Teachers and Student Achievement in the Chicago Public High Schools

Aaronson, Daniel; Barrow, Lisa; Sander, William

doi:10.1086/508733

Cited by 940 publications

(837 citation statements)

References 30 publications

Supporting

Mentioning

778

Contrasting

Unclassified

Order By: Relevance

“…The partition in Eq. 1′ is by subject (math and reading) and grade (4)(5)(6)(7)(8), giving L = 2 × 5 = 10 cells, with weights W l i as in Eq. 5.…”

Section: Resultsmentioning

confidence: 99%

Predictive effects of teachers and schools on test scores, college attendance, and earnings

Chamberlain

2013

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

I studied predictive effects of teachers and schools on test scores in fourth through eighth grade and outcomes later in life such as college attendance and earnings. For example, predict the fraction of a classroom attending college at age 20 given the test score for a different classroom in the same school with the same teacher and given the test score for a classroom in the same school with a different teacher. I would like to have predictive effects that condition on averages over many classrooms, with and without the same teacher. I set up a factor model that, under certain assumptions, makes this feasible. Administrative school district data in combination with tax data were used to calculate estimates and do inference.education production function | unmeasured inputs | teacher effects T he outcome data are based on elementary and middle school classrooms, grades four through eight. For a classroom, there is an average score based on a math or reading test given near the end of the school year. There are also later outcome measures for that classroom. These measures include the fraction of the classroom that is attending college at age 20 and the average earnings of the classroom at age 28. The classrooms can be grouped by schools, and, within a school, can be grouped by teacher.The goal of the paper is to provide predictive effects of teachers and schools on these outcomes. For example, predict the fraction of a classroom attending college at age 20 given the test score for a different classroom in the same school with the same teacher and given the test score for a classroom in the same school with a different teacher. Or predict the fraction of a classroom attending college at age 20 given the fraction attending college for a different classroom with the same teacher and given the fraction attending college for a classroom in the same school with a different teacher. I would like to have predictive effects that condition on averages over many classrooms, with and without the same teacher. I set up a factor model that, under certain assumptions, makes this feasible. Then I can define teacher and school factors based on test score data and measure the predictive effect of the teacher factor on college attendance. More directly, I can define teacher and school factors based on the college attendance data and measure the predictive effect of the teacher factor on college attendance.These predictive effects can be based on residuals, where first we form predictions based on observed variables (X) such as class size, years of teacher experience, lagged test scores, and parent characteristics. The residuals are the prediction errors. Then the teacher and school effects that I measure in these residuals correspond to unmeasured (latent) variables or, more precisely, to the parts of those latent variables that are not predictable using the observed variables in X. I am interested in these latent variables because they may be related to unmeasured characteristics of teachers that have a causal effect on outcomes,...

show abstract

“…The partition in Eq. 1′ is by subject (math and reading) and grade (4)(5)(6)(7)(8), giving L = 2 × 5 = 10 cells, with weights W l i as in Eq. 5.…”

Section: Resultsmentioning

confidence: 99%

Predictive effects of teachers and schools on test scores, college attendance, and earnings

Chamberlain

2013

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…The results of these research efforts made clear that about 15% to 25% of the differences in students' achievement might be explained by the work of teachers (Aaronson, Barrow, & Sander, 2007;Bosker & Witziers, 1996;Brandsma & Knuver, 1989;Houtveen & Van de Grift, 2007a, 2007bHoutveen, Van de Grift, & Brokamp, 2014;Houtveen, Van de Grift, & Creemers, 2004;Rockoff, 2004;Roeleveld, 2003;Wijnstra, Ouwens, & Béguin, 2003). Students of teachers whose teaching quality is a full standard deviation higher, achieve 10% to 25% more learning gains (Aaronson et al, 2007;Bosker & Witziers, 1996;Brandsma & Knuver, 1989;Hanushek & Rivkin, 2010;Houtveen & Van de Grift, 2007a, 2007bKane & Staiger, 2008;Rivkin, Hanushek, & Kain, 2005;Rockoff, 2004;Roeleveld, 2003;Wijnstra et al, 2003).…”

Section: International Comparisons On the Quality Of Teachingmentioning

confidence: 99%

“…Students of teachers whose teaching quality is a full standard deviation higher, achieve 10% to 25% more learning gains (Aaronson et al, 2007;Bosker & Witziers, 1996;Brandsma & Knuver, 1989;Hanushek & Rivkin, 2010;Houtveen & Van de Grift, 2007a, 2007bKane & Staiger, 2008;Rivkin, Hanushek, & Kain, 2005;Rockoff, 2004;Roeleveld, 2003;Wijnstra et al, 2003). Thus, it is not only caused by the students' intelligence and socioeconomic background; the teacher matters in determining learning.…”

