IDEAS home Printed from https://ideas.repec.org/h/aec/ieed11/11-22.html
   My bibliography  Save this book chapter

How to predict university performance: a case study from a prestigious Turkish university?

In: Investigaciones de Economía de la Educación 11

Author

Listed:
  • Sezgin Polat

    (Galatasaray University)

  • Jean-Jacques Paul

    (Galatasaray University/Bourgogne Franche Comté University/IREDU)

Abstract

Turkish education system is based on a centralized selection scheme starting from secondary education level. For the post-secondary studies, students are sorted according to scores they obtained from a national competitive exam. By statute, Galatasaray University, which is a French speaking university founded in 1992 by Turkey and France, enrolls one half of its students amongst the very best candidates. The other half comes from French speaking high schools (relatively top-ranked). These students pass a specific competitive exam in French, and must be classified amongst the first 25000 at the national competition. But their national ranking remains lower than the other group. The first batch has to learn French before starting undergraduate studies, whereas French speaking students are entitled to enter directly the first year. Within the public university system where admission is strictly based on national exam scores, differentiated admission scheme of Galatasaray University offers a unique case to test the respective performance of these two groups of students. Using a special data, we estimate the impact of high school background (public vs. private, types of high school) and national exam score on the university performance of students admitted for the years 1994-2011. We do not have information on family background but public-private distinction can capture some of income effect which is missing in our data. We also use additional controls for selection into graduation (time to complete) and departments. Regional variation is controlled with the location of high school. If we assume that the initial academic level and final grades are correlated, we can measure the trade-off in terms of total academic output linked to the recruitment of French-speaking students through a less-demanding specific competition versus creaming process of the national competition. Finally, we highlight the validity of national exam to sort students according to their abilities.

Suggested Citation

  • Sezgin Polat & Jean-Jacques Paul, 2016. "How to predict university performance: a case study from a prestigious Turkish university?," Investigaciones de Economía de la Educación volume 11, in: José Manuel Cordero Ferrera & Rosa Simancas Rodríguez (ed.), Investigaciones de Economía de la Educación 11, edition 1, volume 11, chapter 22, pages 423-434, Asociación de Economía de la Educación.
  • Handle: RePEc:aec:ieed11:11-22
    as

