2017 General Teacher Prep Programs Policy
The state should collect and publicly report key data on the quality of teacher preparation programs. This goal was reorganized in 2017.
Student Growth Data: The District of Columbia collects data that connect student growth to teacher preparation programs as part of a project called the DC Staffing Data Collaborative. The participation of districts in this project is not mandatory, but currently 90 percent of the District of Columbia's local school districts participate. Student growth data linked to the individual teacher are collected by districts and this information is made available to the District of Columbia via the data collaborative.
Additional Program Data: The District of Columbia collects other objective, meaningful data to measure the performance of teacher preparation programs, including annual summary licensure test pass rates and program completer information. The DC Staffing Data Collaborative tracks the number of teachers each program produces, the percentage of teachers of color and male teachers, the percentage of teachers teaching high-need subjects and high-poverty schools, and rates of teachers who plan to stay in their local school district based on a teacher survey.
http://osse.dc.gov/service/educator-preparation-program-approval-and-accreditation District of Columbia Educator Preparation Profiles http://osse.dc.gov/page/district-columbia-educator-preparation-profiles DC Staffing Data Collaborative https://osse.dc.gov/publication/dc-staffing-data-collaborative https://osse.dc.gov/sites/default/files/dc/sites/osse/publication/attachments/Data%20Collaborative%20One-Pager.pdf
Collect data that connect student growth to teacher preparation programs, when those programs are large enough for the data to be meaningful and reliable.
The District of Columbia should consider collecting, storing, and analyzing the academic achievement gains of students taught by programs' graduates within the District of Columbia Office of the State Superintendent of Education, rather than using a third party research partner. The District should collect these data averaged over the first three years of teaching, when the programs produce enough graduates for those data to be meaningful and reliable. The District should also ensure that all data can be disaggregated at the program level, rather than the level of the institution. Data that are aggregated at the institution level (e.g., combining elementary and secondary programs), rather than disaggregated by the specific preparation program, have less utility for accountability and continuous improvement purposes than more specific data because institution-level data aggregation can mask significant differences in performance among programs.
Gather other meaningful data that reflect program performance.
Rather than collecting this data through the DC Staffing Data Collaborative, the District should ensure that it has laws in place to require the collection of these data.
The District of Columbia was helpful in providing NCTQ with the facts necessary for this analysis.
The District also indicated that all information collected on educator preparation program effectiveness is shared with programs and used by the District to inform monitoring and evaluation of the programs, and that DC Staffing Data Collaborative data will be made public by January 2018 on the District of Columbia Office of the State Superintendent of Education website.
1C: Program Performance Measures
The state should examine a number of factors when measuring the performance of and approving teacher preparation programs. Although the quality of both the subject-matter preparation and professional sequence is crucial, there are also additional measures that can provide the state and the public with meaningful, readily understandable indicators of how well programs are doing when it comes to preparing teachers to be successful in the classroom.
States have made great strides in building data systems with the capacity to provide evidence of teacher performance. These same data systems can be used to link teacher effectiveness to the teacher preparation programs from which they came. States should make such data, as well as other objective measures that go beyond licensure test pass rates, central components of their teacher preparation program approval processes, and they should establish precise standards for performance that are more useful for accountability purposes.
National accrediting bodies, such as CAEP, are raising the bar, but are no substitute for states' own policy. A number of states now have somewhat more rigorous academic standards for admission by virtue of requiring that programs meet CAEP's accreditation standards. However, whether CAEP will uniformly uphold its standards (especially as they have already backtracked on the GPA requirement) and deny accreditation to programs that fall short of these admission requirements remains to be seen. Clear state policy would eliminate this uncertainty and send an unequivocal message to programs about the state's expectations.