PHILOSOPHICAL PRINCIPLES

Philosophical Principles Technical Principles

1.  The content (e.g., ideas, examples, tools, instruments, methods, etc.) described herein are not intended as a mandates for data collection and analysis. Rather, the purpose of this site is to provide suggestions about how states, preparation programs, and school districts can work collaboratively to build and use a TQ data warehouse that ultimately could be used to improve student achievement by creating a better understanding of teacher production, supply, demand, mobility, turnover, and quality.

2.  Data should not be collected and analyzed in order to punish individuals, programs, or agencies. Rather, data collection and subsequent analyses should be used in a formative nature so that we all can help build a more effective education system for all children.

3.   Student achievement data from standardized tests should not be the only measure used to gauge student success. Due to the extreme complexity of identifying high-performing teachers using student achievement data and the problems inherent in many standardized test results, additional measures need to be used when making judgments about the effectiveness of teachers, schools, and preparation programs.

4.   Creating a useful TQ data warehouse and system requires the participation of state agencies, preparation programs, and school districts. Working alone, most school districts lack the human and fiscal capacity to identify high-performing teachers in a meaningful way. Likewise, if working in isolation, most preparation programs do not have the fiscal and human resources to collect and analyze data to provide evidence on their effectiveness. Finally, state education agencies by themselves cannot collect all of the relevant data necessary to fully understand the dynamics of teacher supply, demand, turnover, shortages, and quality. We do believe that federal and state governments should be the catalysts in this effort by providing some of the human and fiscal resources necessary for the creation of such data warehouses. Until that time, preparation programs should not be criticized for being unable to provide evidence about their graduates’ effectiveness in increasing student achievement.

5.  While preparation programs should not be responsible for collecting all of the data, they should invest the resources necessary to collect certain types of data (e.g., why graduates choose to teach or not teach, their perceptions of the effectiveness of the preparation program, etc). Indeed, they should use data analysis as one strategy to examine their effectiveness in producing effective teachers.

6.   States should invest in experts to ensure that student achievement examinations possess the proper psychometric properties necessary for value-added analyses. Too many states do not make such investments, thus leading to erroneous conclusions from data analyses.

7.   School districts, preparation programs, and state education agencies should collect and value both quantitative and qualitative data. Knowing which teachers and teacher education programs are better is of little use if the data do not show why. Quantitative data are best suited for the former, whereas qualitative data are necessary for the latter. Policymakers should value both types of data as evidence of the effectiveness of schools, districts, and preparation programs.

Last updated: February 6, 2006