How To Have Effective Teachers In Every School (Or, What DC Doesn’t Do–But Should)

[Ed. Note: Enjoy the expertise of DCPS parent Betsy Wolf in this guest blog on issues surrounding the distribution, recruitment, and retention of effective teachers in DCPS. Wolf is an assistant professor in the Center for Research and Reform in Education at Johns Hopkins, where she conducts independent evaluations of K-12 reforms and policies. All academic citations not linked herein are listed in the bibliography at the end.]

By Betsy Wolf

We know that teachers are the single most important school-based factor affecting student learning (Rice, 2003). Ensuring that students in all schools have access to effective teachers is critical for academic success.

Yet, as in many other school districts, high-poverty schools in DCPS have fewer highly effective teachers compared with lower poverty schools (Gordon, Kane, & Staiger, 2006; Jackson, 2013; Sass et al., 2012).

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Credit: Betsy Wolf, 2018. Graph was created using data Mary Levy obtained from DCPS responses to questions from the city council during performance hearings. The drop box with this information is posted on the council website.

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Credit: Betsy Wolf, 2018. Graph was created using data Mary Levy obtained from DCPS responses to questions from the city council during performance hearings. The drop box with this information is posted on the council website.

One reason for such inequity is higher teacher turnover in schools with larger percentages of low-income students and students with low test scores, who are not on grade level–which affects many schools in DC.

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Credit: Betsy Wolf, 2018. Turnover data for all staff (not just teachers) here is from the public drop box for the council education committee. PARCC data is from OSSE.

As DC public school analyst Mary Levy has documented, DCPS’s new hires alone leave at a rate of 25% per year, with staff leaving the 40 lowest-performing (and highest poverty) DCPS schools at an average rate of 33% per year. Studies of other jurisdictions have found similar results. For instance, a typical school in Chicago will lose half of its teachers within five years, and the 100 most disadvantaged schools there will lose 25% of their teachers each year (Allensworth, Ponisciak, & Mazzeo, 2009).

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Credit: Betsy Wolf, 2018. Graph was created using data obtained via FOIA by Mary Levy from DCPS in SY2017–18.

As the graph above shows, teachers in high-poverty schools in DCPS have fewer years of experience in the system. That means that teachers are either moving to more affluent schools or leaving the system altogether, which creates teacher churn in our most disadvantaged schools.

The effects of such teacher churn are particularly pernicious, given that most of our publicly funded schools, particularly in DCPS, have large proportions of low-income students.

As it is, schools with high-poverty populations often have challenges that high-income schools don’t and thus need more instructional resources, including effective teachers, to increase student achievement. Yet in DC, low-income schools most often have fewer instructional resources—and less effective teachers—on average than high-income schools.

Since teacher mobility appears to be at the heart of the inequitable distribution of effective teachers in DCPS, to solve it we need to understand it. We know from research that effective teachers tend to leave schools serving largely disadvantaged student populations for schools serving more advantaged populations (Boyd, Lankford, Loeb, & Wyckoff, 2005; Boyd et al., 2009; Feng & Sass, 2011; Feng & Sass, 2015; Hanushek, Kain, & Rivkin, 2004; Hanushek & Rivkin, 2006; Rivkin, Hanushek, & Kain, 2005; Xu et al., 2012).

One factor contributing to this pattern is that effective teachers tend to go to schools where teacher quality is most like their own, and thus end up in schools serving more advantaged students (Feng & Sass, 2011). Other factors, such as a school’s proximity to home or accountability pressure, also contribute to this pattern (Boyd et al., 2005; Feng, 2010).

At issue in DC as well are teachers’ perceptions that DCPS’s teacher evaluation system (IMPACT) is unfair to teachers who work in high-poverty schools. Under IMPACT, 50% of a teacher’s score comes from student learning gains, and 30% comes from classroom observations. In terms of increasing student learning, research has shown that a teacher who is effective is generally effective in any context (high- or low-poverty) (Glazerman et al., 2013; Lockwood & McCaffrey, 2009). However, research has also shown that teachers have lower returns on years of experience in high-poverty schools (Sass et al., 2012): it simply takes longer to reach a level of effectiveness because teachers there have to do so much more than just teach. In addition, classroom observations have been found to be negatively biased against teachers working in high-poverty schools (Steinberg & Garrett, 2016; Whitehurst et al., 2014).

The pressure inherent in such accountability can be a stressor by itself. If you know your job depends on how much students have learned, or how well they take a test on any given day, and you also know that your students are behind grade level so the likelihood of them scoring well is low under the best of circumstances, that’s stressful. If you know that someone is coming into your classroom to observe you and that will influence your rating (and thus your salary and your job), and you don’t know if a certain student will have a bad day and act out, that’s stressful.

