At the intersection of policy pushes to assess teacher
effectiveness and to improve accountability systems for teacher preparation
programs lie state efforts to connect student achievement data to preparation
programs. About a dozen states have
begun or have announced plans to link student learning gains to programs, and
many more will likely follow suit, especially if the long-awaited new HEA regulations make doing so a federal requirement.
The use of student achievement data holds great promise because
it allows objective comparison of one program with another in the same state
and can help institutions improve program quality. With this great value,
however, comes great challenge. Building
strong growth or value added models for teacher preparation programs is no
small task.
We’ve taken a close look at the experience of early adopters
and, in a new brief, NCTQ offers six core principles for strong design
based on the models developed in three pioneering states: Louisiana, North Carolina and
Tennessee.
1.
Data need to be sufficiently specific,
generating findings for individual programs within an institution.
2.
Identifying the outliers–the programs at the
highest and lowest ends of the spectrum–is what’s most important.
3.
Use an absolute standard of new teacher
performance for comparison.
4.
Try to keep politics out of the technical design
of the model.
5.
Check the impact of the distribution of graduates
among K-12 schools in a state.
6.
Findings must be clearly communicated.
Just as student growth data have an important role to play
as one of multiple measures in teacher evaluation, so too can they be used as
part of a multifaceted assessment of teacher preparation programs. These principles can help states to maximize
their models’ usefulness in identifying high and low performing programs and
helping programs to improve. Read more
here.
