Contributed by:

School districts often struggle to recruit and retain effective math teachers. Alternative-route certification programs aim to expand the pool of teachers available; however, many alternate routes have not been able to attract large numbers of teacher candidates with undergraduate degrees in math. In response, some districts, including Baltimore, Philadelphia, Washington D.C., and New York City, have developed alternative programs with a math immersion component to recruit candidates who do not have undergraduate majors in math. Such programs provide potential math teachers with intensive math preparation to meet state certification requirements while at the same time maintaining an early-entry approach in which individuals who have not completed a teacher preparation program can become qualified to teach with only five to seven weeks of coursework and practice teaching. Four years since its inception, the New York City Teacher Fellows Math Immersion program supplies 50 percent of all newly certified math teachers to New York City public schools. In this study, we find that Math Immersion teachers have stronger academic qualifications than their College Recommending (traditionally certified) peers, although they have weaker qualifications than Teach for America teachers. However, despite stronger general academic qualifications Math Immersion teachers produce somewhat smaller gains in math achievement for middle school math students than do College Recommending teachers and substantially smaller gains than do Teach for America teachers.

1.
NBER WORKING PAPER SERIES

RECRUITING EFFECTIVE MATH TEACHERS: HOW DO MATH IMMERSION TEACHERS COMPARE?:

EVIDENCE FROM NEW YORK CITY

Donald Boyd

Pam Grossman

Karen Hammerness

Hamilton Lankford

Susanna Loeb

Mathew Ronfeldt

James Wyckoff

Working Paper 16017

http://www.nber.org/papers/w16017

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue

Cambridge, MA 02138

May 2010

We are grateful to the New York City Department of Education and the New York State Education

Department for the data employed in this paper. We benefited from insights by Vicki Bernstein and

Mark Thames. We also thank the program directors and other administrators who provided us with

details of their preparation programs. Thanks to participants at the AEFA meetings and seminar participants

at the University of Pennsylvania for comments on an earlier draft. We appreciate financial support

from the U.S. Department of Education, IES Grant R305E06025 and the National Center for the Analysis

of Longitudinal Data in Education Research (CALDER). CALDER is supported by IES Grant R305A060018

to the Urban Institute. The views expressed in the paper are solely those of the authors and may not

reflect those of the funders. Any errors are attributable to the authors. The views expressed herein

are those of the authors and do not necessarily reflect the views of the National Bureau of Economic

Research.

© 2010 by Donald Boyd, Pam Grossman, Karen Hammerness, Hamilton Lankford, Susanna Loeb,

Mathew Ronfeldt, and James Wyckoff. All rights reserved. Short sections of text, not to exceed two

paragraphs, may be quoted without explicit permission provided that full credit, including © notice,

RECRUITING EFFECTIVE MATH TEACHERS: HOW DO MATH IMMERSION TEACHERS COMPARE?:

EVIDENCE FROM NEW YORK CITY

Donald Boyd

Pam Grossman

Karen Hammerness

Hamilton Lankford

Susanna Loeb

Mathew Ronfeldt

James Wyckoff

Working Paper 16017

http://www.nber.org/papers/w16017

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue

Cambridge, MA 02138

May 2010

We are grateful to the New York City Department of Education and the New York State Education

Department for the data employed in this paper. We benefited from insights by Vicki Bernstein and

Mark Thames. We also thank the program directors and other administrators who provided us with

details of their preparation programs. Thanks to participants at the AEFA meetings and seminar participants

at the University of Pennsylvania for comments on an earlier draft. We appreciate financial support

from the U.S. Department of Education, IES Grant R305E06025 and the National Center for the Analysis

of Longitudinal Data in Education Research (CALDER). CALDER is supported by IES Grant R305A060018

to the Urban Institute. The views expressed in the paper are solely those of the authors and may not

reflect those of the funders. Any errors are attributable to the authors. The views expressed herein

are those of the authors and do not necessarily reflect the views of the National Bureau of Economic

Research.

© 2010 by Donald Boyd, Pam Grossman, Karen Hammerness, Hamilton Lankford, Susanna Loeb,

Mathew Ronfeldt, and James Wyckoff. All rights reserved. Short sections of text, not to exceed two

paragraphs, may be quoted without explicit permission provided that full credit, including © notice,

2.
Recruiting Effective Math Teachers: How Do Math Immersion Teachers Compare?: Evidence

from New York City

Donald Boyd, Pam Grossman, Karen Hammerness, Hamilton Lankford, Susanna Loeb,

MathewRonfeldt, and James Wyckoff

NBER Working Paper No. 16017

May 2010

JEL No. I21,I28

ABSTRACT

School districts often struggle to recruit and retain effective math teachers. Alternative-route

certification programs aim to expand the pool of teachers available; however, many alternate routes

have not been able to attract large numbers of teacher candidates with undergraduate degrees in math.

In response, some districts, including Baltimore, Philadelphia, Washington D.C., and New York City,

have developed alternative programs with a math immersion component to recruit candidates who do

not have undergraduate majors in math. Such programs provide potential math teachers with intensive

math preparation to meet state certification requirements while, at the same time maintaining an

early-entry approach in which individuals who have not completed a teacher preparation program can

become qualified to teach with only five to seven weeks of coursework and practice teaching. Four

years since its inception, the New York City Teacher Fellows Math Immersion program supplies 50

percent of all new certified math teachers to New York City public schools. In this study, we find that

Math Immersion teachers have stronger academic qualifications than their College Recommending

(traditionally certified) peers, although they have weaker qualifications than Teach for America

teachers. However, despite stronger general academic qualifications Math Immersion teachers

produce somewhat smaller gains in math achievement for middle school math students than do

College Recommending teachers and substantially smaller gains than do Teach for America teachers.

Donald Boyd Karen Hammerness

The Center for Policy Research School of Education

University of Albany Stanford University

135 Western Ave. Stanford, CA 94305

Albany, NY 12222 [email protected]

Hamilton Lankford

Pam Grossman School of Education, ED 317

School of Education University at Albany

Stanford University State University of New York

Stanford, CA 94305 Albany, NY 12222

[email protected] [email protected]

(more on next page)

from New York City

Donald Boyd, Pam Grossman, Karen Hammerness, Hamilton Lankford, Susanna Loeb,

MathewRonfeldt, and James Wyckoff

NBER Working Paper No. 16017

May 2010

JEL No. I21,I28

ABSTRACT

School districts often struggle to recruit and retain effective math teachers. Alternative-route

certification programs aim to expand the pool of teachers available; however, many alternate routes

have not been able to attract large numbers of teacher candidates with undergraduate degrees in math.

In response, some districts, including Baltimore, Philadelphia, Washington D.C., and New York City,

have developed alternative programs with a math immersion component to recruit candidates who do

not have undergraduate majors in math. Such programs provide potential math teachers with intensive

math preparation to meet state certification requirements while, at the same time maintaining an

early-entry approach in which individuals who have not completed a teacher preparation program can

become qualified to teach with only five to seven weeks of coursework and practice teaching. Four

years since its inception, the New York City Teacher Fellows Math Immersion program supplies 50

percent of all new certified math teachers to New York City public schools. In this study, we find that

Math Immersion teachers have stronger academic qualifications than their College Recommending

(traditionally certified) peers, although they have weaker qualifications than Teach for America

teachers. However, despite stronger general academic qualifications Math Immersion teachers

produce somewhat smaller gains in math achievement for middle school math students than do

College Recommending teachers and substantially smaller gains than do Teach for America teachers.

Donald Boyd Karen Hammerness

The Center for Policy Research School of Education

University of Albany Stanford University

135 Western Ave. Stanford, CA 94305

Albany, NY 12222 [email protected]

Hamilton Lankford

Pam Grossman School of Education, ED 317

School of Education University at Albany

Stanford University State University of New York

Stanford, CA 94305 Albany, NY 12222

[email protected] [email protected]

(more on next page)

3.
Susanna Loeb

524 CERAS, 520 Galvez Mall

Stanford University

Stanford, CA 94305

and NBER

Mathew Ronfeldt

School of Education

Stanford University

Stanford, CA 94305

James Wyckoff

Curry School of Education

University of Virginia

P.O. Box 400277

Charlottesville, VA 22904-4277

524 CERAS, 520 Galvez Mall

Stanford University

Stanford, CA 94305

and NBER

Mathew Ronfeldt

School of Education

Stanford University

Stanford, CA 94305

James Wyckoff

Curry School of Education

University of Virginia

P.O. Box 400277

Charlottesville, VA 22904-4277

4.
I. Introduction

For well over a decade school districts across the U.S. have struggled to recruit and retain

effective math teachers. This problem appears to be more acute in schools serving high poverty student

populations (Boyd et al., 2005; Boyd et al., 2009; Hanushek et al., 2004). Historically, this has meant

that many middle and high school math teachers are teaching out of field (Ingersoll, 2003). NCLB

attempted to address this issue by requiring that all children in core academic subjects be taught by a

highly qualified teachers (HQT) beginning in 2005-06. To be highly qualified a teacher must, among

other things, have state certification and demonstrated knowledge in the subject area. States were afforded

substantial discretion in how they met the HQT requirements. Nonetheless, there is evidence that not all

teachers meet the HQT standard and that children in high poverty schools are much more likely to be

taught math by a teacher who does not meet this requirement (Peske and Haycock, 2006).

In response to the shortage of qualified math teachers, school districts have employed a variety of

strategies. Some of these strategies, including paying a one-time signing bonus or a subject-area bonus,

largely target the distribution of teachers between districts while leaving the overall pool of candidates

relatively unchanged. Other strategies, such as alternative-route certification programs, expand the pool

of teachers. For example, the New York City Teaching Fellows Program provided nearly 12,000 new

teachers to New York City schools from 2003 to 2008. However, many alternate routes, including the

Teaching Fellows, have not been able to attract large numbers of teacher candidates with undergraduate

degrees in math or science. For example, fewer than 10 percent of the math certified teachers who

entered teaching in New York City in 2007-08 through the New York City Teaching Fellows program

had an undergraduate major in mathematics. More recently, several teacher residency programs that

focus on math, such as Math for America, have been directing substantial effort to the recruitment and

preparation of highly qualified math candidates. While these programs have attracted individuals with

undergraduate degrees in Mathematics from very strong undergraduate institutions, to date we know little

about the effectiveness of the teachers from these programs compared to those from alternative

certification or tradition teacher preparation programs.

In response to the need for qualified math teachers and the difficulty of directly recruiting

individuals who have already completed the math content required for qualification, some districts,

including Baltimore, Philadelphia, Washington D.C., and New York City, have developed alternative

certification programs with a math immersion component to recruit otherwise well-qualified candidates,

who do not have undergraduate majors in math. Such programs provide candidates with intensive math

preparation to meet state certification requirements while, at the same time maintaining the early-entry

approach common in alternative pathways in which individuals who have not completed a teacher

preparation program can become a qualified teacher with only five to seven weeks of coursework and

1

For well over a decade school districts across the U.S. have struggled to recruit and retain

effective math teachers. This problem appears to be more acute in schools serving high poverty student

populations (Boyd et al., 2005; Boyd et al., 2009; Hanushek et al., 2004). Historically, this has meant

that many middle and high school math teachers are teaching out of field (Ingersoll, 2003). NCLB

attempted to address this issue by requiring that all children in core academic subjects be taught by a

highly qualified teachers (HQT) beginning in 2005-06. To be highly qualified a teacher must, among

other things, have state certification and demonstrated knowledge in the subject area. States were afforded

substantial discretion in how they met the HQT requirements. Nonetheless, there is evidence that not all

teachers meet the HQT standard and that children in high poverty schools are much more likely to be

taught math by a teacher who does not meet this requirement (Peske and Haycock, 2006).

In response to the shortage of qualified math teachers, school districts have employed a variety of

strategies. Some of these strategies, including paying a one-time signing bonus or a subject-area bonus,

largely target the distribution of teachers between districts while leaving the overall pool of candidates

relatively unchanged. Other strategies, such as alternative-route certification programs, expand the pool

of teachers. For example, the New York City Teaching Fellows Program provided nearly 12,000 new

teachers to New York City schools from 2003 to 2008. However, many alternate routes, including the

Teaching Fellows, have not been able to attract large numbers of teacher candidates with undergraduate

degrees in math or science. For example, fewer than 10 percent of the math certified teachers who

entered teaching in New York City in 2007-08 through the New York City Teaching Fellows program

had an undergraduate major in mathematics. More recently, several teacher residency programs that

focus on math, such as Math for America, have been directing substantial effort to the recruitment and

preparation of highly qualified math candidates. While these programs have attracted individuals with

undergraduate degrees in Mathematics from very strong undergraduate institutions, to date we know little

about the effectiveness of the teachers from these programs compared to those from alternative

certification or tradition teacher preparation programs.

In response to the need for qualified math teachers and the difficulty of directly recruiting

individuals who have already completed the math content required for qualification, some districts,

including Baltimore, Philadelphia, Washington D.C., and New York City, have developed alternative

certification programs with a math immersion component to recruit otherwise well-qualified candidates,

who do not have undergraduate majors in math. Such programs provide candidates with intensive math

preparation to meet state certification requirements while, at the same time maintaining the early-entry

approach common in alternative pathways in which individuals who have not completed a teacher

preparation program can become a qualified teacher with only five to seven weeks of coursework and

1

5.
practice teaching. This approach is becoming increasingly widespread but to date there is little evidence

of the effectiveness of teachers that enter through this immersion route.

The New York City Teaching Fellows program was among the first to employ a math immersion

component in the recruitment of math teachers. Prior to 2003, in the absence of sufficient numbers of

teachers who met the math major requirement, New York City employed many uncertified (temporary

license) teachers to teach math. These uncertified teachers disproportionately taught low-performing

students who frequently were from non-white and low-income families.1 As of September 2003, the New

York State Board of Regents required all districts to hire certified teachers. To address this shortage in

math and in other subjects, the New York City Department of Education created the alternative

certification pathway, the New York City Teaching Fellows (NYCTF). NYCTF was successful in

recruiting new teachers to NYC schools. For example, for the 2007-08 school year, there were 11

applicants to the Fellows program for every vacancy filled by a Fellow. However, recruiting math

teachers is often difficult. New York State requires that math teachers receive 30 semester hours of

undergraduate mathematics coursework, typically equivalent to a math major, which is not so different

from the requirements in many other states. Few college graduates meet this requirement and even fewer

of these graduates desire to enter teaching. Thus, even with the creation of the alternative certification

route, New York City finds it difficult to recruit sufficient numbers of teachers with substantial math

coursework or a math undergraduate major.