Section: International Comparisons On the Quality Of Teachingmentioning

confidence: 99%

Measuring teaching quality and student engagement in South Korea and The Netherlands

Grift

Chun

Maulana

et al. 2016

School Effectiveness and School Improvement

View full text Add to dashboard Cite

Six observation scales for measuring the skills of teachers and 1 scale for measuring student engagement, assessed in South Korea and The Netherlands, are sufficiently reliable and offer sufficient predictive value for student engagement. A multigroup confirmatory factor analysis shows that the factor loadings and intercepts of the scales are the same, within acceptable boundaries, in both countries. Therefore, we can compare the average scores of teachers in both countries in a reliable and valid way. The 289 Dutch teachers score significantly better on "creating a safe and stimulating learning climate" and "intensive and activating teaching" and almost significantly on "efficient classroom management". We find no significant differences in "clear and structured instruction". The 375 South Korean teachers perform significantly better than the Dutch teachers on "teaching learning strategies" and almost significantly on "differentiating instruction". Furthermore, we find better student engagement in South Korea. ARTICLE HISTORY

show abstract

“…For example, Jacob and Lefgren (2005) found that principal R CE M assessments of teachers explained only about 10 percent of the variation in classroom effects on reading and math. Similarly, Aaronson et al (2007) found that a host of teacher characteristics-including age, gender, race, educational background, tenure, and total experience-together only explained about 20 percent of the variation in classroom effects. Thus, it is likely that even a strong predictor of classroom effects could explain only a portion of this variation.…”

Section: Identifying Plausible R 2 Valuesmentioning

confidence: 93%

Do Typical RCTs of Education Interventions Have Sufficient Statistical Power for Linking Impacts on Teacher Practice and Student Achievement Outcomes?

Schochet

2011

Journal of Educational and Behavioral Statistics

View full text Add to dashboard Cite

For RCTs of education interventions, it is often of interest to estimate associations between student and mediating teacher practice outcomes, to examine the extent to which the study's conceptual model is supported by the data, and to identify specific mediators that are most associated with student learning. This paper develops statistical power formulas for such exploratory analyses under clustered school-based RCTs using ordinary least squares (OLS) and instrumental variable (IV) estimators U.S. Department of Education Arne Duncan Secretary Institute of Education Sciences John Q. Easton Director National Center for Education Evaluation and Regional Assistance John Q. Easton Acting Commissioner October 2009This report is in the public domain. While permission to reprint this publication is not necessary, the citation should be: Disclosure of Potential Conflicts of InterestThe author for this report, Dr. Peter Z. Schochet, is an employee of Mathematica Policy Research with whom IES contracted to develop the methods that are presented in this report. Dr. Schochet and other MPR staff do not have financial interests that could be affected by the content in this report. Disclosure of Potential Conflicts of Interest iii ForewordThe National Center for Education Evaluation and Regional Assistance (NCEE) conducts unbiased large-scale evaluations of education programs and practices supported by federal funds; provides research-based technical assistance to educators and policymakers; and supports the synthesis and the widespread dissemination of the results of research and evaluation throughout the United States.In support of this mission, NCEE promotes methodological advancement in the field of education evaluation through investigations involving analyses using existing data sets and explorations of applications of new technical methods, including cost-effectiveness of alternative evaluation strategies. The results of these methodological investigations are published as commissioned, peer reviewed papers, under the series title, Technical Methods Reports, posted on the NCEE website at http://ies.ed.gov/ncee/pubs/. These reports are specifically designed for use by researchers, methodologists, and evaluation specialists. The reports address current methodological questions and offer guidance to resolving or advancing the application of highquality evaluation methods in varying educational contexts.This NCEE Technical Methods paper addresses whether typical large-scale RCT designs have sufficient statistical power for meditational analyses that associate teacher practice and student achievement outcomes. These exploratory analyses are important for helping to understand key pathways through which the intervention affects student learning as hypothesized by the study's conceptual model, and for identifying specific teacher practices that are most associated with student learning. These analyses, however, will be informative only if the study has sufficient statistical power for estimating mediator-achievement...

show abstract

Teachers and Student Achievement in the Chicago Public High Schools

Cited by 940 publications

References 30 publications

Predictive effects of teachers and schools on test scores, college attendance, and earnings

Predictive effects of teachers and schools on test scores, college attendance, and earnings

Measuring teaching quality and student engagement in South Korea and The Netherlands

Do Typical RCTs of Education Interventions Have Sufficient Statistical Power for Linking Impacts on Teacher Practice and Student Achievement Outcomes?

Contact Info

Product

Resources

About