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Gendered Teacher Feedback, Students' Math Performance and Enrollment Outcomes: A Text Mining Approach

Author

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  • Pauline Charousset

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Marion Monnet

    (INED - Institut national d'études démographiques)

Abstract
This paper studies how student gender influences the feedback given by teachers, and how this affects the student's performance in school. Using the written feedback provided to the universe of French high school students by their math teachers over a five-year period, we show that teachers use different words to assess the performance of equally able male and female students. Teachers highlight the positive behavior and encourage the efforts of their female students while, for similarly-performing males, they criticize the students for unruly behavior and praise them for their intellectual skills. To understand how this relates to the student's subsequent educational outcomes, we then match these data to records from French national examinations, as well as these students' higher education application behavior and ultimate institution of enrollment. Using the quasi-random allocation of teachers to classes, we estimate that being assigned to a teacher with feedback that is one standard deviation more gendered improves student math performance by 1.6 percent of a standard deviation on average, but does not affect students' enrollment in higher education in the following year.

Suggested Citation

  • Pauline Charousset & Marion Monnet, 2022. "Gendered Teacher Feedback, Students' Math Performance and Enrollment Outcomes: A Text Mining Approach," Working Papers halshs-03733956, HAL.
    • Handle: RePEc:hal:wpaper:halshs-03733956
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03733956
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    References listed on IDEAS

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    1. Terrier, Camille, 2020. "Boys lag behind: How teachers’ gender biases affect student achievement," Economics of Education Review, Elsevier, vol. 77(C).
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    Cited by:

    1. Etienne Dagorn & Léonard Moulin, 2023. "Dropping Out of University in Response to the COVID-19 Pandemic," Working Papers 276, French Institute for Demographic Studies.

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    Keywords

    Teacher feedback; Text mining; Gender; Student performance; Higher education;
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