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The Effect of Student Evaluations on Academic Success

Author

Listed:
  • Benjamin Artz

    (Department of Economics, University of Wisconsin–Whitewater)

  • David M. Welsch

    (Department of Economics, University of Wisconsin–Whitewater)

Abstract

This article uses longitudinal student-level data from the American University of Sharjah, a large comprehensive university in the Middle East, to examine the relationship between student evaluations of teachers and current and future student achievement. Our model strategies control for the observed and unobserved heterogeneity of students and use unique instruments. We find that when all disciplines are examined together there is a positive relationship between current evaluation and current grade point average (GPA) but a negative relationship between past evaluations and current GPA. Discipline-specific estimations find the same results in the math and science course subsample, but for other course types there is little relation between evaluation and GPA. © 2013 Association for Education Finance and Policy

Suggested Citation

  • Benjamin Artz & David M. Welsch, 2013. "The Effect of Student Evaluations on Academic Success," Education Finance and Policy, MIT Press, vol. 8(1), pages 100-119, January.
    • Handle: RePEc:tpr:edfpol:v:8:y:2013:i:1:p:100-119
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    References listed on IDEAS

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    1. Petra E. Todd & Kenneth I. Wolpin, 2007. "The Production of Cognitive Achievement in Children: Home, School, and Racial Test Score Gaps," Journal of Human Capital, University of Chicago Press, vol. 1(1), pages 91-136.
    2. Martin Davies & Joe Hirschberg & Jenny Lye & Carol Johnston & Ian Mcdonald, 2007. "Systematic Influences On Teaching Evaluations: The Case For Caution," Australian Economic Papers, Wiley Blackwell, vol. 46(1), pages 18-38, March.
    3. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 318, University of California, Davis, Department of Economics.
    4. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    5. Cameron, A. Colin & Gelbach, Jonah B. & Miller, Douglas L., 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 238-249.
    6. Tim R. Sass, 2006. "Charter Schools and Student Achievement in Florida," Education Finance and Policy, MIT Press, vol. 1(1), pages 91-122, January.
    7. Ladd, Helen F. & Walsh, Randall P., 2002. "Implementing value-added measures of school effectiveness: getting the incentives right," Economics of Education Review, Elsevier, vol. 21(1), pages 1-17, February.
    8. Scott E. Carrell & James E. West, 2010. "Does Professor Quality Matter? Evidence from Random Assignment of Students to Professors," Journal of Political Economy, University of Chicago Press, vol. 118(3), pages 409-432, June.
    9. Horacio Matos-Diaz & James Ragan, 2010. "Do student evaluations of teaching depend on the distribution of expected grade?," Education Economics, Taylor & Francis Journals, vol. 18(3), pages 317-330.
    10. Eric A. Hanushek, 1979. "Conceptual and Empirical Issues in the Estimation of Educational Production Functions," Journal of Human Resources, University of Wisconsin Press, vol. 14(3), pages 351-388.
    11. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 107, University of California, Davis, Department of Economics.
    12. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages 3-33, February.
    13. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    14. Langbein, Laura, 2008. "Management by results: Student evaluation of faculty teaching and the mis-measurement of performance," Economics of Education Review, Elsevier, vol. 27(4), pages 417-428, August.
    15. Paul Isely & Harinder Singh, 2005. "Do Higher Grades Lead to Favorable Student Evaluations?," The Journal of Economic Education, Taylor & Francis Journals, vol. 36(1), pages 29-42, January.
    16. Cory Koedel, 2010. "Grading Standards in Education Departments at Universities," Working Papers 1002, Department of Economics, University of Missouri, revised 13 Jun 2011.
    17. Bruce A. Weinberg & Masanori Hashimoto & Belton M. Fleisher, 2009. "Evaluating Teaching in Higher Education," The Journal of Economic Education, Taylor & Francis Journals, vol. 40(3), pages 227-261, July.
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    Cited by:

    1. Joonmo Cho & Wonyoung Baek, 2019. "Identifying Factors Affecting the Quality of Teaching in Basic Science Education: Physics, Biological Sciences, Mathematics, and Chemistry," Sustainability, MDPI, vol. 11(14), pages 1-18, July.

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    More about this item

    Keywords

    student evaluations; student achievement; American University of Sharjah;
    All these keywords.

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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