In response to the continued shortage of qualified math teachers, the district developed the Math

Immersion component of the New York City Teaching Fellows. Math Immersion began as a small pilot

in 2002-03, just as NYCTF was beginning, and, depending on the year, supplies nearly 50 percent of all

new middle and high school math teachers in New York City. Math Immersion seeks to increase the

supply of math teachers by reducing entrance requirements and providing opportunities for teaching

candidates interested in mathematics to complete the math required to be qualified, without returning to

college for an additional degree. By design, the Math Immersion program recruits individuals who did

not major in math but who demonstrate evidence of math proficiency by having a math related

undergraduate major (e.g., economics or science) or who have math related work experiences.

In this study, we examine the following research questions:

How does the background and preparation of Math Immersion teachers compare to math teachers

entering through other pathways?

For a detailed discussion of the sorting of teachers in New York see Lankford, Loeb and Wyckoff (2002).

Research in other states has demonstrated very similar patterns ((Betts, Reuben & Danenberg , 2000; Clotfelter,

Ladd, Vigdor & Wheeler, 2007; and Peske & Haycock 2006).

2

of the effectiveness of teachers that enter through this immersion route.

The New York City Teaching Fellows program was among the first to employ a math immersion

component in the recruitment of math teachers. Prior to 2003, in the absence of sufficient numbers of

teachers who met the math major requirement, New York City employed many uncertified (temporary

license) teachers to teach math. These uncertified teachers disproportionately taught low-performing

students who frequently were from non-white and low-income families.1 As of September 2003, the New

York State Board of Regents required all districts to hire certified teachers. To address this shortage in

math and in other subjects, the New York City Department of Education created the alternative

certification pathway, the New York City Teaching Fellows (NYCTF). NYCTF was successful in

recruiting new teachers to NYC schools. For example, for the 2007-08 school year, there were 11

applicants to the Fellows program for every vacancy filled by a Fellow. However, recruiting math

teachers is often difficult. New York State requires that math teachers receive 30 semester hours of

undergraduate mathematics coursework, typically equivalent to a math major, which is not so different

from the requirements in many other states. Few college graduates meet this requirement and even fewer

of these graduates desire to enter teaching. Thus, even with the creation of the alternative certification

route, New York City finds it difficult to recruit sufficient numbers of teachers with substantial math

coursework or a math undergraduate major.

In response to the continued shortage of qualified math teachers, the district developed the Math

Immersion component of the New York City Teaching Fellows. Math Immersion began as a small pilot

in 2002-03, just as NYCTF was beginning, and, depending on the year, supplies nearly 50 percent of all

new middle and high school math teachers in New York City. Math Immersion seeks to increase the

supply of math teachers by reducing entrance requirements and providing opportunities for teaching

candidates interested in mathematics to complete the math required to be qualified, without returning to

college for an additional degree. By design, the Math Immersion program recruits individuals who did

not major in math but who demonstrate evidence of math proficiency by having a math related

undergraduate major (e.g., economics or science) or who have math related work experiences.

In this study, we examine the following research questions:

How does the background and preparation of Math Immersion teachers compare to math teachers

entering through other pathways?

For a detailed discussion of the sorting of teachers in New York see Lankford, Loeb and Wyckoff (2002).

Research in other states has demonstrated very similar patterns ((Betts, Reuben & Danenberg , 2000; Clotfelter,

Ladd, Vigdor & Wheeler, 2007; and Peske & Haycock 2006).

2

6.
How do the achievement gains of the students taught by Math Immersion teachers compare to

those of students taught by math teachers entering through other pathways?

How does the retention of Math Immersion candidates compare to math teachers entering through

other pathways?

II. Background

Linking teacher preparation and pathways into teaching to student learning is a complex process.

Student outcomes are influenced directly by the teacher workforce but also by other school inputs and

external factors such as student background and environment. Because of these complexities linking

teacher preparation to student achievement is difficult to model empirically. On top of this, the teacher

workforce and each teacher’s decisions of where to teach and how to teach is influenced by many

institutional factors such as state and district policies, by teacher preparation pathways, and even by

student performance. Teacher preparation, alone, is difficult to describe and measure, as it comprises

many elements from subject-matter, to pedagogy, to child and youth development and classroom

management. In addition, quality of implementation likely is at least as important as content coverage in

With the increasing availability of rich data on students, teachers and schools in recent years,

researchers have begun to develop a range of empirical models to examine the relationship between how

teachers are prepared and the outcomes of their students. Most of these models either compare the

learning gains of students taught by teachers in the same school or compare the learning gains of the same

students taught by different teachers in different years. Recent rigorous research using these approaches

to assess the effectiveness of alternative routes to teaching shows that individuals entering teaching

through highly selective early-entry routes are either as effective in teaching math as teachers entering

through traditional preparation programs or become so within the first few years of their careers, (Decker

et al. 2004; Boyd et al. 2006; Kane et al. 2007; Harris and Sass, 2008; and Constantine et al. 2009).

However, there is wide variation in the selection and preparation requirements of both traditional

and alternative preparation programs, and comparing broad categories of pathways into teaching does

little to uncover the effects of program or pathway characteristics. In some instances the difference

between an alternative route and a traditional route can be more a matter of timing of requirements than a

difference in requirements (Boyd et al, 2008). In other cases there are dramatic differences in the

requirements that teachers must fulfill to become certified through alternative and traditional preparation

programs, (Feistritzer, 2008; Grossman and Loeb, 2008). Nearly all of the research examining the relative

effectiveness of various forms of teacher preparation has been limited to exploring relative differences in

the gains of student achievement for teachers from different programs (e.g. Boyd et al, 2006; Harris and

3

those of students taught by math teachers entering through other pathways?

How does the retention of Math Immersion candidates compare to math teachers entering through

other pathways?

II. Background

Linking teacher preparation and pathways into teaching to student learning is a complex process.

Student outcomes are influenced directly by the teacher workforce but also by other school inputs and

external factors such as student background and environment. Because of these complexities linking

teacher preparation to student achievement is difficult to model empirically. On top of this, the teacher

workforce and each teacher’s decisions of where to teach and how to teach is influenced by many

institutional factors such as state and district policies, by teacher preparation pathways, and even by

student performance. Teacher preparation, alone, is difficult to describe and measure, as it comprises

many elements from subject-matter, to pedagogy, to child and youth development and classroom

management. In addition, quality of implementation likely is at least as important as content coverage in

With the increasing availability of rich data on students, teachers and schools in recent years,

researchers have begun to develop a range of empirical models to examine the relationship between how

teachers are prepared and the outcomes of their students. Most of these models either compare the

learning gains of students taught by teachers in the same school or compare the learning gains of the same

students taught by different teachers in different years. Recent rigorous research using these approaches

to assess the effectiveness of alternative routes to teaching shows that individuals entering teaching

through highly selective early-entry routes are either as effective in teaching math as teachers entering

through traditional preparation programs or become so within the first few years of their careers, (Decker

et al. 2004; Boyd et al. 2006; Kane et al. 2007; Harris and Sass, 2008; and Constantine et al. 2009).

However, there is wide variation in the selection and preparation requirements of both traditional

and alternative preparation programs, and comparing broad categories of pathways into teaching does

little to uncover the effects of program or pathway characteristics. In some instances the difference

between an alternative route and a traditional route can be more a matter of timing of requirements than a

difference in requirements (Boyd et al, 2008). In other cases there are dramatic differences in the

requirements that teachers must fulfill to become certified through alternative and traditional preparation

programs, (Feistritzer, 2008; Grossman and Loeb, 2008). Nearly all of the research examining the relative

effectiveness of various forms of teacher preparation has been limited to exploring relative differences in

the gains of student achievement for teachers from different programs (e.g. Boyd et al, 2006; Harris and

3

7.
Sass, 2008; Decker, Mayer, & Glazerman, 2004; Raymond, Fletcher, & Lucque, 2001; Xu, Hannaway

and Taylor, 2007) without attempting to understand the many components of teacher preparation. There

are a few exceptions to this focus on program effects. Constantine et al., 2009 provide a detailed

description of differences in programs in their analysis. Boyd et al. 2009 assess the effects of preparation

program characteristics for elementary school teachers on student learning and Harris and Sass, 2007,

examine the extent to which a teacher's specific preparation coursework is associated with achievement

gains in her students.

Thus, several studies have examined the effectiveness of teachers from alternative pathways and

some have included middle school math outcomes. In addition, a few studies have examined the

relationship between preparation features and classroom achievement gains. On the other hand, to our

knowledge, no prior research has systematically examined the specific preparation and effectiveness of

math teachers, in particular, nor has it examined the effectiveness of routes into math teaching based on a

math-immersion model.

Recruiting Math Teachers. New York City hires between 6,000 and 9,000 new teachers every

year. In many years prior to the 2003-04 school year, uncertified teachers (temporary license teachers)

constituted as much as fifty percent of all new hires. The New York State Board of Regents required that

effective as of 2003-04 virtually all teachers must be certified. In anticipation of this, in 2000 the Regents

had created the opportunity for districts to hire alternatively certified teachers. In response, the New York

City Department of Education working with the New Teacher Project created the New York City

Teaching Fellows program (NYCTF) and soon thereafter the Math Immersion component of NYCTF

(NYCTF-MI). These changes dramatically altered the composition of entering teachers to New York

City Public Schools. Figure 1 shows that uncertified teachers were largely replaced by NYCTF and

NCTF-MI teachers, although there has also been meaningful increases in the number of College

Recommending teachers in recent years.

Figure 1 reflects the hiring of all teachers in New York City while, for this analysis, we are

particularly interested in math teachers. The change in pathways for math teachers was even greater than

the changes overall. Prior to 2003, NYCDOE relied heavily on uncertified teachers, because sufficient

numbers of College Recommending math teachers were unavailable. In addition, from 2003-04 through

2007-08 New York City expanded the total number of math teachers by 18 percent due to increasing

enrollments and reductions in class size.2 As a result, New York City needed to recruit between 600 and

800 new math teachers per year during this period. When other sources of supply were unavailable, New

York City turned to the Math Immersion program to meet demand. For each year starting in 2005-06

until 2009-10 that meant that approximately 20 percent of Math Immersion Fellows did not meet internal

Based on correspondence from Vicki Bernstein, New York City Department of Education, 9/14/09.

4

and Taylor, 2007) without attempting to understand the many components of teacher preparation. There

are a few exceptions to this focus on program effects. Constantine et al., 2009 provide a detailed

description of differences in programs in their analysis. Boyd et al. 2009 assess the effects of preparation

program characteristics for elementary school teachers on student learning and Harris and Sass, 2007,

examine the extent to which a teacher's specific preparation coursework is associated with achievement

gains in her students.

Thus, several studies have examined the effectiveness of teachers from alternative pathways and

some have included middle school math outcomes. In addition, a few studies have examined the

relationship between preparation features and classroom achievement gains. On the other hand, to our

knowledge, no prior research has systematically examined the specific preparation and effectiveness of

math teachers, in particular, nor has it examined the effectiveness of routes into math teaching based on a

math-immersion model.

Recruiting Math Teachers. New York City hires between 6,000 and 9,000 new teachers every

year. In many years prior to the 2003-04 school year, uncertified teachers (temporary license teachers)

constituted as much as fifty percent of all new hires. The New York State Board of Regents required that

effective as of 2003-04 virtually all teachers must be certified. In anticipation of this, in 2000 the Regents

had created the opportunity for districts to hire alternatively certified teachers. In response, the New York

City Department of Education working with the New Teacher Project created the New York City

Teaching Fellows program (NYCTF) and soon thereafter the Math Immersion component of NYCTF

(NYCTF-MI). These changes dramatically altered the composition of entering teachers to New York

City Public Schools. Figure 1 shows that uncertified teachers were largely replaced by NYCTF and

NCTF-MI teachers, although there has also been meaningful increases in the number of College

Recommending teachers in recent years.

Figure 1 reflects the hiring of all teachers in New York City while, for this analysis, we are

particularly interested in math teachers. The change in pathways for math teachers was even greater than

the changes overall. Prior to 2003, NYCDOE relied heavily on uncertified teachers, because sufficient

numbers of College Recommending math teachers were unavailable. In addition, from 2003-04 through

2007-08 New York City expanded the total number of math teachers by 18 percent due to increasing

enrollments and reductions in class size.2 As a result, New York City needed to recruit between 600 and

800 new math teachers per year during this period. When other sources of supply were unavailable, New

York City turned to the Math Immersion program to meet demand. For each year starting in 2005-06

until 2009-10 that meant that approximately 20 percent of Math Immersion Fellows did not meet internal

Based on correspondence from Vicki Bernstein, New York City Department of Education, 9/14/09.

4

8.
selection standards for the NYCTF. The problem is more acute in the recruitment of math teachers than

other teachers as only about 12 percent of non-Math Immersion Fellows in that period failed to meet

internal standards.3 Below we explore whether the need to go beyond selection standards affected student

performance. Figure 2 shows the number of new teachers who are certified in math.4 In recent years

Math Immersion has supplied nearly half of all new math teachers, far more than any other pathway into

math teaching. College Recommending programs have shown strong growth in recent years, but as of

2008 still only supplied about 30 percent of new math teachers.

New York City has come to rely heavily on Math Immersion for its new math teachers,

accentuating the importance of a better understanding the effectiveness of these teachers and this

approach to pre-service preparation. Dramatic changes in other pathways would be needed to fill the

demand for middle and high school math teachers if the Math Immersion program were eliminated. In

this analysis we compare Math Immersion to other current pathways as a means to understand their effect

on student achievement and teacher retention.

III. Data and Methods

The data for this analysis come from three distinct sources: extensive administrative data,

information about teacher preparation programs obtained from document reviews and interviews with

administrators in teacher preparation programs, and from a survey of teachers. We describe each of these

datasets in turn below.

