IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5267-d803269.html
   My bibliography  Save this article

Does School-Level Instructional Quality Matter for School Mathematics Performance? Comparing Teacher Data across Seven Countries

Author

Listed:
  • Xin Liu

    (Department of Educational Studies, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium)

  • Martin Valcke

    (Department of Educational Studies, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 2, 9000 Ghent, Belgium)

  • Kajsa Yang Hansen

    (Department of Education and Special Education, Faculty of Education, University of Gothenburg, Västra Hamngatan 25, 40530 Gothenburg, Sweden)

  • Jan De Neve

    (Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 1, 9000 Ghent, Belgium)

Abstract

Based on the TALIS 2013 and PISA 2012 linkage data, we examine the measurement properties of school instructional quality and study its relationship with mathematics performance, considering school context characteristics (school composition, teacher–student relationship, and teacher qualifications). The study adopts a cross-country perspective. In five of the seven countries, a three-dimensional framework has been confirmed to study mathematics instructional quality (disciplinary climate, supportive climate, and cognitive activation). As a common factor, disciplinary climate explains the variation in school mathematics achievement in four countries. The key is the interaction with socioeconomic status. Schools composed of students with favourable socioeconomic backgrounds reflect a disciplinary climate conducive to learning. Schools consisting of students with low socioeconomic backgrounds benefit more from a supportive climate, contributing to the reduction in the achievement gap. Schools with harmonious teacher–student relationships reflect differential effects on mathematics performance of schools consisting of students from lower- and higher-socioeconomic status families. Low-SES schools are more likely have less academically qualified teachers. School collective teacher qualification seems not directly related to school mathematics performance, but disciplinary climate mediates this link. Consistently, schools composed of students from high-socioeconomic status families tend to perform better.

