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Using Artificial Societies to Understand the Impact of Teacher Student Match on Academic Performance: The Case of Same Race Effects

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  • Guillermo Montes

Abstract

This paper presents an agent-based model of the standard U.S. k-12th grade classroom using NetLogo. By creating an artificial society, we identify the casual implications of the same-race effect (a moderate sized academic boost to students whose teachers have the same race) on the national educational achievement trends. The model predicts sizeable achievement gaps at the national level, consistent in size with those documented by the US National Report Card (NAEP) stemming from moderate sized same race effects. In addition, matching effects are found to be a source of increased heterogeneity in academic performance for the minority group. These results hold for all teacher-student matching phenomena and have implications for educational policy at the aggregate level. Using artificial societies to disentangle the aggregate effects of hypothesized causes of the achievement gap is a promising strategy that merits further research.

Suggested Citation

  • Guillermo Montes, 2012. "Using Artificial Societies to Understand the Impact of Teacher Student Match on Academic Performance: The Case of Same Race Effects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(4), pages 1-8.
    • Handle: RePEc:jas:jasssj:2011-74-3
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    References listed on IDEAS

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    1. Erik Hanushek & F. Welch (ed.), 2006. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 1, number 1, June.
    2. Ronald G. Ehrenberg & Daniel D. Goldhaber & Dominic J. Brewer, 1995. "Do Teachers' Race, Gender, and Ethnicity Matter? Evidence from the National Educational Longitudinal Study of 1988," ILR Review, Cornell University, ILR School, vol. 48(3), pages 547-561, April.
    3. Kukla-Acevedo, Sharon, 2009. "Do teacher characteristics matter? New results on the effects of teacher preparation on student achievement," Economics of Education Review, Elsevier, vol. 28(1), pages 49-57, February.
    4. Erik Hanushek & F. Welch (ed.), 2006. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 2, number 2, June.
    5. Gary L. St. C. Oates, 2003. "Teacher‐Student Racial Congruence, Teacher Perceptions, and Test Performance," Social Science Quarterly, Southwestern Social Science Association, vol. 84(3), pages 508-525, September.
    6. Douglas N. Harris, 2009. "Would Accountability Based on Teacher Value Added Be Smart Policy? An Examination of the Statistical Properties and Policy Alternatives," Education Finance and Policy, MIT Press, vol. 4(4), pages 319-350, October.
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    Cited by:

    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 94-121, March.
    2. Matt Kasman & Brynle Owen & Joshua Hayward, 2017. "A Community-based Complex Systems Approach to High School Completion," Systems Research and Behavioral Science, Wiley Blackwell, vol. 34(3), pages 267-276, May.
    3. Annie TUBADJI & Vassilis ANGELIS & Peter NIJKAMP, 2019. "Micro-Cultural Preferences and Macro-Percolation of New Ideas: A NetLogo Simulation," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(1), pages 168-185, March.

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