Administrative data. We employ administrative data on students, teachers and schools drawn from

a variety of databases from the New York City Department of Education, the New York State Education

Department and the College Board. Student achievement exams are given in grades 3 through 8. All the

exams are aligned to the New York State learning standards and each set of tests is scaled to reflect item

difficulty and are equated across grades and over time.5 Tests are given to all registered students with

limited accommodations and exclusions. Thus, for nearly all students the tests provide a consistent

assessment of achievement for a student from grade three through grade eight. Since the Math Immersion

program was initiated in the 2003-04, we include data for all teachers who teach students with math

achievement outcomes from 2003-04 through 2007-08. The dependent variables in our models come from

annual student achievement exams given in grades four through eight to almost all New York City

students. The student data, provided by the New York City Department of Education (NYCDOE),

Based on correspondence from Vicki Bernstein, New York City Department of Education, 9/14/09.

For purposes of this graph a teacher is defined as having math certification if at the time she entered teaching she

held either an elementary/middle school or a secondary school math certification.

The mathematics exams in all grades are developed by CTB-McGraw Hill. New York State employs CTB-

McGraw Hill for its 4th and 8th grade ELA exams. In 2003 New York City switched from CTB to Harcourt Brace

for its 3rd, 5th-7th grade exams. At that time there was an equating study done to accommodate the switch in exams.

5

other teachers as only about 12 percent of non-Math Immersion Fellows in that period failed to meet

internal standards.3 Below we explore whether the need to go beyond selection standards affected student

performance. Figure 2 shows the number of new teachers who are certified in math.4 In recent years

Math Immersion has supplied nearly half of all new math teachers, far more than any other pathway into

math teaching. College Recommending programs have shown strong growth in recent years, but as of

2008 still only supplied about 30 percent of new math teachers.

New York City has come to rely heavily on Math Immersion for its new math teachers,

accentuating the importance of a better understanding the effectiveness of these teachers and this

approach to pre-service preparation. Dramatic changes in other pathways would be needed to fill the

demand for middle and high school math teachers if the Math Immersion program were eliminated. In

this analysis we compare Math Immersion to other current pathways as a means to understand their effect

on student achievement and teacher retention.

III. Data and Methods

The data for this analysis come from three distinct sources: extensive administrative data,

information about teacher preparation programs obtained from document reviews and interviews with

administrators in teacher preparation programs, and from a survey of teachers. We describe each of these

datasets in turn below.

Administrative data. We employ administrative data on students, teachers and schools drawn from

a variety of databases from the New York City Department of Education, the New York State Education

Department and the College Board. Student achievement exams are given in grades 3 through 8. All the

exams are aligned to the New York State learning standards and each set of tests is scaled to reflect item

difficulty and are equated across grades and over time.5 Tests are given to all registered students with

limited accommodations and exclusions. Thus, for nearly all students the tests provide a consistent

assessment of achievement for a student from grade three through grade eight. Since the Math Immersion

program was initiated in the 2003-04, we include data for all teachers who teach students with math

achievement outcomes from 2003-04 through 2007-08. The dependent variables in our models come from

annual student achievement exams given in grades four through eight to almost all New York City

students. The student data, provided by the New York City Department of Education (NYCDOE),

Based on correspondence from Vicki Bernstein, New York City Department of Education, 9/14/09.

For purposes of this graph a teacher is defined as having math certification if at the time she entered teaching she

held either an elementary/middle school or a secondary school math certification.

The mathematics exams in all grades are developed by CTB-McGraw Hill. New York State employs CTB-

McGraw Hill for its 4th and 8th grade ELA exams. In 2003 New York City switched from CTB to Harcourt Brace

for its 3rd, 5th-7th grade exams. At that time there was an equating study done to accommodate the switch in exams.

5

9.
consists of measures of gender, ethnicity, language spoken at home, free-lunch status, special-education

status, number of absences, and number of suspensions for each student who was active in any of grades

three through eight that year.

For most years, the data include scores for approximately 65,000 to 80,000 students in each

grade. Using these data, we construct a set of records with a student’s current exam score and his or her

lagged exam score. For this purpose, a student is considered to have value added information in cases

where we had a math score for the current year and a score for the same subject in the immediately

preceding year for the immediately preceding grade. All student achievement scores have been

normalized by grade and year to have a zero mean and a unit standard deviation.

To enrich our data on teachers, we match New York City teachers to data from New York State

Education Department (NYSED) databases, using a crosswalk file provided by NYCDOE that links their

teacher file reference numbers to unique identifiers employed by NYSED. We draw variables for NYC

teachers from New York State data files as follows:

Teacher Experience: For teacher experience, we use transaction-level data from the NYCDOE

Division of Human Resources to identify when individuals joined the NYCDOE payroll

system in a teaching position. When this information is missing or when the value is less than

the value in the NYSED personnel master files, we use the NYSED data.

Teacher Demographics: We draw gender, ethnicity, and age from a combined analysis of all

available data files, to choose most-common values for individuals.

Test performance: We draw information regarding the teacher certification exam scores of

individual teachers and whether they passed on their first attempts from the NYS Teacher

Certification Exam History File (EHF).

Pathway: Initial pathway into teaching comes from an analysis of teacher certification data plus

separate data files for individuals who participated in Teach for America or the New York

City Teaching Fellows Program.

College Recommending: We obtain indicators for whether an individual had completed a

college-Recommending teacher preparation program and, if so, the level of degree obtained

(bachelor’s or master’s) from NYSED’s program-completers data files.

Program Data. The information on preparation programs comes from a data collection effort in

the spring and summer of 2004 designed to characterize the preparation received by individuals entering

teaching in 2004-05 but also applicable to surrounding cohorts. We focus specifically on the 18

institutions that prepare about two-thirds of the College Recommending teachers hired in NYC schools in

recent years. Within these institutions, we concentrated on the pre-service preparation at 25 college-

6

status, number of absences, and number of suspensions for each student who was active in any of grades

three through eight that year.

For most years, the data include scores for approximately 65,000 to 80,000 students in each

grade. Using these data, we construct a set of records with a student’s current exam score and his or her

lagged exam score. For this purpose, a student is considered to have value added information in cases

where we had a math score for the current year and a score for the same subject in the immediately

preceding year for the immediately preceding grade. All student achievement scores have been

normalized by grade and year to have a zero mean and a unit standard deviation.

To enrich our data on teachers, we match New York City teachers to data from New York State

Education Department (NYSED) databases, using a crosswalk file provided by NYCDOE that links their

teacher file reference numbers to unique identifiers employed by NYSED. We draw variables for NYC

teachers from New York State data files as follows:

Teacher Experience: For teacher experience, we use transaction-level data from the NYCDOE

Division of Human Resources to identify when individuals joined the NYCDOE payroll

system in a teaching position. When this information is missing or when the value is less than

the value in the NYSED personnel master files, we use the NYSED data.

Teacher Demographics: We draw gender, ethnicity, and age from a combined analysis of all

available data files, to choose most-common values for individuals.

Test performance: We draw information regarding the teacher certification exam scores of

individual teachers and whether they passed on their first attempts from the NYS Teacher

Certification Exam History File (EHF).

Pathway: Initial pathway into teaching comes from an analysis of teacher certification data plus

separate data files for individuals who participated in Teach for America or the New York

City Teaching Fellows Program.

College Recommending: We obtain indicators for whether an individual had completed a

college-Recommending teacher preparation program and, if so, the level of degree obtained

(bachelor’s or master’s) from NYSED’s program-completers data files.

Program Data. The information on preparation programs comes from a data collection effort in

the spring and summer of 2004 designed to characterize the preparation received by individuals entering

teaching in 2004-05 but also applicable to surrounding cohorts. We focus specifically on the 18

institutions that prepare about two-thirds of the College Recommending teachers hired in NYC schools in

recent years. Within these institutions, we concentrated on the pre-service preparation at 25 college-

6

10.
recommending math certification programs, as well as the preparation provided by two large alternative

route programs: the New York City Teaching Fellows and Teach for America.

We rely on a number of data sources to document information about programs: state documents,

institutional bulletins and program descriptions, NCATE documents when available, and institutional

websites to find information about requirements and course descriptions. In documenting information

about courses, whenever possible we use the information that is closest to what is actually taught. For

example, we ask programs for the names of instructors who taught math methods for the cohorts

completing programs in 2004, and use this list rather than the list of faculty included in the state

documents. In addition, we interview program directors and directors of field experiences about the

curriculum, structure, and field experiences in their programs. We also documented the curricular

requirements in each program, focusing specifically on the number of required courses in math methods

and in math content, as well as required courses related to learning, assessment, diverse learners, and

classroom management. To further document the preparation received in mathematics, we collected

syllabi from both math content and math methods courses whenever possible. In our analyses of

preparation to teach mathematics, we looked at the overall emphasis on the teaching of mathematics, as

represented by the percentage of the curriculum that focused on math, as opposed to an emphasis on less

subject-specific preparation. Because participants in these various pathways complete their coursework at

different times, it is important to remember that students in the College Recommending programs will

have completed all of these requirements prior to teaching full-time as a teacher of record; in both TFA

and the NYC Teaching Fellows, participants complete 6-8 weeks of initial coursework prior to becoming

full-time teachers, completing the rest of the requirements during their first 2-3 years of teaching.

Surveys. In the spring of 2005 we conducted a survey of all first-year New York City teachers in

which we ask detailed questions about their preparation experiences, the mentoring they received in their

first year, and their teaching practices and goals. Our overall response rate is 71.5 percent and the

response rate fo Respondents were asked to consider the preparation they received prior to entering the

classroom—what is typically referred to as pre-service teacher education. For teachers who entered

through TFA or NYC Teaching Fellows, this referred to the 6-8 weeks of preparation, generally offered in

the summer. r each pathway is nearly or slightly above 70 percent.

The survey asked all respondents a variety of questions regarding their general teacher

preparation, mentoring and current working environment. 6 In addition, we surveyed middle and high

school math teachers specifically about several aspects of their current teaching and their preparation to

teach math. We received completed surveys from 603 respondents including 210 Teaching Fellow Math

Immersion teachers (NYCTF-MI), 130 Teaching Fellows (NYCTF), 22 Teach For America teachers

The survey can be found at www.teacherresearchpolicy.org.

7

route programs: the New York City Teaching Fellows and Teach for America.

We rely on a number of data sources to document information about programs: state documents,

institutional bulletins and program descriptions, NCATE documents when available, and institutional

websites to find information about requirements and course descriptions. In documenting information

about courses, whenever possible we use the information that is closest to what is actually taught. For

example, we ask programs for the names of instructors who taught math methods for the cohorts

completing programs in 2004, and use this list rather than the list of faculty included in the state

documents. In addition, we interview program directors and directors of field experiences about the

curriculum, structure, and field experiences in their programs. We also documented the curricular

requirements in each program, focusing specifically on the number of required courses in math methods

and in math content, as well as required courses related to learning, assessment, diverse learners, and

classroom management. To further document the preparation received in mathematics, we collected

syllabi from both math content and math methods courses whenever possible. In our analyses of

preparation to teach mathematics, we looked at the overall emphasis on the teaching of mathematics, as

represented by the percentage of the curriculum that focused on math, as opposed to an emphasis on less

subject-specific preparation. Because participants in these various pathways complete their coursework at

different times, it is important to remember that students in the College Recommending programs will

have completed all of these requirements prior to teaching full-time as a teacher of record; in both TFA

and the NYC Teaching Fellows, participants complete 6-8 weeks of initial coursework prior to becoming

full-time teachers, completing the rest of the requirements during their first 2-3 years of teaching.

Surveys. In the spring of 2005 we conducted a survey of all first-year New York City teachers in

which we ask detailed questions about their preparation experiences, the mentoring they received in their

first year, and their teaching practices and goals. Our overall response rate is 71.5 percent and the

response rate fo Respondents were asked to consider the preparation they received prior to entering the

classroom—what is typically referred to as pre-service teacher education. For teachers who entered

through TFA or NYC Teaching Fellows, this referred to the 6-8 weeks of preparation, generally offered in

the summer. r each pathway is nearly or slightly above 70 percent.

The survey asked all respondents a variety of questions regarding their general teacher

preparation, mentoring and current working environment. 6 In addition, we surveyed middle and high

school math teachers specifically about several aspects of their current teaching and their preparation to

teach math. We received completed surveys from 603 respondents including 210 Teaching Fellow Math

Immersion teachers (NYCTF-MI), 130 Teaching Fellows (NYCTF), 22 Teach For America teachers

The survey can be found at www.teacherresearchpolicy.org.

7

11.
(TFA), 129 College Recommending teachers (CR), and 112 teachers from “other” preparation routes

(“other path”).

We employ factor analysis of survey items to measure the extent to which programs emphasize

various aspects of preparation. These factors and the survey questions on which they are based are

summarized in Appendix B. For this purpose, we identify factors for opportunities to learn about

teaching math; their subject matter preparation in math, their preparation in specific teaching strategies,

their preparation for special education students, the quality of their field experience and the overall

opinion of the quality of their teacher preparation program.

Methods. In describing teacher preparation programs we employ data from our analysis of

program documents and interviews with program administrators that is summarized in tabular form. We

employ the factors constructed from the survey questions in regression analysis to examine whether

teachers prepared in certain pathways and programs identify similarities in their preparation that

differentiates it from that of other pathways. These regressions also include controls for the school

context in which teachers work and their personal characteristics.

As described above, a number of factors potentially complicate the identification of aspects of

teacher preparation that may influence the achievement of students taught by these teachers. First,

teaching candidates select their teaching pathway, preparation institution and program. This selection is

important because of the need to account for it in our assessment of program effects. Also by identifying

the features of pathways that attract individuals with the greatest potential, programs can recruit more

effective teachers. Second, different pathways into teaching can lead teachers into schools and

classrooms with different characteristics. For example, even at the pathway level there exist systematic

differences in the observable characteristics of the students they teach (see Table 1). On average the

students of Math Immersion teachers appear to be meaningfully more challenging to teach than the

students of College Recommending teachers. The students of Math Immersion teachers have math

achievement scores that average nearly 30 percent of a standard deviation lower than those of students of

College Recommending teachers. They are also more likely to be eligible for free lunch and are more

likely to be absent. By the same measures, the Math Immersion teachers have students who appear less

challenging than other New York City Teaching Fellows teachers or Teach for America teachers.