As a result, teachers have many incentives to move to schools with less poverty–and DCPS is not doing much to stop them. Research shows that teachers move to schools where they can feel successful, and teaching in high-poverty schools is hard work. You’re not just teaching: you’re also trying to be a social worker and deal with trauma; acquire necessary classroom and technological resources; reach out to parents; and manage classroom behavior.

These hardships are often exacerbated by DCPS’s lack of support. Here are some examples just from my child’s school:

–Teachers don’t have working computers, yet the mandated curriculum requires blended learning, and student assessments are taken on computers. Teachers resort to Donors Choose to bring in computers, and then computers are trashed when they need repairs because there is no one to repair them.

–DCPS provides minimal support for kids experiencing trauma. A social worker told me it can take up to two years for an appropriate placement to be identified for a child who is particularly struggling. When a child is not in the right placement and/or doesn’t have adequate supports, it’s a lot harder for the teacher to manage classroom behaviors and focus on instruction.

–DCPS doesn’t consider class size to be an important factor affecting student learning, despite a general consensus among researchers that class size matters for children in high-poverty schools in grades K-3. Large class sizes for kids who are multiple years behind grade level makes for an impossible teaching assignment, even for the best of teachers. That’s because in these situations, teachers need to spend more one-on-one time with individual students, which is challenging when class sizes are too large.

To be clear, it’s not wrong to have rigorous teacher evaluation systems—but in a school district like DCPS, with relatively few ineffective teachers to begin with, why is weeding out teachers the most talked-about policy solution when it also results in losing effective ones as well? Also, because student learning gains (a key part of IMPACT) have been available for only 17% of teachers in DCPS (Dee & Wyckoff, 2015), to the extent that IMPACT has rigor it is not seen in student performance. (In fact, a recent report showed that more rigorous teacher evaluations systems do not improve student performance.)

A growing body of research suggests that teachers do respond to financial incentives to remain at high-poverty schools–but that such incentives may need to be large and recurring to retain effective teachers in those schools (Glazerman et al., 2013; Springer et al., 2016).

Moreover, research shows that working conditions are still very important to teachers, regardless of salary (Horng, 2009; Milanowski et al., 2009). For example, Horng (2009) was able to disentangle preservice teacher preferences via a survey for elementary school teachers and found that an $8,000 difference in salary was not as important to teachers in selecting a school as facilities, administrative support, class sizes, or commuting times. Findings by Liu, Johnson, and Peske (2004) also suggested that recruiting teachers was not adequate; more focus was needed on retaining teachers and building teachers’ capacity.

This suggests a path ahead for our publicly funded schools that simply has not been approached effectively in DC.

Although DCPS provides generous bonuses to teachers for teaching in high-poverty schools, those bonuses are provided only under two conditions: having a highly effective rating and permanently giving up rights under excessing. (DCPS provides smaller bonuses to teachers in low-poverty schools–with the same conditions; see page 35ff of the contract here.)

Possibly worse, effective teachers in high-income schools have few incentives to move to low-income schools because they may be concerned that the move will hurt their effectiveness rating.

In other words, DCPS’s system places all of the risk of teaching at high-poverty schools on teachers—with no additional supports. Worse, this lack of support goes in many directions. Every year, for instance, good principals ask their best teachers what they need to stay, but there’s only so much each school leader can change. A school leader can’t acquire computers if they are lacking or hire effective teachers in the middle of the school year.

Research also suggests that teachers may be more willing to work or remain in low-achieving schools if they have a group of effective peers. One study of Teach for America (TFA) participants found that teacher retention in a school improved when TFA participants were placed in groups in each school during the 2-year program (Hansen, Backes, & Brady, 2016). Emerging evidence from other reforms suggests that effective teachers were more likely to move to high-needs schools when other effective teachers were willing to do the same (Partee, 2014).

Given the harmful effects of the inequitable distribution of effective teachers in DCPS, the question is whether city leaders will avail themselves of this research and use it to inform their decision making and policies going forward. The simple act of going into schools and asking teachers what do they need to stay is the first step. The second is to use what has been proven to work. And the third step is to revisit both, with the active collaboration of teachers.

In a city where competition rules the day in so many things, including our public schools, collaboration may seem old-fashioned. But to recruit, and retain, effective teachers in low-income schools, collaboration is the first, perhaps most important, step.

Bibliography

Allensworth, E., Ponisciak, S., & Mazzeo, C. (2009). The Schools Teachers Leave: Teacher Mobility in Chicago Public Schools. Consortium on Chicago School Research.

Boyd, D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2009). Who leaves? Teacher attrition and student achievement. Washington, DC: Urban Institute.

