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Skills, productivity and the evaluation of teacher performance

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  • Harris, Douglas N.
  • Sass, Tim R.

Abstract

We examine the relationships between observational ratings of teacher performance, principals’ evaluations of teachers’ cognitive and non-cognitive skills and test-score based measures of teachers’ productivity. We find that principals can distinguish between high and low performing teachers, but the overall correlation between principal ratings of teachers and teachers’ value-added contribution to student achievement is modest. The variation across metrics occurs in part because they are capturing different traits. While past teacher value-added predicts future value-added, principals’ subjective ratings can provide additional information, particularly when prior value-added measures are based on a single year of teacher performance.

Suggested Citation

  • Harris, Douglas N. & Sass, Tim R., 2014. "Skills, productivity and the evaluation of teacher performance," Economics of Education Review, Elsevier, vol. 40(C), pages 183-204.
  • Handle: RePEc:eee:ecoedu:v:40:y:2014:i:c:p:183-204
    DOI: 10.1016/j.econedurev.2014.03.002
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    Cited by:

    1. Cheng, Albert & Zamarro, Gema, 2018. "Measuring teacher non-cognitive skills and its impact on students: Insight from the Measures of Effective Teaching Longitudinal Database," Economics of Education Review, Elsevier, vol. 64(C), pages 251-260.
    2. Figlio, D. & Karbownik, K. & Salvanes, K.G., 2016. "Education Research and Administrative Data," Handbook of the Economics of Education,, Elsevier.
    3. Gershenson, Seth, 2021. "Identifying and Producing Effective Teachers," IZA Discussion Papers 14096, Institute of Labor Economics (IZA).
    4. Koedel, Cory & Mihaly, Kata & Rockoff, Jonah E., 2015. "Value-added modeling: A review," Economics of Education Review, Elsevier, vol. 47(C), pages 180-195.
    5. Matthew A. Kraft & John P. Papay & Olivia L. Chi, 2020. "Teacher Skill Development: Evidence from Performance Ratings by Principals," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 315-347, March.
    6. Li Feng & Tim R. Sass, 2017. "Teacher Quality and Teacher Mobility," Education Finance and Policy, MIT Press, vol. 12(3), pages 396-418, Summer.
    7. Cory Koedel & Mark Ehlert & Eric Parsons & Michael Podgursky, 2012. "Selecting Growth Measures for School and Teacher Evaluations," Working Papers 1210, Department of Economics, University of Missouri.
    8. Eric Parsons & Cory Koedel & Li Tan, 2019. "Accounting for Student Disadvantage in Value-Added Models," Journal of Educational and Behavioral Statistics, , vol. 44(2), pages 144-179, April.
    9. Goel, Deepti & Barooah, Bidisha, 2018. "Drivers of Student Performance: Evidence from Higher Secondary Public Schools in Delhi," GLO Discussion Paper Series 231, Global Labor Organization (GLO).
    10. Dominic Coey & Kenneth Hung, 2022. "Empirical Bayesian Selection for Value Maximization," Papers 2210.03905, arXiv.org.
    11. Jason A. Grissom & Susanna Loeb, 2017. "Assessing Principals’ Assessments: Subjective Evaluations of Teacher Effectiveness in Low- and High-Stakes Environments," Education Finance and Policy, MIT Press, vol. 12(3), pages 369-395, Summer.
    12. Marc Steeg & Sander Gerritsen, 2016. "Teacher Evaluations and Pupil Achievement Gains: Evidence from Classroom Observations," De Economist, Springer, vol. 164(4), pages 419-443, December.

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    More about this item

    Keywords

    Teacher evaluations; Observational ratings; Value-added;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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