Because these differences likely influence student outcomes, our empirical models must be able to control

for them if we are to identify the effects of preparation as distinct from placement.

There are two parts to our multivariate analysis of the effects of math preparation. In the first, we

explore the effect of pathways by estimating the mean differences in value-added to student achievement

in math of teachers from different preparation pathways. We net out the effects of student, classroom and

8

(“other path”).

We employ factor analysis of survey items to measure the extent to which programs emphasize

various aspects of preparation. These factors and the survey questions on which they are based are

summarized in Appendix B. For this purpose, we identify factors for opportunities to learn about

teaching math; their subject matter preparation in math, their preparation in specific teaching strategies,

their preparation for special education students, the quality of their field experience and the overall

opinion of the quality of their teacher preparation program.

Methods. In describing teacher preparation programs we employ data from our analysis of

program documents and interviews with program administrators that is summarized in tabular form. We

employ the factors constructed from the survey questions in regression analysis to examine whether

teachers prepared in certain pathways and programs identify similarities in their preparation that

differentiates it from that of other pathways. These regressions also include controls for the school

context in which teachers work and their personal characteristics.

As described above, a number of factors potentially complicate the identification of aspects of

teacher preparation that may influence the achievement of students taught by these teachers. First,

teaching candidates select their teaching pathway, preparation institution and program. This selection is

important because of the need to account for it in our assessment of program effects. Also by identifying

the features of pathways that attract individuals with the greatest potential, programs can recruit more

effective teachers. Second, different pathways into teaching can lead teachers into schools and

classrooms with different characteristics. For example, even at the pathway level there exist systematic

differences in the observable characteristics of the students they teach (see Table 1). On average the

students of Math Immersion teachers appear to be meaningfully more challenging to teach than the

students of College Recommending teachers. The students of Math Immersion teachers have math

achievement scores that average nearly 30 percent of a standard deviation lower than those of students of

College Recommending teachers. They are also more likely to be eligible for free lunch and are more

likely to be absent. By the same measures, the Math Immersion teachers have students who appear less

challenging than other New York City Teaching Fellows teachers or Teach for America teachers.

Because these differences likely influence student outcomes, our empirical models must be able to control

for them if we are to identify the effects of preparation as distinct from placement.

There are two parts to our multivariate analysis of the effects of math preparation. In the first, we

explore the effect of pathways by estimating the mean differences in value-added to student achievement

in math of teachers from different preparation pathways. We net out the effects of student, classroom and

8

12.
school influences from the effects of preparation pathway. The model for estimating pathway effects is

based on the following equation:

Aijst = β0 + β 1Aijs(t-1) + Xitβ 2 + Cijstβ 3 + Tjstβ 4 + Πj + νs + ε ijst (1)

Here, the achievement (A) of student i in year t with teacher j in school s is a function of his or her prior

achievement, time-varying and fixed student characteristics (X), characteristics of the classroom (C),

characteristics of the teacher (T), indicator variables (fixed effect) for the preparation pathway, e.g.,

College Recommending, the teacher completed (Π), a fixed-effect for the school (ν), and a random error

term (ε). Student characteristics include race and ethnicity, gender, eligibility for free or reduced-price

lunch, whether or not the student switched schools, whether English is spoken at home, status as an

English language learner, the number of school absences in the previous year, and the number of

suspensions in the previous year. Classroom variables include the averages of all the student

characteristics, class size, grade, and the mean and standard deviation of student test scores in the prior

year. All pathway effects are estimated relative to Math Immersion.

Because the field is not settled on the appropriate specification for estimating student

achievement gains, we estimate a variety of alternative specifications. Instead of estimating current

achievement as a function of prior achievement, we employ achievement gains. For each of these models

we substitute student fixed effects for school fixed effects. All models cluster errors at the teacher level.

Whether or not to include teacher characteristics depends upon the question at hand. If we want

to know whether teachers from Math Immersion are more effective than teachers from another pathway

then there is no reason to include fixed teacher characteristics, such as SAT or certification exam scores.

In fact, the benefit of one pathway may come from its ability to recruit and select high quality candidates.

However, if we want to separate the selection from the preparation aspects of programs, then it is

important to control for teachers’ initial characteristics. These controls are particularly important for the

parts of our analysis that look at the effects of program characteristics on preparation, as opposed to

programs overall. The teacher characteristics that we include are age, gender, race and ethnicity, whether

they passed their general knowledge certification exam on the first attempt, SAT scores and a series of

indicator variables summarizing the ranking of their under graduate college. We estimate a variety of

alternative specifications for Equation 1, including: using gains scores as the dependent variable while

omitting lag scores as independent variables, employing student fixed effects rather than school fixed

effects and by limiting the sample to only individuals who begin teaching in New York City in 2004 or

In addition to exploring the average effects of pathways, we are interested in a series of related

questions. How does the effect of pathways differ based on teaching experience—that is do the students

of novice teachers in Math Immersion experience different achievement gains from the students of novice

9

based on the following equation:

Aijst = β0 + β 1Aijs(t-1) + Xitβ 2 + Cijstβ 3 + Tjstβ 4 + Πj + νs + ε ijst (1)

Here, the achievement (A) of student i in year t with teacher j in school s is a function of his or her prior

achievement, time-varying and fixed student characteristics (X), characteristics of the classroom (C),

characteristics of the teacher (T), indicator variables (fixed effect) for the preparation pathway, e.g.,

College Recommending, the teacher completed (Π), a fixed-effect for the school (ν), and a random error

term (ε). Student characteristics include race and ethnicity, gender, eligibility for free or reduced-price

lunch, whether or not the student switched schools, whether English is spoken at home, status as an

English language learner, the number of school absences in the previous year, and the number of

suspensions in the previous year. Classroom variables include the averages of all the student

characteristics, class size, grade, and the mean and standard deviation of student test scores in the prior

year. All pathway effects are estimated relative to Math Immersion.

Because the field is not settled on the appropriate specification for estimating student

achievement gains, we estimate a variety of alternative specifications. Instead of estimating current

achievement as a function of prior achievement, we employ achievement gains. For each of these models

we substitute student fixed effects for school fixed effects. All models cluster errors at the teacher level.

Whether or not to include teacher characteristics depends upon the question at hand. If we want

to know whether teachers from Math Immersion are more effective than teachers from another pathway

then there is no reason to include fixed teacher characteristics, such as SAT or certification exam scores.

In fact, the benefit of one pathway may come from its ability to recruit and select high quality candidates.

However, if we want to separate the selection from the preparation aspects of programs, then it is

important to control for teachers’ initial characteristics. These controls are particularly important for the

parts of our analysis that look at the effects of program characteristics on preparation, as opposed to

programs overall. The teacher characteristics that we include are age, gender, race and ethnicity, whether

they passed their general knowledge certification exam on the first attempt, SAT scores and a series of

indicator variables summarizing the ranking of their under graduate college. We estimate a variety of

alternative specifications for Equation 1, including: using gains scores as the dependent variable while

omitting lag scores as independent variables, employing student fixed effects rather than school fixed

effects and by limiting the sample to only individuals who begin teaching in New York City in 2004 or

In addition to exploring the average effects of pathways, we are interested in a series of related

questions. How does the effect of pathways differ based on teaching experience—that is do the students

of novice teachers in Math Immersion experience different achievement gains from the students of novice

9

13.
teachers in other pathways and how do these patterns change as teachers become more experienced? To

examine this question we interact pathways with teaching experience for each of the first four years of

IV. RESULTS

In this section we address each of the three research questions in turn.

Question 1: How does the background and preparation of Math Immersion teachers compare to

math teachers entering through other pathways?

Attributes of Math Teachers: There are meaningful differences between the attributes of math

immersion teachers and teachers who enter through pathways other than NYCTF, particularly the College

Recommending pathway. As shown in Table 2, Math Immersion teachers, both those teaching in high

school and middle school, are a more diverse group of teachers than their College Recommending

peers—they are substantially more likely to be male, Black and Hispanic. They also tend to perform

better on most measures of academic ability, including the math and verbal SAT exams, the Liberal Arts

and Sciences Test (LAST), New York’s general knowledge certification exam, and the math/science sub-

score of the LAST, although they perform slightly worse on the Content Specialty Test in Math (CST

Math) and the secondary pedagogy exam (ATS Secondary). Not surprisingly Math Immersion teachers

are fairly similar to other NYCTF teachers but perform less well on all measures of academic ability than

TFA math certified teachers.

Many of the Math Immersion teachers who become math certified either have a math related

undergraduate major (49 percent) or math related work experience (19 percent).7 Although it appears

that a substantial percentage of Math Immersion teachers do not have math related majors or work

experiences, we do not have information on college course work which is another way candidates may

have met the Math Immersion eligibility criteria. As shown in Table 3, among Math Immersion teachers

there are some differences between those with math related backgrounds and those without such

backgrounds. On many measures, however, Math Immersion teachers who do not have math related

backgrounds have qualifications that are at least as strong, and sometimes even stronger, when compared

to those with math related backgrounds.

NYCTF math teachers and the subcomponent of Math Immersion teachers are prepared at several

different institutions. Table 4 shows that four campuses are responsible for the vast majority of these

teachers. There are many similarities, but some interesting differences across the attributes of math

We obtain information about undergraduate major and work experiences based on a program information obtained

from the New York City Teaching Fellows.

10

examine this question we interact pathways with teaching experience for each of the first four years of

IV. RESULTS

In this section we address each of the three research questions in turn.

Question 1: How does the background and preparation of Math Immersion teachers compare to

math teachers entering through other pathways?

Attributes of Math Teachers: There are meaningful differences between the attributes of math

immersion teachers and teachers who enter through pathways other than NYCTF, particularly the College

Recommending pathway. As shown in Table 2, Math Immersion teachers, both those teaching in high

school and middle school, are a more diverse group of teachers than their College Recommending

peers—they are substantially more likely to be male, Black and Hispanic. They also tend to perform

better on most measures of academic ability, including the math and verbal SAT exams, the Liberal Arts

and Sciences Test (LAST), New York’s general knowledge certification exam, and the math/science sub-

score of the LAST, although they perform slightly worse on the Content Specialty Test in Math (CST

Math) and the secondary pedagogy exam (ATS Secondary). Not surprisingly Math Immersion teachers

are fairly similar to other NYCTF teachers but perform less well on all measures of academic ability than

TFA math certified teachers.

Many of the Math Immersion teachers who become math certified either have a math related

undergraduate major (49 percent) or math related work experience (19 percent).7 Although it appears

that a substantial percentage of Math Immersion teachers do not have math related majors or work

experiences, we do not have information on college course work which is another way candidates may

have met the Math Immersion eligibility criteria. As shown in Table 3, among Math Immersion teachers

there are some differences between those with math related backgrounds and those without such

backgrounds. On many measures, however, Math Immersion teachers who do not have math related

backgrounds have qualifications that are at least as strong, and sometimes even stronger, when compared

to those with math related backgrounds.

NYCTF math teachers and the subcomponent of Math Immersion teachers are prepared at several

different institutions. Table 4 shows that four campuses are responsible for the vast majority of these

teachers. There are many similarities, but some interesting differences across the attributes of math

We obtain information about undergraduate major and work experiences based on a program information obtained

from the New York City Teaching Fellows.

10

14.
certified teachers prepared at these campuses. 8 Table 5 shows that many of the demographic

characteristics are very similar across campuses, though Campus C’s teachers tend to be somewhat older

and are more likely to be male, while Campus A’s teachers are more likely to be Black. There is

remarkable consistency across many of the measures of ability, with the exception that Campus C’s

relatively small Math Immersion program has teachers who outperform several other campuses on the

pedagogy exam. On the SAT math and verbal tests, Math Immersion teachers at Campus Z perform

better, while those at Campus A appear to perform worse than the other campuses.

Among the College Recommending programs, a similarly small number of campuses account for

most of the math certified teachers. Three institutions R, S, and T account for 40 percent of all the math

certified teachers produced by College Recommending programs hired by New York City schools over

the five years 2004-08. Each year, most programs produce only a handful of math certified teachers who

are hired in New York City.

Differences in Preparation Between Math Immersion and College Recommending Pathways: Our

reviews of program requirements across 25 College Recommending and 5 Math Immersion programs

suggest that there is relatively little variation between pathways but substantial variation within each

pathway with regard to required coursework. Table 6 shows the average number of courses and course

credits required across several key components of pathways, where we have separated the graduate and

undergraduate College Recommending programs. As these results show, the average Math Immersion

program requires roughly as many or more courses and credit hour in most components of the programs,

including math content and math methods, as either the average graduate or undergraduate College

Recommending program. There are two exceptions. The undergraduate College Recommending

programs require more classroom management and learning about learners than do Math Immersion

programs (1.75 credit hours v. 0.6 credit hours for classroom management and 4.5 v. 2.4 credit hours for

learning about learners). College Recommending graduate programs are between the other groups on

These findings are often, but not always, supported by our survey of teachers regarding their

perceptions of the preparation they received in their programs. Table 7 presents the results of regression

analyses where factors created from teachers responses to survey questions regarding their perceptions of

the opportunities they had to engage in various preparation activities during pre-service education are

regressed on preparation pathways where all pathways are relative to the Math Immersion pathway as

well as school context factors. As shown, teachers from College Recommending programs cite

significantly greater general opportunities to learn about the teaching of math, preparation in specific

Pseudonyms are provided for the campuses in order to protect the confidentiality of the institutions and

participating faculty.

11

characteristics are very similar across campuses, though Campus C’s teachers tend to be somewhat older

and are more likely to be male, while Campus A’s teachers are more likely to be Black. There is

remarkable consistency across many of the measures of ability, with the exception that Campus C’s

relatively small Math Immersion program has teachers who outperform several other campuses on the

pedagogy exam. On the SAT math and verbal tests, Math Immersion teachers at Campus Z perform

better, while those at Campus A appear to perform worse than the other campuses.

Among the College Recommending programs, a similarly small number of campuses account for

most of the math certified teachers. Three institutions R, S, and T account for 40 percent of all the math

certified teachers produced by College Recommending programs hired by New York City schools over

the five years 2004-08. Each year, most programs produce only a handful of math certified teachers who

are hired in New York City.

