mathematics with active learning methodologies," Economics of Education Review, Elsevier, vol. 100(C).
    5. Andreea Cordos (Labaditis) & Adriana Tiron-Tudor, 2023. "Employability skills for professional accountants in the midstof Industry 4.0 - a literature review," Journal of Financial Studies, Institute of Financial Studies, vol. 15(8), pages 62-85, November.
    6. Antonella D’Agostino & Francesco Schirripa Spagnolo & Nicola Salvati, 2022. "Studying the relationship between anxiety and school achievement: evidence from PISA data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 1-20, March.
    7. Giofrè, D. & Allen, K. & Toffalini, E. & Mammarella, I.C. & Caviola, S., 2022. "Decoding gender differences: Intellectual profiles of children with specific learning disabilities," Intelligence, Elsevier, vol. 90(C).
    8. Marco Sanfilippo & Adnan Seric, 2016. "Spillovers from agglomerations and inward FDI: a multilevel analysis on sub-Saharan African firms," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 152(1), pages 147-176, February.
    9. Sulis, Isabella & Giambona, Francesca & Porcu, Mariano, 2020. "Adjusted indicators of quality and equity for monitoring the education systems over time. Insights on EU15 countries from PISA surveys," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    10. Cavalieri, Marina & Finocchiaro Castro, Massimo & Guccio, Calogero, 2023. "Organised crime and educational outcomes in Southern Italy: An empirical investigation," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    11. Martin Okolikj & Stephen Quinlan, 2016. "Context Matters: Economic Voting in the 2009 and 2014 European Parliament Elections," Politics and Governance, Cogitatio Press, vol. 4(1), pages 145-166.
    12. Vladimir Shepelev & Aleksandr Glushkov & Ivan Slobodin & Yuri Cherkassov, 2023. "Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks," Mathematics, MDPI, vol. 11(5), pages 1-23, February.
    13. Marina Cavalieri & Massimo Finocchiaro Castro & Calogero Guccio, 2020. "Does the Fish Rot from the Head? Organised Crime and Educational Outcomes in Southern Italy," Working papers 97, Società Italiana di Economia Pubblica.
    14. Antra Singh & Seema Singh, 2021. "Do Employability Skills Matter in Placement: An Exploratory Study of Private Engineering Institutions and IT Firms in Delhi NCR," The Indian Journal of Labour Economics, Springer;The Indian Society of Labour Economics (ISLE), vol. 64(4), pages 1093-1113, December.
    15. Monika Mühlböck & Nadia Steiber & Bernhard Kittel, 2022. "Learning to keep the faith? Further education and perceived employability among young unemployed," Economic and Industrial Democracy, Department of Economic History, Uppsala University, Sweden, vol. 43(2), pages 705-725, May.
    16. David Autor & David Figlio & Krzysztof Karbownik & Jeffrey Roth & Melanie Wasserman, 2023. "Males at the Tails: How Socioeconomic Status Shapes the Gender Gap," The Economic Journal, Royal Economic Society, vol. 133(656), pages 3136-3152.
    17. Patrizia Ordine & Giuseppe Rose, 2019. "Early entry, age-at-test, and schooling attainment: evidence from Italian primary schools," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 36(3), pages 761-784, October.
    18. Daniela Piazzalunga, 2018. "The Gender Wage Gap Among College Graduates in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 4(1), pages 33-90, March.
    19. Paniagua, Victoria, 2022. "When clients vote for brokers: How elections improve public goods provision in urban slums," World Development, Elsevier, vol. 158(C).
    20. Diana Chise & Margherita Fort & Chiara Monfardini, 2020. "Scientifico! like Dad: On the Intergenerational Transmission of STEM Education," FBK-IRVAPP Working Papers 2020-01, Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation.

    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:jmathe:v:11:y:2023:i:6:p:1488-:d:1100896. 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.