    Download full text from publisher

    File URL: http://repec.economicsofeducation.com/2016badajoz/11-22.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Caner, Asena & Okten, Cagla, 2013. "Higher education in Turkey: Subsidizing the rich or the poor?," Economics of Education Review, Elsevier, vol. 35(C), pages 75-92.
    2. Cohn, Elchanan & Cohn, Sharon & Balch, Donald C. & Bradley, James Jr., 2004. "Determinants of undergraduate GPAs: SAT scores, high-school GPA and high-school rank," Economics of Education Review, Elsevier, vol. 23(6), pages 577-586, December.
    3. Clark, Melissa & Rothstein, Jesse & Schanzenbach, Diane Whitmore, 2009. "Selection bias in college admissions test scores," Economics of Education Review, Elsevier, vol. 28(3), pages 295-307, June.
    4. Robinson, Michael & Monks, James, 2005. "Making SAT scores optional in selective college admissions: a case study," Economics of Education Review, Elsevier, vol. 24(4), pages 393-405, August.
    5. Cyrenne, Philippe & Chan, Alan, 2012. "High school grades and university performance: A case study," Economics of Education Review, Elsevier, vol. 31(5), pages 524-542.
    6. Rothstein, J.M.Jesse M., 2004. "College performance predictions and the SAT," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 297-317.
    7. repec:cdl:econwp:qt59s4j4m4 is not listed on IDEAS
    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. Sandra E. Black & Kalena E. Cortes & Jane Arnold Lincove, 2014. "Efficacy vs. Equity: What Happens When States Tinker with College Admissions in a Race-Blind Era?," NBER Working Papers 20804, National Bureau of Economic Research, Inc.
    2. Black, Sandra E. & Lincove, Jane & Cullinane, Jennifer & Veron, Rachel, 2015. "Can you leave high school behind?," Economics of Education Review, Elsevier, vol. 46(C), pages 52-63.
    3. Beattie, Graham & Laliberté, Jean-William P. & Oreopoulos, Philip, 2018. "Thrivers and divers: Using non-academic measures to predict college success and failure," Economics of Education Review, Elsevier, vol. 62(C), pages 170-182.
    4. Cyrenne, Philippe & Chan, Alan, 2012. "High school grades and university performance: A case study," Economics of Education Review, Elsevier, vol. 31(5), pages 524-542.
    5. Conlin, Michael & Dickert-Conlin, Stacy & Chapman, Gabrielle, 2013. "Voluntary disclosure and the strategic behavior of colleges," Journal of Economic Behavior & Organization, Elsevier, vol. 96(C), pages 48-64.
    6. Yang, Guangliang, 2014. "Are all admission sub-tests created equal? — Evidence from a National Key University in China," China Economic Review, Elsevier, vol. 30(C), pages 600-617.
    7. Pedro Luis Silva, 2024. "Specialists or All-Rounders: How Best to Select University Students?," Journal of Human Capital, University of Chicago Press, vol. 18(2), pages 227-271.
    8. Bai, Chong-en & Chi, Wei & Qian, Xiaoye, 2014. "Do college entrance examination scores predict undergraduate GPAs? A tale of two universities," China Economic Review, Elsevier, vol. 30(C), pages 632-647.
    9. Nick Huntington-Klein & Andrew Gill, 2021. "Semester Course Load and Student Performance," Research in Higher Education, Springer;Association for Institutional Research, vol. 62(5), pages 623-650, August.
    10. Philippe Cyrenne & Alan Chan, 2019. "The Determinants of Student Success in University: A Generalized Ordered Logit Approach," Departmental Working Papers 2019-03, The University of Winnipeg, Department of Economics.
    11. Jensen, Elizabeth J. & Wu, Stephen, 2010. "Early decision and college performance," Economics of Education Review, Elsevier, vol. 29(4), pages 517-525, August.
    12. Chapman, Gabrielle & Dickert-Conlin, Stacy, 2012. "Applying early decision: Student and college incentives and outcomes," Economics of Education Review, Elsevier, vol. 31(5), pages 749-763.
    13. Pengfei Jia & Tim Maloney, 2014. "Using Predictive Modelling to Identify Students at Risk of Poor University Outcomes," Working Papers 2014-03, Auckland University of Technology, Department of Economics.
    14. A. Abigail Payne & Justin Smith, 2020. "Big Fish, Small Pond: The Effect of Rank at Entry on Postsecondary Outcomes," Southern Economic Journal, John Wiley & Sons, vol. 86(4), pages 1475-1509, April.
    15. Mallik, Girijasankar & Shankar, Sriram, 2016. "Does prior knowledge of economics and higher level mathematics improve student learning in principles of economics?," Economic Analysis and Policy, Elsevier, vol. 49(C), pages 66-73.
    16. Phipps, Aaron & Amaya, Alexander, 2023. "Are students time constrained? Course load, GPA, and failing," Journal of Public Economics, Elsevier, vol. 225(C).
    17. Bai, Chong-en & Chi, Wei, 2011. "Determinants of undergraduate GPAs in China: college entrance examination scores, high school achievement, and admission route," MPRA Paper 31240, University Library of Munich, Germany.
    18. Oleg Zamkov & Anatoly Peresetsky, 2013. "Russian Unified National Exams (UNE) and academic performance of ICEF HSE students," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 30(2), pages 93-114.
    19. Peter Bergman, 2020. "Nudging Technology Use: Descriptive and Experimental Evidence from School Information Systems," Education Finance and Policy, MIT Press, vol. 15(4), pages 623-647, Fall.
    20. Card, David & Rothstein, Jesse, 2007. "Racial segregation and the black-white test score gap," Journal of Public Economics, Elsevier, vol. 91(11-12), pages 2158-2184, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    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:aec:ieed11:11-22. 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: Domingo P. Ximénez-de-Embún (email available below). General contact details of provider: https://edirc.repec.org/data/aedeeea.html .

    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.