Differences in Preparation Between Math Immersion and College Recommending Pathways: Our

reviews of program requirements across 25 College Recommending and 5 Math Immersion programs

suggest that there is relatively little variation between pathways but substantial variation within each

pathway with regard to required coursework. Table 6 shows the average number of courses and course

credits required across several key components of pathways, where we have separated the graduate and

undergraduate College Recommending programs. As these results show, the average Math Immersion

program requires roughly as many or more courses and credit hour in most components of the programs,

including math content and math methods, as either the average graduate or undergraduate College

Recommending program. There are two exceptions. The undergraduate College Recommending

programs require more classroom management and learning about learners than do Math Immersion

programs (1.75 credit hours v. 0.6 credit hours for classroom management and 4.5 v. 2.4 credit hours for

learning about learners). College Recommending graduate programs are between the other groups on

These findings are often, but not always, supported by our survey of teachers regarding their

perceptions of the preparation they received in their programs. Table 7 presents the results of regression

analyses where factors created from teachers responses to survey questions regarding their perceptions of

the opportunities they had to engage in various preparation activities during pre-service education are

regressed on preparation pathways where all pathways are relative to the Math Immersion pathway as

well as school context factors. As shown, teachers from College Recommending programs cite

significantly greater general opportunities to learn about the teaching of math, preparation in specific

Pseudonyms are provided for the campuses in order to protect the confidentiality of the institutions and

participating faculty.

11

15.
teaching strategies, greater quality of field experiences and more opportunities to learn preparation for

working with special education students. There is no difference in perceptions of opportunities to learn

math content between College Recommending teachers and Math Immersion teachers. It is also the case

that Teach for America teachers report more opportunities to learn in specific strategies and better field

experiences but less opportunity for math subject matter preparation, as was the case with regular

Teaching Fellows. Regular Teaching Fellows also report fewer opportunities to learn teaching of math

but more opportunities in the preparation of specific strategies. Again, it is important to remember that the

survey asked specifically about opportunities to learn prior to entering the classroom as a full-time

teacher; teachers in both TFA and NYC Teaching Fellows, including Math Immersion, were still taking

courses to fulfill program requirements.

Although we find only modest differences in the average program requirements between Math

Immersion and College Recommending programs, we do observe much greater differences among

programs within each pathway.

Variation Within Preparation Pathways: While Math Immersion is in some senses a single

program, the preparation experiences of NYCTF-MI teachers can be quite different depending on which

institution they attend. College Recommending programs also establish differing program requirements

within the broader requirements established by New York State. To understand the preparation in each

program, we accessed program documents and accreditation materials as well interviewed program

directors and field coordinators.

A Math Immersion Teaching Fellow could be prepared in mathematics and general pedagogy in

very different ways, depending upon the campus at which he or she was prepared. As Table 8 suggests,

the programs vary in terms of their course requirements.9 There are three telling aspects of this analysis.

First, there are remarkable differences across campuses in their math content and math methods

requirements, ranging from one 3-credit course in math content required by at Campus Z to 5 or more

courses required by Campuses A, B and C. The range in requirements for math methods was smaller. In

sum, Math Immersion Fellows could receive different emphasis on math content or math methods

depending on the campus they attend. Second, there is a range of requirements in general pedagogy10

across these programs. As seen in Table 8, only two of the five campuses required courses on assessment,

and, despite the continued emphasis upon and discussions about the role technology should play in

Our categorization of the courses (whether they are considered subject matter content courses or methods; whether

they are general pedagogy courses, or courses about learners) is based upon and consistent with an earlier analysis

we conducted on childhood education programs at many of these same institutions.

“General pedagogy” in our analysis refers to any courses that were not specific to the teaching of a content area,

but rather had to do with general issues of teaching—such as coursework in technology, assessment;

interdisciplinary or general methods courses that did not focus upon a particular discipline; courses in literacy across

the content areas.

12

working with special education students. There is no difference in perceptions of opportunities to learn

math content between College Recommending teachers and Math Immersion teachers. It is also the case

that Teach for America teachers report more opportunities to learn in specific strategies and better field

experiences but less opportunity for math subject matter preparation, as was the case with regular

Teaching Fellows. Regular Teaching Fellows also report fewer opportunities to learn teaching of math

but more opportunities in the preparation of specific strategies. Again, it is important to remember that the

survey asked specifically about opportunities to learn prior to entering the classroom as a full-time

teacher; teachers in both TFA and NYC Teaching Fellows, including Math Immersion, were still taking

courses to fulfill program requirements.

Although we find only modest differences in the average program requirements between Math

Immersion and College Recommending programs, we do observe much greater differences among

programs within each pathway.

Variation Within Preparation Pathways: While Math Immersion is in some senses a single

program, the preparation experiences of NYCTF-MI teachers can be quite different depending on which

institution they attend. College Recommending programs also establish differing program requirements

within the broader requirements established by New York State. To understand the preparation in each

program, we accessed program documents and accreditation materials as well interviewed program

directors and field coordinators.

A Math Immersion Teaching Fellow could be prepared in mathematics and general pedagogy in

very different ways, depending upon the campus at which he or she was prepared. As Table 8 suggests,

the programs vary in terms of their course requirements.9 There are three telling aspects of this analysis.

First, there are remarkable differences across campuses in their math content and math methods

requirements, ranging from one 3-credit course in math content required by at Campus Z to 5 or more

courses required by Campuses A, B and C. The range in requirements for math methods was smaller. In

sum, Math Immersion Fellows could receive different emphasis on math content or math methods

depending on the campus they attend. Second, there is a range of requirements in general pedagogy10

across these programs. As seen in Table 8, only two of the five campuses required courses on assessment,

and, despite the continued emphasis upon and discussions about the role technology should play in

Our categorization of the courses (whether they are considered subject matter content courses or methods; whether

they are general pedagogy courses, or courses about learners) is based upon and consistent with an earlier analysis

we conducted on childhood education programs at many of these same institutions.

“General pedagogy” in our analysis refers to any courses that were not specific to the teaching of a content area,

but rather had to do with general issues of teaching—such as coursework in technology, assessment;

interdisciplinary or general methods courses that did not focus upon a particular discipline; courses in literacy across

the content areas.

12

16.
teacher education programs, only one campus required coursework in technology. Finally, of the five

Fellows campuses, four programs required at least one course in learning or child development.11

However, again, as with the preparation in other areas reported thus far, the requirements in learning

range substantially. Variation across the other components of preparation programs was not meaningful.

In sum, the most striking variation across programs lies with whether programs put greater

emphasis on math content and methods, or more emphasis on more general preparation for teaching that

was not specific to teaching mathematics topics, courses or issues. For instance, two of the Math

Immersion fellows programs are structured around heavier requirements in general courses on pedagogy

and learners and learning (Campus Z and Campus D), and require fewer courses in math and math

methods. Campus Z has particularly weak requirements in Math content. Campus Z program requires 3

credits in mathematics content, and 6 credits in methods; these requirements represent 9 of the total of 39

credits, or 23 percent of the total required. On the other hand, at Campus C, math methods and math

content credits represent 30 of the required 47 credits, for 63 percent of the total requirements. Two

campuses stand out for their curricular emphasis on math content and math methods in their course

requirements: Campus C and Campus A.

We also examined program documents and interview program administrators of College

Recommending programs in mathematics who supply the majority of math teachers from College

Recommending programs for New York City public schools (See Table 9). The programs we reviewed

included a total of 25 programs at 16 campuses, 14 of these programs were graduate programs, 11 were

undergraduate programs. Of the 16 institutions, 10 are private and 6 are public. All of the institutions

that offered NYCTF Math Immersion programs also offered College Recommending programs in

We find a substantial range in requirements in mathematics content. For graduate programs in

the teaching of mathematics, requirements ranged from no courses required in math content, to five

courses in math content (See Table 9). In part, these lower requirements in math content may be due to

the fact that a number of the graduate programs required math preparation prior to entry—in many of

these programs, incoming applicants were required to have been math majors, although there is

substantial variation among undergraduate programs in math content, too. In terms of math methods

courses, we find a similar range with regard to requirements; almost half of the programs required just

one mathematics methods course and four programs required either three or four courses. In sum, the

range of requirements in math methods appears to be somewhat similar to the range seen in the Math

In this category, consistent with prior analysis, we included courses that focus upon learners and learning; courses

on child development; courses on classroom management; courses on diverse learners or diverse language learners;

and courses on children with special needs.

13

Fellows campuses, four programs required at least one course in learning or child development.11

However, again, as with the preparation in other areas reported thus far, the requirements in learning

range substantially. Variation across the other components of preparation programs was not meaningful.

In sum, the most striking variation across programs lies with whether programs put greater

emphasis on math content and methods, or more emphasis on more general preparation for teaching that

was not specific to teaching mathematics topics, courses or issues. For instance, two of the Math

Immersion fellows programs are structured around heavier requirements in general courses on pedagogy

and learners and learning (Campus Z and Campus D), and require fewer courses in math and math

methods. Campus Z has particularly weak requirements in Math content. Campus Z program requires 3

credits in mathematics content, and 6 credits in methods; these requirements represent 9 of the total of 39

credits, or 23 percent of the total required. On the other hand, at Campus C, math methods and math

content credits represent 30 of the required 47 credits, for 63 percent of the total requirements. Two

campuses stand out for their curricular emphasis on math content and math methods in their course

requirements: Campus C and Campus A.

We also examined program documents and interview program administrators of College

Recommending programs in mathematics who supply the majority of math teachers from College

Recommending programs for New York City public schools (See Table 9). The programs we reviewed

included a total of 25 programs at 16 campuses, 14 of these programs were graduate programs, 11 were

undergraduate programs. Of the 16 institutions, 10 are private and 6 are public. All of the institutions

that offered NYCTF Math Immersion programs also offered College Recommending programs in

We find a substantial range in requirements in mathematics content. For graduate programs in

the teaching of mathematics, requirements ranged from no courses required in math content, to five

courses in math content (See Table 9). In part, these lower requirements in math content may be due to

the fact that a number of the graduate programs required math preparation prior to entry—in many of

these programs, incoming applicants were required to have been math majors, although there is

substantial variation among undergraduate programs in math content, too. In terms of math methods

courses, we find a similar range with regard to requirements; almost half of the programs required just

one mathematics methods course and four programs required either three or four courses. In sum, the

range of requirements in math methods appears to be somewhat similar to the range seen in the Math

In this category, consistent with prior analysis, we included courses that focus upon learners and learning; courses

on child development; courses on classroom management; courses on diverse learners or diverse language learners;

and courses on children with special needs.

13

17.
Immersion programs. The variation in requirements for preparation in learning and learners and that in

classroom management in College Recommending programs also is similar to that in Math Immersion.

As summarized in by the standard deviations of required courses and credit hours for Math Immersion

and College Recommending programs (Table 6), the variation of within pathway course requirements

substantially exceeds the variation between pathways. This is perhaps not surprising in that New York’s

alternative preparation pathways are best characterized as allowing for differences in the timing of

meeting requirements rather than allowing for different requirements.

In light of our program analysis which reveals that one program, Campus Z, stands out as having

the fewest requirements in math-related preparation to teach, we examine the results of the survey

comparing the responses of students from campus Z to students from the other Math Immersion

campuses. To explore differences among Math Immersion programs across our measures of teacher

preparation, we estimate models including indicator variables for each campus within the Math

Immersion pathway where the comparison group is teachers prepared at Program Z. Because a teachers’

perspective on her preparation may be influenced by the context in which she is teaching at the time she

completes the questionnaire, we also estimate models that include school context factors as controls.

As compared to teachers from Campus Z, Table 10 shows that teachers from other campuses

score higher across survey factors measuring preparation program attributes. Though the coefficients are

only sometimes statistically significant, they are consistently positive. When we group together all other

campuses and compare them to Campus Z (bottom row), teachers from all other campuses report having

significantly more opportunities to learn teaching math and more preparation to use specific teaching

practices, however there are no differences in their perceptions of opportunities to learn math. These

results are consistent with many, but not all, of the findings from our program review. Additionally,

teachers from other campuses report higher quality field experiences.

Based on our review of the structure and content in Math Immersion and College Recommending

preparation programs in mathematics and based on teacher reports of their preparation, there appears to be

substantial variation within and across pathways. We now explore whether different pathways influence

gains in student achievement outcomes.

Question 2: How do the achievement gains of the students taught by Math Immersion teachers

compare to those of students taught by math teachers entering through other pathways?

Are teachers entering teaching in New York City through the Math Immersion program more or

less effective than math teachers from other pathways? Based on their preparation and their background

there are reasons to believe that NYCTF-MI teachers may have different effects on students than do other

teachers. By definition, Math Immersion teachers do not have an undergraduate major in their subject

14

classroom management in College Recommending programs also is similar to that in Math Immersion.

As summarized in by the standard deviations of required courses and credit hours for Math Immersion

and College Recommending programs (Table 6), the variation of within pathway course requirements

substantially exceeds the variation between pathways. This is perhaps not surprising in that New York’s

alternative preparation pathways are best characterized as allowing for differences in the timing of

meeting requirements rather than allowing for different requirements.

In light of our program analysis which reveals that one program, Campus Z, stands out as having

the fewest requirements in math-related preparation to teach, we examine the results of the survey

comparing the responses of students from campus Z to students from the other Math Immersion

campuses. To explore differences among Math Immersion programs across our measures of teacher

preparation, we estimate models including indicator variables for each campus within the Math

Immersion pathway where the comparison group is teachers prepared at Program Z. Because a teachers’

perspective on her preparation may be influenced by the context in which she is teaching at the time she

completes the questionnaire, we also estimate models that include school context factors as controls.

As compared to teachers from Campus Z, Table 10 shows that teachers from other campuses

score higher across survey factors measuring preparation program attributes. Though the coefficients are

only sometimes statistically significant, they are consistently positive. When we group together all other

campuses and compare them to Campus Z (bottom row), teachers from all other campuses report having

significantly more opportunities to learn teaching math and more preparation to use specific teaching

practices, however there are no differences in their perceptions of opportunities to learn math. These

results are consistent with many, but not all, of the findings from our program review. Additionally,

teachers from other campuses report higher quality field experiences.

Based on our review of the structure and content in Math Immersion and College Recommending

preparation programs in mathematics and based on teacher reports of their preparation, there appears to be

substantial variation within and across pathways. We now explore whether different pathways influence

gains in student achievement outcomes.