Boyd, D., Lankford, H., Loeb, S., & Wyckoff, J. (2005). The Draw of Home: How Teachers’ Preferences for Proximity Disadvantage Urban Schools. Journal of Policy Analysis & Management, 24(1), 113–132.

Dee, T. & Wyckoff, J. (2015). Incentives, Selection, and Teacher Performance: Evidence from IMPACT. Journal of Policy Analysis and Management, 34(2), 267-297.

Feng, L. (2010). Hire today, gone tomorrow: New teacher classroom assignments and teacher mobility. Education Finance and Policy, 5(3), 278–316.

Feng, L., & Sass, T. (2011). Teacher Quality and Teacher Mobility (Working Paper No. 57). Washington, D.C.: National Center for Analysis of Longitudinal Data in Education Research, The Urban Institute.

Feng, L., & Sass, T. R. (2015). The impact of incentives to recruit and retain techers in “hard to staff” subjects: An analysis of the Florida Critical Teacher Shortage Program. Washington, DC: Urban Institute.

Glazerman, S., Protik, A., Teh, B., Bruch, J., & Max, J. (2013). Transfer incentives for high-performing teachers: Final results from a multisite randomized experiment (NCEE 2014–4004). Washington, DC: National Center for Education Evaluation and Regional Assistance.

Gordon, R., Kane, T. J., & Staiger, D. O. (2006). Identifying effective teachers using performance on the job. Discussion Paper Series (Hamilton Project), 1(1).

Hansen, M., Backes, B., & Brady, V. (2016). Teacher attrition and moblity during the Teach for Amercian clustering strategy in Miami-Dade Public Schools. Educational Evaluation and Policy Analysis, 38(3), 495–516.

Hanushek, E.A., Kain, J., & Rivkin, S. (2004). Why Public Schools Lose Teachers. Journal of Human Resources, 39(2), 326–354.

Hanushek, E.A., & Rivkin, S. (2006). Teacher Quality. In E. Hanushek & F. Welch (Eds.), Handbook of the Economics of Education (Vol. 2). Elsevier.

Horng, E. (2009). Teacher Tradeoffs: Disentangling Teachers’ Preferences for Working Conditions and Student Demographics. American Educational Research Journal, 46(3), 690–717.

Jackson, C. K. (2013). Match quality, worker productivity, and worker mobility: Direct evidence from teachers. Review of Economics and Statistics, 95(4), 1096–1116.

Liu, E., Johnson, S. M., & Peske, H. G. (2004). New Teachers and the Massachusetts Signing Bonus: The Limits of Inducements. Educational Evaluation and Policy Analysis, 26(3), 217–236.

Liu, K. (2010). Peer group effects on student outcomes: Evidence from randomized lotteries (Doctoral dissertation). Vanderbilt University, Nashville.

Lockwood, J. R., & McCaffrey, D. F. (2009). Exploring student-teacher interactions in longitudinal achievement data. Education Finance and Policy, 4(4), 439–467.

Milanowski, A. T., Longwell-Grice, H., Saffold, F., Jones, J., Schomisch, K., & Odden, A. (2009). Recruiting New Teachers to Urban School Districts: What Incentives Will Work? International Journal of Education Policy and Leadership, 4(8).

Partee, G. L. (2014). Attaining equitable distribution of effective teachers in public schools. Washington, DC: Center for American Progress.

Rice, J. (2003). Teacher quality. Washington, DC: Economic Policy Institute.

Rivkin, S., Hanushek, E., & Kain, J. (2005). Teachers, Schools, and Academic Achievement. Econometrica, 73(2), 417–458.

Sass, T. R. (2008). The stability of value-added measures of teacher quality and implications for teacher compensation policy (Brief 4). Washington, DC: National Center for Analysis of Longitudinal Data in Education Research.

Sass, T., Hannaway, J., Xu, Z., Figlio, D., & Feng, L. (2012). Value added of teachers in high-poverty schools and lower-poverty schools. Journal of Urban Economics, 72(2–3), 104–122.

Springer, M. G., Swain, W. A., & Rodriguez, L. A. (2016). Effective teacher retention bonuses: Evidence from Tennesse. Educational Evaluation and Policy Analysis, 38(2), 199–221.

Steinberg, M. P., & Garrett, R. (2016). Classroom composition and measured teacher performance: What do teacher observation scores really measure? Educational Evaluation and Policy Analysis, 38(2), 293–317.

Whitehurst, G., Chingos, M., & Lindquist, K. (2014). Evaluating teachers with classroom observations: Lessons learned in four districts. Brown Center on Education Policy at the Brookings Institution.

Xu, Z., Ozek, U., & Corritore, M. (2012). Portability of Teacher Effectiveness across School Settings. Working Paper 77. National Center for Analysis of Longitudinal Data in Education Research.

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