State Data Systems: Georgia

2011 Identifying Effective Teachers Policy

Goal

The state should have a data system that contributes some of the evidence needed to assess teacher effectiveness.

Meets
Suggested Citation:
National Council on Teacher Quality. (2011). State Data Systems: Georgia results. State Teacher Policy Database. [Data set].
Retrieved from: https://www.nctq.org/yearbook/state/GA-State-Data-Systems-8

Analysis of Georgia's policies

Georgia has a data system with the capacity to provide evidence of teacher effectiveness.

Georgia has all three necessary elements of a student- and teacher-level longitudinal data system. The state has assigned unique student identifiers that connect student data across key databases across years and has assigned unique teacher identifiers that enable it to match individual teacher records with individual student records. It also has the capacity to match student test records from year to year in order to measure student academic growth.

Citation

Recommendations for Georgia

Develop a clear definition of "teacher of record."
Georgia has not yet established a definition of teacher of record, which is essential in order to use the student-data link for the purpose of providing value-added evidence of teacher effectiveness. To ensure that data provided through the state data system are actionable and reliable, Georgia should articulate a definition of teacher of record and require its consistent use throughout the state.

State response to our analysis

Georgia noted that it has the capacity to match students to teachers in all grades within a newly designed state longitudinal data system, which will comprehensively link districts' student information to the state databases. The state added that its data collection process specifies procedures and specifications for the selection of a teacher of record for a corresponding course, and the system will allow local districts to populate courses with multiple teachers in order to account for co-taught and cooperative classroom environments. The system will be used as part of the Teacher Effectiveness Measure currently being developed in Georgia.  
 
The state also pointed out that it is developing a clear definition of teacher of record, as part of its work for Race to the Top. 

How we graded

Value-added analysis connects student data to teacher data to measure achievement and performance.

Value-added models are an important tool for measuring student achievement and school effectiveness. These models measure individual students' learning gains, controlling for students' previous knowledge. They can also control for students' background characteristics. In the area of teacher quality, value-added models offer a fairer and potentially more meaningful way to evaluate a teacher's effectiveness than other methods schools use.
For example, at one time a school might have known only that its fifth-grade teacher, Mrs. Jones, consistently had students who did not score at grade level on standardized assessments of reading. With value-added analysis, the school can learn that Mrs. Jones' students were reading on a third-grade level when they entered her class, and that they were above a fourth-grade performance level at the end of the school year. While not yet reaching appropriate grade level, Mrs. Jones' students had made more than a year's progress in her class. Because of value-added data, the school can see that she is an effective teacher.

The school could not have seen this effectiveness without a data system that connects student and teacher data. Furthermore, multiple years of data are necessary to enable meaningful determinations of teacher effectiveness. Value-added analysis requires both student and teacher identifiers and the ability to match test records over time.

It is an inefficient use of resources for individual districts to build their own data systems for value-added analyses.

States need to take the lead and provide districts with state-level data that can be used for the purpose of measuring teacher effectiveness.  All states have longitudinal data systems, but not all states are yet able to connect student data to individual teacher records.  Such data is useful not just for teacher evaluation but also to measure overall school performance and the performance of teacher preparation programs. 

Research rationale

The Data Quality Campaign tracks the development of states' longitudinal data systems by reporting annually on states' inclusion of 10 elements in their data systems. Among these 10 elements are the three key elements (Elements 1, 3 and 5) that NCTQ has identified as being fundamental to the development of value-added assessment. For more information, see http://www.dataqualitycampaign.org.

For information about the use of student-growth models to report on student-achievement gains at the school level, see P. Schochet and H. Chiang, "Error Rates in Measuring Teacher and School Performance Based on Student Test Score Gains." Mathematica Policy Research. Department of Education (2010); as well as The Commission on No Child Left Behind, "Commission Staff Research Report: Growth Models, An Examination Within the Context of NCLB," Beyond NCLB, 2007.

For information about the differences between accountability models, including the differences between growth models and value-added growth models, see Pete Goldschmidt, et al., "Policymakers' Guide to Growth Models for School Accountability: How Do Accountability Models Differ?" Council of Chief State School Officers' Report, 2005 at: http://www.ccsso.org/publications/details.cfm?PublicationID=287

For information regarding the methodologies and utility of value-added analysis see, C. Koedel and J. Betts, "Does Student Sorting Invalidate Value-Added Models of Teacher Effectiveness? An Extended Analysis of the Rothstein Critique." Education Finance and Policy Vol. 6 No. 1 (2011), D. Goldhaber and M. Hansen, "Assessing the Potential of Using Value-Added Estimates of Teacher Job Performance for Making Tenure Decisions." Urban Institute (2010), and S. Glazerman et al, "Evaluating Teachers; The Important Role of Value-Added." Brookings Brown Center Task Group on Teacher Quality (2011); Glazerman, Steven et. al., Passing Muster: Evaluating Teacher Evaluation Systems, The Brookings Brown Center Task Group on Teacher Quality (2011); Harris, D.N.  (2009). "Teacher value-added: Don't end the search before it starts," Journal of Policy Analysis and Management, 28(4), pp. 693-699. Hill, H.C. (2009). "Evaluating value-added models: A validity argument approach," Journal of Policy Analysis and Management, 28(4), pp. 700-709; Kane, T.J. & Staiger, D.O. (2008). Estimating teacher impacts on student achievement: An experimental evaluation. NBER Working Paper W14607. Cambridge, MA: National Bureau of Economic Research.

There is no shortage of studies using value-added methodologies by researchers including Thomas J. Kane, Eric Hanushek, Steven Rivkin, Jonah E. Rockoff and Jessie Rothstein. See also Kane, T.J. 2008. Estimating teacher impacts on student achievement: An experimental evaluation. Working Paper 14607. Cambridge, MA: National Bureau of Economic Research; Hanushek, Erik A. and Steven G. Rivkin. "Generalizations about using value-added measures of teacher quality." American Economic Review (May 2010); Rothstein, Jesse. 2010. "Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement." Quarterly Journal of Economics, 25(1); Kane, Thomas J. and Douglas O. Staiger. 2008. "Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation." National Bureau of Economic Research W14607, December. Rivkin, Steven G.; Eric A. Hanushek and John F. Kain. 2005. "Teachers, Schools, and  Academic Achievement." Econometrica, 73(2), pp. 417-58; Hanushek, Eric A. 2010. "The Difference is Teacher Quality." In Waiting for "Superman": How We Can Save America's Failing Public Schools, Karl Weber, ed. New York: Public Affairs.

See also NCTQ's "If Wishes Were Horses" by Kate Walsh at: http://www.nctq.org/p/publications/docs/wishes_horses_20080316034426.pdf and the National Center on Performance Incentives at: www.performanceincentives.org.

For information about the limitations of value-added analysis, see Jesse Rothstein, "Do Value-Added Models Add Value? Tracking, Fixed Effects, and Casual Inference." Princeton University and NBER. (2007) as well as Dale Ballou, "Value-added Assessment: Lessons from Tennessee," Value Added Models in Education: Theory and Applications, ed. Robert W. Lissitz (Maple Grove, MN: JAM Press, 2005).See also Dale Ballou, "Sizing Up Test Scores," Education Next, Summer 2002; 2(2).