Question 2: How do the achievement gains of the students taught by Math Immersion teachers

compare to those of students taught by math teachers entering through other pathways?

Are teachers entering teaching in New York City through the Math Immersion program more or

less effective than math teachers from other pathways? Based on their preparation and their background

there are reasons to believe that NYCTF-MI teachers may have different effects on students than do other

teachers. By definition, Math Immersion teachers do not have an undergraduate major in their subject

14

18.
area, which is commonly required for teachers entering through the College Recommending pathway.

However, Math Immersion teachers also tend to have stronger academic credentials than teachers from

other pathways with the exception of those entering through Teach for America. To explore the relative

effectiveness of Math Immersion teachers in improving student achievement outcomes, we estimate

several value-added models for students taking standardized math achievement exams in grades 6-8.

We should note that to more fully examine math achievement we would like to have value added

measures for high school mathematics but such data do not currently in exist in New York City, or most

other districts. This does raise a potentially important methodological issue of the placement of math

teachers between middle school and high school. There is anecdotal evidence that many math teachers

prefer to teach in high school and that many preparation programs steer their strongest students toward

teaching positions in high schools, where content knowledge may be even more important. To assess

whether there is any evidence of this and more importantly if such placements differentially affect some

pathways or programs (a sample selection issue), we examine the qualifications of high school and middle

school math certified teachers by pathway in Table 2 and by program in Table 5.

As shown in Table 2, the qualifications of math certified teachers over the 2004-08 period is

generally stronger for teachers in high school than those in middle schools across each pathway. For

example, the College Recommending teachers in high school have SAT math scores that are 7.9 percent

higher than College Recommending teachers in middle school, while comparable differences for Math

Immersion and TFA are 4.6 percent and 9.6 percent respectively. The differences for the Content

Specialty test are 4.4 percent for College Recommending, 2.4 percent greater for Math Immersion and no

difference for TFA. To the extent that these measures of qualifications have some predictive ability of a

teacher's value added, then we would expect high school teachers from each pathway to more effective.

However, these differences do not suggest that one pathway is being systematically affected by teacher

sorting to high school. Similar comparisons can be made among the Math Immersion programs. As

shown in Table 5, each of the Math Immersion programs places teachers with somewhat stronger

qualifications in high school relative to the teachers from their pathway who teach in middle school.12

These differences vary but across every measure Program Z has the smallest difference between middle

and high school teacher, suggesting the Program Z's middle school teachers may be relatively more

effective compared other pathways than its high school teachers.

In general we find that most of the independent variables characterizing individual students, the

class of the student, and the experience of teachers produce math achievement gains in grades 6 through 8

It is also the case that Math Immersion teachers who do not meet internal acceptance standards but who were

admitted due to excess demand are somewhat more likely to teach in middle school than high school compared to

their colleagues who met internal recruitment standards (58 v. 52 percent). (Correspondence with Vicki Bernstein,

New York City Department of Education, 9/14/09.)

15

However, Math Immersion teachers also tend to have stronger academic credentials than teachers from

other pathways with the exception of those entering through Teach for America. To explore the relative

effectiveness of Math Immersion teachers in improving student achievement outcomes, we estimate

several value-added models for students taking standardized math achievement exams in grades 6-8.

We should note that to more fully examine math achievement we would like to have value added

measures for high school mathematics but such data do not currently in exist in New York City, or most

other districts. This does raise a potentially important methodological issue of the placement of math

teachers between middle school and high school. There is anecdotal evidence that many math teachers

prefer to teach in high school and that many preparation programs steer their strongest students toward

teaching positions in high schools, where content knowledge may be even more important. To assess

whether there is any evidence of this and more importantly if such placements differentially affect some

pathways or programs (a sample selection issue), we examine the qualifications of high school and middle

school math certified teachers by pathway in Table 2 and by program in Table 5.

As shown in Table 2, the qualifications of math certified teachers over the 2004-08 period is

generally stronger for teachers in high school than those in middle schools across each pathway. For

example, the College Recommending teachers in high school have SAT math scores that are 7.9 percent

higher than College Recommending teachers in middle school, while comparable differences for Math

Immersion and TFA are 4.6 percent and 9.6 percent respectively. The differences for the Content

Specialty test are 4.4 percent for College Recommending, 2.4 percent greater for Math Immersion and no

difference for TFA. To the extent that these measures of qualifications have some predictive ability of a

teacher's value added, then we would expect high school teachers from each pathway to more effective.

However, these differences do not suggest that one pathway is being systematically affected by teacher

sorting to high school. Similar comparisons can be made among the Math Immersion programs. As

shown in Table 5, each of the Math Immersion programs places teachers with somewhat stronger

qualifications in high school relative to the teachers from their pathway who teach in middle school.12

These differences vary but across every measure Program Z has the smallest difference between middle

and high school teacher, suggesting the Program Z's middle school teachers may be relatively more

effective compared other pathways than its high school teachers.

In general we find that most of the independent variables characterizing individual students, the

class of the student, and the experience of teachers produce math achievement gains in grades 6 through 8

It is also the case that Math Immersion teachers who do not meet internal acceptance standards but who were

admitted due to excess demand are somewhat more likely to teach in middle school than high school compared to

their colleagues who met internal recruitment standards (58 v. 52 percent). (Correspondence with Vicki Bernstein,

New York City Department of Education, 9/14/09.)

15

19.
as suggested by theory and found in most other research employing administrative data (see Table 11).

All of the student attributes affect achievement. For example, prior achievement is an important predictor

of current achievement, Asian students outperform whites, while Black and Hispanic students have lower

achievement than whites. Students who have changed schools perform substantially more poorly than

those who are not mobile, as do students with more absences and suspensions, other things equal. The

attributes of class peers also influences student achievement in the expected ways. As has been found in

several previous studies, increasing experience as a teacher improves student math achievement for the

first four or five years, with additional experience having no meaningful effect on achievement. This

effect includes both changes in an individual teacher’s ability to improve achievement and the changing

composition of the workforce. If teachers who are less effective are disproportionately more likely to

leave middle school math classrooms then at least some of the gains to experience may reflect this

The focus of this research is the effect of the pathway through which a teacher enters teaching,

and in particular the relative effect of math immersion, the omitted pathway in the estimates found in

Table 11. These estimates suggest that on average, students of Math Immersion teachers in grades 6-8

have smaller gains in math achievement than students of teachers from the College Recommending,

Teaching Fellows, and TFA pathways. Coefficients reflect effect sizes. In gauging effect size

magnitudes it is useful to compare coefficient estimates to the effect of student gains produced by the first

year of teaching experience, which most observers regard as important to student achievement. In this

context, the effect of having a Teach for America teacher relative to a Math Immersion teacher is roughly

the same as the first year of teaching experience (about 0.05). The additional achievement of students of

College Recommending (0.016) and regular Teaching Fellows (0.021) relative to Math Immersion

teachers is estimated to be about 40 percent as large as the first year of teaching experience, and in models

with school fixed effects these estimates are statistically significant at the 10 percent level.

Although there are significant differences between the mean effects of some of the pathways,

there is also substantial overlap of the distribution of teacher value added. Figure 3 shows the distribution

of the teacher fixed effects by pathway.13 The distribution of TFA teachers is generally shifted to the

right, but they also have a meaningful number of relatively more effective teachers as indicated by the

The figure plots the persistent component of a teacher’s effectiveness by employing an empirical Bayes estimator

similar to that suggested in Kane, Rockoff and Staiger (2008). The estimate of teacher effectiveness results from a

regression of student math achievement identical to equation 1 with teacher experience as the only measure of

teacher attributes. The residuals from this regression are shrunken to adjust for the measurement error associated

with the estimates. We should note that while the estimates of effectiveness for each individual teacher are

unbiased, the estimates by pathway taken together to form the distribution of teacher effectiveness over adjusts the

overall distribution of teacher effects. Even so, there is substantial overlap among the pathways.

16

All of the student attributes affect achievement. For example, prior achievement is an important predictor

of current achievement, Asian students outperform whites, while Black and Hispanic students have lower

achievement than whites. Students who have changed schools perform substantially more poorly than

those who are not mobile, as do students with more absences and suspensions, other things equal. The

attributes of class peers also influences student achievement in the expected ways. As has been found in

several previous studies, increasing experience as a teacher improves student math achievement for the

first four or five years, with additional experience having no meaningful effect on achievement. This

effect includes both changes in an individual teacher’s ability to improve achievement and the changing

composition of the workforce. If teachers who are less effective are disproportionately more likely to

leave middle school math classrooms then at least some of the gains to experience may reflect this

The focus of this research is the effect of the pathway through which a teacher enters teaching,

and in particular the relative effect of math immersion, the omitted pathway in the estimates found in

Table 11. These estimates suggest that on average, students of Math Immersion teachers in grades 6-8

have smaller gains in math achievement than students of teachers from the College Recommending,

Teaching Fellows, and TFA pathways. Coefficients reflect effect sizes. In gauging effect size

magnitudes it is useful to compare coefficient estimates to the effect of student gains produced by the first

year of teaching experience, which most observers regard as important to student achievement. In this

context, the effect of having a Teach for America teacher relative to a Math Immersion teacher is roughly

the same as the first year of teaching experience (about 0.05). The additional achievement of students of

College Recommending (0.016) and regular Teaching Fellows (0.021) relative to Math Immersion

teachers is estimated to be about 40 percent as large as the first year of teaching experience, and in models

with school fixed effects these estimates are statistically significant at the 10 percent level.

Although there are significant differences between the mean effects of some of the pathways,

there is also substantial overlap of the distribution of teacher value added. Figure 3 shows the distribution

of the teacher fixed effects by pathway.13 The distribution of TFA teachers is generally shifted to the

right, but they also have a meaningful number of relatively more effective teachers as indicated by the

The figure plots the persistent component of a teacher’s effectiveness by employing an empirical Bayes estimator

similar to that suggested in Kane, Rockoff and Staiger (2008). The estimate of teacher effectiveness results from a

regression of student math achievement identical to equation 1 with teacher experience as the only measure of

teacher attributes. The residuals from this regression are shrunken to adjust for the measurement error associated

with the estimates. We should note that while the estimates of effectiveness for each individual teacher are

unbiased, the estimates by pathway taken together to form the distribution of teacher effectiveness over adjusts the

overall distribution of teacher effects. Even so, there is substantial overlap among the pathways.

16

20.
bump in the distribution between effect sizes of 0.4 and 0.6. Although the distributions diverge in some

interesting ways, it is clear that most of the teachers from one pathway are indistinguishable from teachers

who entered through other pathways.

To explore the robustness of these findings, Table 12 compares these estimates across a variety of

model specifications. We examine the consequences: of employing student fixed effects rather than

school fixed effects, of including teacher controls (age, gender, race and ethnicity, whether they passed

their general knowledge certification exam on the first attempt, SAT scores and a series of indicator

variables summarizing the ranking of their under graduate college), and of employing achievement gains

rather than levels as the dependent variable. In general, the effect of gains rather than levels result in only

minor changes in the estimated effects of pathways (columns 1, 3 versus 2, and 4). Similarly employing

student fixed effects rather than school fixed effects as controls changes the estimated coefficients in

small ways, though the regular Teaching Fellows and College Recommending pathways are now

statistically significantly different from Math Immersion at the 5 percent level or better (e.g., column 1 v.

However, due to excess demand from 2004-08, the NYCTF program accepted some applicants

who fell below their internal selection standards. During this period 9 percent of the math immersion

teachers who taught students in our value-added analysis did not met these standards (NYCTF-MI

Below), 51 percent met these criteria (above) and 40 percent did not receive a rating (NYCTF-MI NA).

As shown in column 9 of Table 12, these ratings identify meaningful differences in Math Immersion

teachers. The comparison group is now the Math Immersion teachers who exceeded the selection

threshold. These teachers are on average relatively more effective that their colleagues who were rated

below the threshold (0.044), although the difference is not statistically significant. The difference

between Math Immersion and College Recommending is eliminated when compared to the Math

Immersion teachers who exceeded the threshold and the difference with TFA is reduced. Our best

estimates of the effect of Math Immersion are those presented in column 1, but the results of column 9

indicate that excess demand for math teachers during those years plays a role in the differences between

Math Immersion and other pathways.

Including teacher controls substantially reduces the magnitude of the pathway coefficient

estimates (Table 12, columns 3, 4, 7 and 8) . In general we believe that teacher preparation programs

perform two functions—selection and preparation, and should be judged on the combined effect.

However, we also find it interesting to attempt to disentangle these components by including teacher

controls that can be viewed as proxies for variables programs use in determining admissions. Admittedly

these are not great controls for the characteristics that likely differentiate teachers at point of application.

However, the effect of including the teacher controls that we can observe has the effect of reducing the

17

interesting ways, it is clear that most of the teachers from one pathway are indistinguishable from teachers

who entered through other pathways.

To explore the robustness of these findings, Table 12 compares these estimates across a variety of

model specifications. We examine the consequences: of employing student fixed effects rather than

school fixed effects, of including teacher controls (age, gender, race and ethnicity, whether they passed

their general knowledge certification exam on the first attempt, SAT scores and a series of indicator

variables summarizing the ranking of their under graduate college), and of employing achievement gains

rather than levels as the dependent variable. In general, the effect of gains rather than levels result in only

minor changes in the estimated effects of pathways (columns 1, 3 versus 2, and 4). Similarly employing

student fixed effects rather than school fixed effects as controls changes the estimated coefficients in

small ways, though the regular Teaching Fellows and College Recommending pathways are now

statistically significantly different from Math Immersion at the 5 percent level or better (e.g., column 1 v.

However, due to excess demand from 2004-08, the NYCTF program accepted some applicants

who fell below their internal selection standards. During this period 9 percent of the math immersion

teachers who taught students in our value-added analysis did not met these standards (NYCTF-MI

Below), 51 percent met these criteria (above) and 40 percent did not receive a rating (NYCTF-MI NA).