Suggested Citation

  • Xin Liu & Martin Valcke & Kajsa Yang Hansen & Jan De Neve, 2022. "Does School-Level Instructional Quality Matter for School Mathematics Performance? Comparing Teacher Data across Seven Countries," Sustainability, MDPI, vol. 14(9), pages 1-26, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5267-:d:803269
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5267/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/5267/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Steven G. Rivkin & Eric A. Hanushek & John F. Kain, 2005. "Teachers, Schools, and Academic Achievement," Econometrica, Econometric Society, vol. 73(2), pages 417-458, March.
    2. Croninger, Robert G. & Rice, Jennifer King & Rathbun, Amy & Nishio, Masako, 2007. "Teacher qualifications and early learning: Effects of certification, degree, and experience on first-grade student achievement," Economics of Education Review, Elsevier, vol. 26(3), pages 312-324, June.
    3. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    4. Qin, Lixia & Bowen, Daniel H., 2019. "The distributions of teacher qualification: A cross-national study," International Journal of Educational Development, Elsevier, vol. 70(C), pages 1-1.
    5. Kristen Miller & Daniel Mont & Aaron Maitland & Barbara Altman & Jennifer Madans, 2011. "Results of a cross-national structured cognitive interviewing protocol to test measures of disability," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(4), pages 801-815, June.
    6. Bruce Austin & Olusola O. Adesope & Brian F. French & Chad Gotch & Julie Bélanger & Katarzyna Kubacka, 2015. "Examining school context and its influence on teachers: linking Talis 2013 with PISA 2012 student data," OECD Education Working Papers 115, OECD Publishing.
    7. Delprato, Marcos & Chudgar, Amita, 2018. "Factors associated with private-public school performance: Analysis of TALIS-PISA link data," International Journal of Educational Development, Elsevier, vol. 61(C), pages 155-172.
    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. Cordero, Jose M. & Gil-Izquierdo, María, 2018. "The effect of teaching strategies on student achievement: An analysis using TALIS-PISA-link," Journal of Policy Modeling, Elsevier, vol. 40(6), pages 1313-1331.
    2. Kang, Changhui, 2007. "Classroom peer effects and academic achievement: Quasi-randomization evidence from South Korea," Journal of Urban Economics, Elsevier, vol. 61(3), pages 458-495, May.
    3. Michela M. Tincani, 2021. "Teacher labor markets, school vouchers, and student cognitive achievement: Evidence from Chile," Quantitative Economics, Econometric Society, vol. 12(1), pages 173-216, January.
    4. Morales, Camila, 2022. "Do refugee students affect the academic achievement of peers? Evidence from a large urban school district," Economics of Education Review, Elsevier, vol. 89(C).
    5. Schindler, Dirk & Schjelderup, Guttorm, 2012. "Debt shifting and ownership structure," European Economic Review, Elsevier, vol. 56(4), pages 635-647.
    6. Eisenkopf, Gerald & Hessami, Zohal & Fischbacher, Urs & Ursprung, Heinrich W., 2015. "Academic performance and single-sex schooling: Evidence from a natural experiment in Switzerland," Journal of Economic Behavior & Organization, Elsevier, vol. 115(C), pages 123-143.
    7. Mary A. Burke & Tim R. Sass, 2013. "Classroom Peer Effects and Student Achievement," Journal of Labor Economics, University of Chicago Press, vol. 31(1), pages 51-82.
    8. Lee, Se Woong & Lee, Eunjung Alice, 2020. "Teacher qualification matters: The association between cumulative teacher qualification and students’ educational attainment," International Journal of Educational Development, Elsevier, vol. 77(C).
    9. Gil-Izquierdo, María & Cordero, José Manuel, 2017. "Guidelines for data fusion with international large scale assessments: Insights from the TALIS-PISA link," MPRA Paper 79781, University Library of Munich, Germany.
    10. Richard Murphy & Felix Weinhardt, 2020. "Top of the Class: The Importance of Ordinal Rank," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2777-2826.
    11. Brodaty, Thibault & Gurgand, Marc, 2016. "Good peers or good teachers? Evidence from a French University," Economics of Education Review, Elsevier, vol. 54(C), pages 62-78.
    12. Nirav Mehta, 2017. "Competition In Public School Districts: Charter School Entry, Student Sorting, And School Input Determination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 58(4), pages 1089-1116, November.
    13. Daniel Aaronson & Lisa Barrow & William Sander, 2007. "Teachers and Student Achievement in the Chicago Public High Schools," Journal of Labor Economics, University of Chicago Press, vol. 25(1), pages 95-135.
    14. Kuersteiner, Guido M. & Prucha, Ingmar R. & Zeng, Ying, 2023. "Efficient peer effects estimators with group effects," Journal of Econometrics, Elsevier, vol. 235(2), pages 2155-2194.
    15. Francis Kramarz & Stephen Machin & Amine Ouazad, 2008. "What Makes a Test Score ? The Respective Contributions of Pupils, Schools and Peers in Achievement in English Primary Education," Working Papers 2008-21, Center for Research in Economics and Statistics.
    16. Bernal, Pedro & Mittag, Nikolas & Qureshi, Javaeria A., 2016. "Estimating effects of school quality using multiple proxies," Labour Economics, Elsevier, vol. 39(C), pages 1-10.
    17. Eric A. Hanushek & John F. Kain & Steven G. Rivkin, 2009. "New Evidence about Brown v. Board of Education: The Complex Effects of School Racial Composition on Achievement," Journal of Labor Economics, University of Chicago Press, vol. 27(3), pages 349-383, July.
    18. Bouguen, Adrien, 2016. "Adjusting content to individual student needs: Further evidence from an in-service teacher training program," Economics of Education Review, Elsevier, vol. 50(C), pages 90-112.
    19. Christiani, Charis, 2021. "The Education Process Viewed from the Standard-Based Education Paradigm in Public Schools: A Case From Central Java, Indonesia," OSF Preprints n9gez, Center for Open Science.
    20. Haelermans, Carla & De Witte, Kristof, 2012. "The role of innovations in secondary school performance – Evidence from a conditional efficiency model," European Journal of Operational Research, Elsevier, vol. 223(2), pages 541-549.

    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:14:y:2022:i:9:p:5267-:d:803269. 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.