As shown in column 9 of Table 12, these ratings identify meaningful differences in Math Immersion

teachers. The comparison group is now the Math Immersion teachers who exceeded the selection

threshold. These teachers are on average relatively more effective that their colleagues who were rated

below the threshold (0.044), although the difference is not statistically significant. The difference

between Math Immersion and College Recommending is eliminated when compared to the Math

Immersion teachers who exceeded the threshold and the difference with TFA is reduced. Our best

estimates of the effect of Math Immersion are those presented in column 1, but the results of column 9

indicate that excess demand for math teachers during those years plays a role in the differences between

Math Immersion and other pathways.

Including teacher controls substantially reduces the magnitude of the pathway coefficient

estimates (Table 12, columns 3, 4, 7 and 8) . In general we believe that teacher preparation programs

perform two functions—selection and preparation, and should be judged on the combined effect.

However, we also find it interesting to attempt to disentangle these components by including teacher

controls that can be viewed as proxies for variables programs use in determining admissions. Admittedly

these are not great controls for the characteristics that likely differentiate teachers at point of application.

However, the effect of including the teacher controls that we can observe has the effect of reducing the

17

21.
TFA pathway effect by more than half (0.055 to 0.018) in the model estimated in levels with school fixed

effects. This is consistent with the notion that TFA is very good at recruiting and identifying teachers

who are ultimately effective in producing achievement gains. This also suggests that our proxies for

teacher qualifications are important in improving student achievement.14 In addition, we estimate the

same models presented in Table 12 but limiting the sample to only teachers who began their careers in

2003-04 or later. These results are very similar to those presented in Table 12, however they indicate that

College Recommending teachers outperform Math Immersion teachers (effect size =0.035).15 To

understand this result better, we explore the relationship between experience and pathway in more detail.

The timing of teacher preparation is much different for teachers entering through alternative

certification pathways such as Math Immersion than for teachers entering through College

Recommending programs in New York. State certification requires both pathways to meet essentially the

same requirements but at different points relative to becoming the teacher of record. While College

Recommending teachers meet all of the requirements for an initial teaching license prior to becoming a

classroom teacher, alternatively certified teachers in New York complete an intensive pre-service

component during the summer prior to becoming a classroom teacher, then enroll in a masters program in

education that is typically completed during the first two to three years as a teacher.16

Due to these timing differences, it is useful to explore how the effects of pathways may differ

systematically with the early years of teaching experience. We might expect that teachers entering

through alternative certification pathways might be less effective in their first year or two of teaching but

that the gap would close as they both gained more experience and completed their preparation

requirements.17 Table 13, shows the interaction effects of pathway and experience for a variety of model

specifications. The comparison group is first year Math Immersion teachers. As is expected, the

effectiveness of teachers in all pathways increases with experience.18 Table 14 provides an easier means

of comparing the relative effectiveness of each pathway at each level of experience. Table 14 shows the

difference at each level of experience between each pathway and Math Immersion and whether that

difference is statistically significant. Students of Math Immersion teachers typically have smaller math

achievement gains at every level of experience than those of College Recommending and Teaching

Fellows teachers. However, these differences are typically not statistically significant at the 10 percent

Boyd et al. 2008 explore the effect of teacher qualifications in detail.

Full results available from the authors.

For more details on certification requirements in New York State, see

http://www.highered.nysed.gov/tcert/certificate/typesofcerts.htm

In earlier work, we found precisely this result (Boyd et al, 2006).

Based on these estimates we can distinguish whether these gains to experience reflect teachers becoming more

adept at improving student achievement over time or a composition effect of less effective teachers leaving the

workforce. Based on other work we believe that both explanations contribute to the results presented.

18

effects. This is consistent with the notion that TFA is very good at recruiting and identifying teachers

who are ultimately effective in producing achievement gains. This also suggests that our proxies for

teacher qualifications are important in improving student achievement.14 In addition, we estimate the

same models presented in Table 12 but limiting the sample to only teachers who began their careers in

2003-04 or later. These results are very similar to those presented in Table 12, however they indicate that

College Recommending teachers outperform Math Immersion teachers (effect size =0.035).15 To

understand this result better, we explore the relationship between experience and pathway in more detail.

The timing of teacher preparation is much different for teachers entering through alternative

certification pathways such as Math Immersion than for teachers entering through College

Recommending programs in New York. State certification requires both pathways to meet essentially the

same requirements but at different points relative to becoming the teacher of record. While College

Recommending teachers meet all of the requirements for an initial teaching license prior to becoming a

classroom teacher, alternatively certified teachers in New York complete an intensive pre-service

component during the summer prior to becoming a classroom teacher, then enroll in a masters program in

education that is typically completed during the first two to three years as a teacher.16

Due to these timing differences, it is useful to explore how the effects of pathways may differ

systematically with the early years of teaching experience. We might expect that teachers entering

through alternative certification pathways might be less effective in their first year or two of teaching but

that the gap would close as they both gained more experience and completed their preparation

requirements.17 Table 13, shows the interaction effects of pathway and experience for a variety of model

specifications. The comparison group is first year Math Immersion teachers. As is expected, the

effectiveness of teachers in all pathways increases with experience.18 Table 14 provides an easier means

of comparing the relative effectiveness of each pathway at each level of experience. Table 14 shows the

difference at each level of experience between each pathway and Math Immersion and whether that

difference is statistically significant. Students of Math Immersion teachers typically have smaller math

achievement gains at every level of experience than those of College Recommending and Teaching

Fellows teachers. However, these differences are typically not statistically significant at the 10 percent

Boyd et al. 2008 explore the effect of teacher qualifications in detail.

Full results available from the authors.

For more details on certification requirements in New York State, see

http://www.highered.nysed.gov/tcert/certificate/typesofcerts.htm

In earlier work, we found precisely this result (Boyd et al, 2006).

Based on these estimates we can distinguish whether these gains to experience reflect teachers becoming more

adept at improving student achievement over time or a composition effect of less effective teachers leaving the

workforce. Based on other work we believe that both explanations contribute to the results presented.

18

22.
level. Math Immersion teachers are estimated to be less effective than TFA teachers at each level of

experience, although these effects are statistically significant only in the first and second years, which

likely reflects the small sample sizes in both groups, as the point estimates remain relatively large.

However, these differences largely disappear when we include variables intended to measure teacher

qualifications. Math Immersion teachers appear to be more effective than teachers in the Other category,

although these differences are statistically significant only in the first two years without the teacher

Our earlier analysis of the structure and content of the preparation that Math Immersion Teachers

received revealed substantive variation across the five programs that prepared the vast majority of Math

Immersion Teachers. Further we found some differences in the students who participated in each of these

programs. To explore whether these differences resulted in differential student achievement gains, we

estimated models that included all pathways but also identified the specific institutions through which

Math Immersion teachers were prepared, see Table 15. Here teachers enrolling at Campus Z are the

comparison group. These results suggest that Campuses B, C and E appear to outperform Campus Z in

most model specifications and Campus D does so less consistently. When Campus Z is eliminated from

the estimation of pathway effects (Table 11) there are no differences between College Recommending,

Teaching Fellows and Math Immersion teachers. Students of TFA teachers have substantially better math

achievement than those of teachers from the other pathways.19 Taken together, these results suggest that

the specific implementation of Math Immersion in programs can importantly affect teacher preparation

and resulting student achievement.

In trying to understand the relatively less effective performance of teachers from Campus Z, we

refer back to our analysis of program requirements and of the survey results. As described above,

Campus Z had the fewest requirements in math and math methods of all the Math Immersion campuses,

while Campus C had the greatest followed closely by A and B. Given the few programs training Math

Immersion teachers, we can not hope to make causal statements of the effects of program design on

outcomes, but these results do suggest that the relative focus on math content and math pedagogy offered

by a program may influence a teacher’s ability to improve math achievement.

Question 3: How does the retention of Math Immersion candidates compare to math teachers

entering through other pathways?

The students of individuals who enter teaching through the Math Immersion program appear to

have math achievement gains that are somewhat lower than those of College Recommending and

substantially lower than TFA teachers, other things equal. Most policy makers appropriately place great

Results available from authors on request.

19

experience, although these effects are statistically significant only in the first and second years, which

likely reflects the small sample sizes in both groups, as the point estimates remain relatively large.

However, these differences largely disappear when we include variables intended to measure teacher

qualifications. Math Immersion teachers appear to be more effective than teachers in the Other category,

although these differences are statistically significant only in the first two years without the teacher

Our earlier analysis of the structure and content of the preparation that Math Immersion Teachers

received revealed substantive variation across the five programs that prepared the vast majority of Math

Immersion Teachers. Further we found some differences in the students who participated in each of these

programs. To explore whether these differences resulted in differential student achievement gains, we

estimated models that included all pathways but also identified the specific institutions through which

Math Immersion teachers were prepared, see Table 15. Here teachers enrolling at Campus Z are the

comparison group. These results suggest that Campuses B, C and E appear to outperform Campus Z in

most model specifications and Campus D does so less consistently. When Campus Z is eliminated from

the estimation of pathway effects (Table 11) there are no differences between College Recommending,

Teaching Fellows and Math Immersion teachers. Students of TFA teachers have substantially better math

achievement than those of teachers from the other pathways.19 Taken together, these results suggest that

the specific implementation of Math Immersion in programs can importantly affect teacher preparation

and resulting student achievement.

In trying to understand the relatively less effective performance of teachers from Campus Z, we

refer back to our analysis of program requirements and of the survey results. As described above,

Campus Z had the fewest requirements in math and math methods of all the Math Immersion campuses,

while Campus C had the greatest followed closely by A and B. Given the few programs training Math

Immersion teachers, we can not hope to make causal statements of the effects of program design on

outcomes, but these results do suggest that the relative focus on math content and math pedagogy offered

by a program may influence a teacher’s ability to improve math achievement.

Question 3: How does the retention of Math Immersion candidates compare to math teachers

entering through other pathways?

The students of individuals who enter teaching through the Math Immersion program appear to

have math achievement gains that are somewhat lower than those of College Recommending and

substantially lower than TFA teachers, other things equal. Most policy makers appropriately place great

Results available from authors on request.

19

23.
weight on student outcomes as means of evaluating alternative policies and programs. Increasingly,

teacher attrition has become an important issue and there is concern that individuals who enter teaching

through alternative certification routes, such as Math Immersion, are less likely to remain in teaching.

Teacher attrition is potentially troubling for several reasons—there is very strong evidence that the

effectiveness of teachers improves during their first four or five years (see Rockoff, 2004; Rivkin et al,

2005, Boyd et al. 2008b) and as a result losing teachers who have gained experience directly influences

student achievement, other things being equal. There are indirect effects as well. High turnover rates

make it difficult for school leaders and teachers to work together effectively thus compromising the

learning environment. Finally, the costs associated with recruiting and mentoring new teachers represents

a substantial investment that could easily be employed in other ways (see, for example, Barnes et al.

We employ personnel files from the New York City Department of Education to explore teacher

attrition. These files identify each time a teacher changes status, e.g., retire, transfers schools, take a leave

of absence, etc. Using these data we define a teacher in any given year as someone employed as a teacher

as of October 15th of that academic year.20 Teachers are defined as remaining in the same school if their

personnel records indicate they began the next academic year teaching in the same school; they are

defined as having transferred to another school in NYC at the beginning of the next academic year they

are a teacher in a different school; and they are defined as leaving teaching in New York City public

schools if personnel records show they have retired, quit or on leave and not returning for more than one

Descriptive statistics characterizing the attrition rates for math-certified teachers by pathway in

grades 6 through 12 are shown in Table 16. Math Immersion teachers had relatively low first year attrition

but in years 2 through 4, Math Immersion teachers, like teachers from other alternative certification

pathways experienced a higher likelihood of transferring and leaving the New York City public school

system. By the end of what would have been their fourth year, more than 40 percent of Math Immersion

teachers have left teaching in New York City and fewer than a third remain in their original school. This

is meaningfully higher attrition than College Recommending teachers, 31 percent of whom have left New

York City teaching while about half remain in their original school. Math Immersion teachers persist in

teaching at somewhat greater levels than other New York City Teaching Fellows, and at much greater

This definition would exclude individuals in a year who may be teaching under some other title, such as a

substitute teacher; those who are not teachers, and an individual who began teaching in a given year after October

15th. Individuals who began after October 15th and who continued as a teacher in the subsequent year are included

for that year.

There are cases where individuals are not teachers in NYC public schools for more than a year and subsequently

return to teach, but these cases are relatively rare. It is also true that teachers who have left teaching in NYC may be

teaching in other school districts or in an administrative position in NYC.

20

teacher attrition has become an important issue and there is concern that individuals who enter teaching

through alternative certification routes, such as Math Immersion, are less likely to remain in teaching.

Teacher attrition is potentially troubling for several reasons—there is very strong evidence that the

effectiveness of teachers improves during their first four or five years (see Rockoff, 2004; Rivkin et al,

2005, Boyd et al. 2008b) and as a result losing teachers who have gained experience directly influences

student achievement, other things being equal. There are indirect effects as well. High turnover rates

make it difficult for school leaders and teachers to work together effectively thus compromising the

learning environment. Finally, the costs associated with recruiting and mentoring new teachers represents

a substantial investment that could easily be employed in other ways (see, for example, Barnes et al.

We employ personnel files from the New York City Department of Education to explore teacher

attrition. These files identify each time a teacher changes status, e.g., retire, transfers schools, take a leave

of absence, etc. Using these data we define a teacher in any given year as someone employed as a teacher

as of October 15th of that academic year.20 Teachers are defined as remaining in the same school if their

personnel records indicate they began the next academic year teaching in the same school; they are

defined as having transferred to another school in NYC at the beginning of the next academic year they

are a teacher in a different school; and they are defined as leaving teaching in New York City public

schools if personnel records show they have retired, quit or on leave and not returning for more than one

Descriptive statistics characterizing the attrition rates for math-certified teachers by pathway in

grades 6 through 12 are shown in Table 16. Math Immersion teachers had relatively low first year attrition

but in years 2 through 4, Math Immersion teachers, like teachers from other alternative certification

pathways experienced a higher likelihood of transferring and leaving the New York City public school

system. By the end of what would have been their fourth year, more than 40 percent of Math Immersion

teachers have left teaching in New York City and fewer than a third remain in their original school. This

is meaningfully higher attrition than College Recommending teachers, 31 percent of whom have left New

York City teaching while about half remain in their original school. Math Immersion teachers persist in

teaching at somewhat greater levels than other New York City Teaching Fellows, and at much greater

This definition would exclude individuals in a year who may be teaching under some other title, such as a

substitute teacher; those who are not teachers, and an individual who began teaching in a given year after October

15th. Individuals who began after October 15th and who continued as a teacher in the subsequent year are included

for that year.

There are cases where individuals are not teachers in NYC public schools for more than a year and subsequently

return to teach, but these cases are relatively rare. It is also true that teachers who have left teaching in NYC may be

teaching in other school districts or in an administrative position in NYC.

20

24.
levels than Teach for America teachers. By the conclusion of the fourth year, nearly 80 percent of TFA

teachers have left teaching in New York City public schools, while fewer than 10 percent remain in their

original schools.

How would the academic gains of students differ as a result of school officials systematically

filling job openings by hiring teachers entering through one pathway versus another? The answer, in part,

depends upon the relative effectiveness of teachers at each level of experience across pathway as

discussed above. However, it is also necessary to account for differences in retention rates across

pathways. This follows from the meaningful gains in teacher value-added associated with increased

experience over the first few year of teaching. If one pathway consistently has higher turnover even if its

teachers do well relative to those in other pathways with the same experience, the pathway may not be

providing the most effective teachers, on average.

How does the average value-added of teachers vary across pathways once differences in teacher

retention rates are taken into account? We address this question using the following simulation. Suppose

that school officials hired an arbitrary number of new teachers (e.g., 1000) from each of the pathways. For

subsequent years, the teachers hired from each pathway are allowed to age through the experience

distribution, applying the pathway dependent retention rates implied in Table 16. Teachers who leave are

replaced by teachers with no prior experience from the same pathway. These new hires in turn age

through the system. In this way, it is possible to simulate how the experience distribution of teachers from

each pathway would evolve over time and differ across pathways thus allowing us to estimate how such

differences affect the average value-added of the teachers from each pathway. These results are shown in

Table 17. The most striking result is that the clear advantage that TFA teachers had at every level of

experience (see the value added estimates from Table 13 replicated in the bottom panel of Table 18)

dissipates as the very high attrition of TFA teachers following their second and third years of experience

causes many more TFA teachers to be replaced by novices. Because of its lower attrition the College

Recommending pathway develops a small advantage relative to the Math Immersion and is roughly

equivalent to regular the Teaching Fellows and TFA pathways.

V. Conclusion

Math Immersion was born of necessity to assist in filling the vacancies when uncertified teachers

were barred from teaching and insufficient numbers of College Recommending or alternatively certified

teachers who met the existing math certification requirements were available to teach in New York City.

Remarkably four years since its inception, the Math Immersion preparation pathway supplies 50 percent

of all new certified math teachers to New York City public schools. Given the prominence of the Math

Immersion pathway in supplying math teachers to NYC schools, it is important to examine the design of

21

teachers have left teaching in New York City public schools, while fewer than 10 percent remain in their

original schools.

How would the academic gains of students differ as a result of school officials systematically

filling job openings by hiring teachers entering through one pathway versus another? The answer, in part,

depends upon the relative effectiveness of teachers at each level of experience across pathway as

discussed above. However, it is also necessary to account for differences in retention rates across

pathways. This follows from the meaningful gains in teacher value-added associated with increased

experience over the first few year of teaching. If one pathway consistently has higher turnover even if its

teachers do well relative to those in other pathways with the same experience, the pathway may not be

providing the most effective teachers, on average.

How does the average value-added of teachers vary across pathways once differences in teacher

retention rates are taken into account? We address this question using the following simulation. Suppose

that school officials hired an arbitrary number of new teachers (e.g., 1000) from each of the pathways. For

subsequent years, the teachers hired from each pathway are allowed to age through the experience

distribution, applying the pathway dependent retention rates implied in Table 16. Teachers who leave are

replaced by teachers with no prior experience from the same pathway. These new hires in turn age

through the system. In this way, it is possible to simulate how the experience distribution of teachers from

each pathway would evolve over time and differ across pathways thus allowing us to estimate how such

differences affect the average value-added of the teachers from each pathway. These results are shown in

Table 17. The most striking result is that the clear advantage that TFA teachers had at every level of

experience (see the value added estimates from Table 13 replicated in the bottom panel of Table 18)

dissipates as the very high attrition of TFA teachers following their second and third years of experience

causes many more TFA teachers to be replaced by novices. Because of its lower attrition the College

Recommending pathway develops a small advantage relative to the Math Immersion and is roughly

equivalent to regular the Teaching Fellows and TFA pathways.

V. Conclusion

Math Immersion was born of necessity to assist in filling the vacancies when uncertified teachers

were barred from teaching and insufficient numbers of College Recommending or alternatively certified

teachers who met the existing math certification requirements were available to teach in New York City.

Remarkably four years since its inception, the Math Immersion preparation pathway supplies 50 percent

of all new certified math teachers to New York City public schools. Given the prominence of the Math

Immersion pathway in supplying math teachers to NYC schools, it is important to examine the design of

21

25.
the program and its effects on student achievement.

In general, we find that Math Immersion teachers have stronger academic qualifications, e.g.,

SAT scores and licensure exam scores, than their College Recommending peers, although they have

weaker qualifications than Teach for America teachers. In addition, Math Immersion teachers are found

in some of the most challenging classrooms in New York City. In this respect, the program has

succeeded in attracting teachers with stronger academic backgrounds to teach in high needs schools.

However, despite stronger general academic qualifications Math Immersion teachers are

responsible for somewhat smaller gains in math achievement for middle school math students than are

College Recommending teachers, although in many cases these differences are not statistically

significant. Math Immersion teachers have substantially smaller gains than Teach for America teachers.

These results are robust to a variety of alternative specifications. However, Math Immersion teachers are

more likely to leave teaching in New York City than are their College Recommending peers, but

substantially less likely to do so than Teach for America teachers. In simulating the impact of attrition on

the effectiveness of different pathways, the College Recommending pathway develops a small advantage

relative to Math Immersion but is roughly equivalent to Teach for America and regular Teaching Fellows.

Based on the value-added and attrition results, one might be tempted to conclude that New York

City should be hiring more TFA and College Recommending teachers and looking to dismantle the Math

Immersion program. However, such a conclusion ignores the fact that for many years prior to the

creation Math Immersion New York City hired a very large number of uncertified teachers; many of these

teachers taught middle and high school math classes precisely because there were insufficient numbers of

College Recommending teachers certified in math who were willing to staff these low-performing

schools. While the number of math teachers prepared through College Recommending programs has

increased in recent years, these programs are still not preparing sufficient math teachers to fill the

demand. Additionally, due to reduced demand for teachers beginning in 2008-09, the Math Immersion

program has been able to raise the standards by which it accepts applicants. It will be interesting to assess

whether this change affects the average effectiveness of new cohorts.

Recruiting and preparing high quality teachers to meet the demand of K-12 schools is a massive

undertaking and many high needs schools have found it very difficult to recruit and retain effective

teachers. While there is a great deal to learn regarding the effective recruitment and preparation of

teachers, there is already ample evidence that each pathway produces teachers who range in effectiveness,

with some very effective teachers and some teachers who are less so. Similarly, within pathways

programs vary in their effectiveness. This suggests that the policy discussion about teacher preparation

should be focused on the features of programs and pathways that contribute most importantly to

successful teachers and not whether one pathway outperforms another. Rather we believe that

22

In general, we find that Math Immersion teachers have stronger academic qualifications, e.g.,

SAT scores and licensure exam scores, than their College Recommending peers, although they have

weaker qualifications than Teach for America teachers. In addition, Math Immersion teachers are found

in some of the most challenging classrooms in New York City. In this respect, the program has

succeeded in attracting teachers with stronger academic backgrounds to teach in high needs schools.

However, despite stronger general academic qualifications Math Immersion teachers are

responsible for somewhat smaller gains in math achievement for middle school math students than are

College Recommending teachers, although in many cases these differences are not statistically

significant. Math Immersion teachers have substantially smaller gains than Teach for America teachers.

These results are robust to a variety of alternative specifications. However, Math Immersion teachers are

more likely to leave teaching in New York City than are their College Recommending peers, but

substantially less likely to do so than Teach for America teachers. In simulating the impact of attrition on

the effectiveness of different pathways, the College Recommending pathway develops a small advantage

relative to Math Immersion but is roughly equivalent to Teach for America and regular Teaching Fellows.

Based on the value-added and attrition results, one might be tempted to conclude that New York

City should be hiring more TFA and College Recommending teachers and looking to dismantle the Math

Immersion program. However, such a conclusion ignores the fact that for many years prior to the

creation Math Immersion New York City hired a very large number of uncertified teachers; many of these

teachers taught middle and high school math classes precisely because there were insufficient numbers of

College Recommending teachers certified in math who were willing to staff these low-performing

schools. While the number of math teachers prepared through College Recommending programs has

increased in recent years, these programs are still not preparing sufficient math teachers to fill the

demand. Additionally, due to reduced demand for teachers beginning in 2008-09, the Math Immersion

program has been able to raise the standards by which it accepts applicants. It will be interesting to assess

whether this change affects the average effectiveness of new cohorts.

Recruiting and preparing high quality teachers to meet the demand of K-12 schools is a massive

undertaking and many high needs schools have found it very difficult to recruit and retain effective

teachers. While there is a great deal to learn regarding the effective recruitment and preparation of

teachers, there is already ample evidence that each pathway produces teachers who range in effectiveness,

with some very effective teachers and some teachers who are less so. Similarly, within pathways

programs vary in their effectiveness. This suggests that the policy discussion about teacher preparation

should be focused on the features of programs and pathways that contribute most importantly to

successful teachers and not whether one pathway outperforms another. Rather we believe that

22

26.
policymakers are well advised to invest in the development of programs that draw on the most promising

features of the more successful existing programs.

As we have argued earlier, programs can influence their outcomes through both the recruitment

and selection of promising candidates and strong preparation. The analysis in this paper suggests that on

average TFA teachers produce student achievement gains in middle school math that exceed those of

teachers from other pathways with comparable experience. TFA has invested heavily in the recruitment

and selection of its Corps members and this effort appears to account for a substantial portion of the

difference between TFA and Math Immersion or College Recommending teachers. However, this

advantage is largely eliminated once the much higher attrition of TFA teachers is taken into account.

Additionally, TFA recruits far fewer teachers into New York City schools than do either the Teaching

Fellows or College Recommending pathways. However, other programs could learn from TFA regarding

the selection of candidates who are effective teachers in low-performing classrooms.

Selection, however, is only one part of the equation. We also suspect, although we have only

limited evidence to support the hypothesis, that a teacher’s preparation in math content and pedagogy may

influence the math achievement of his/her middle school students. We found evidence regarding the

positive influence of math content and the nature of field experiences when we examined the attributes of

teacher preparation programs in childhood education (Boyd et al. 2009). The somewhat weaker

performance of Math Immersion teachers relative to College Recommending teachers in light of the

stronger academic skills of Math Immersion teachers also may suggest that preparation can improve

teacher effectiveness; and the TFA advantage in middle school mathematics may in part signal the

importance of strong math content knowledge as well. In addition, the more circumstantial evidence on

the impact of a program with limited content preparation suggested by the weak effects of program Z also

suggests that programs invest in math-specific preparation, in both content and pedagogy.

One of the implications of this line of reasoning is to design and evaluate programs that combine the

recruitment of academically strong candidates with high quality preparation in math content, math

pedagogy, and field experiences that provide them with opportunities to observe effective teachers and

practice their teaching skills in closely supervised classrooms of high needs students. Another

implication to explore is the notion that the availability of teachers from a variety of pathways benefits

schools that have been traditionally difficult to staff because each pathway is able to recruit some good

teachers for these schools. The variability of teachers within each pathway points both to the importance

of better understanding effective recruitment and preparation and to the importance of monitoring and

supporting teachers once in the classroom.

Improving the quality of math teaching in our schools will require more systematic and rigorous

evaluation of the selection and preparation components of teacher education. State departments of

23

features of the more successful existing programs.

As we have argued earlier, programs can influence their outcomes through both the recruitment

and selection of promising candidates and strong preparation. The analysis in this paper suggests that on

average TFA teachers produce student achievement gains in middle school math that exceed those of

teachers from other pathways with comparable experience. TFA has invested heavily in the recruitment

and selection of its Corps members and this effort appears to account for a substantial portion of the

difference between TFA and Math Immersion or College Recommending teachers. However, this

advantage is largely eliminated once the much higher attrition of TFA teachers is taken into account.

Additionally, TFA recruits far fewer teachers into New York City schools than do either the Teaching

Fellows or College Recommending pathways. However, other programs could learn from TFA regarding

the selection of candidates who are effective teachers in low-performing classrooms.

Selection, however, is only one part of the equation. We also suspect, although we have only

limited evidence to support the hypothesis, that a teacher’s preparation in math content and pedagogy may

influence the math achievement of his/her middle school students. We found evidence regarding the

positive influence of math content and the nature of field experiences when we examined the attributes of

teacher preparation programs in childhood education (Boyd et al. 2009). The somewhat weaker

performance of Math Immersion teachers relative to College Recommending teachers in light of the

stronger academic skills of Math Immersion teachers also may suggest that preparation can improve

teacher effectiveness; and the TFA advantage in middle school mathematics may in part signal the

importance of strong math content knowledge as well. In addition, the more circumstantial evidence on

the impact of a program with limited content preparation suggested by the weak effects of program Z also

suggests that programs invest in math-specific preparation, in both content and pedagogy.

One of the implications of this line of reasoning is to design and evaluate programs that combine the

recruitment of academically strong candidates with high quality preparation in math content, math

pedagogy, and field experiences that provide them with opportunities to observe effective teachers and

practice their teaching skills in closely supervised classrooms of high needs students. Another

implication to explore is the notion that the availability of teachers from a variety of pathways benefits

schools that have been traditionally difficult to staff because each pathway is able to recruit some good

teachers for these schools. The variability of teachers within each pathway points both to the importance

of better understanding effective recruitment and preparation and to the importance of monitoring and

supporting teachers once in the classroom.

Improving the quality of math teaching in our schools will require more systematic and rigorous

evaluation of the selection and preparation components of teacher education. State departments of

23

27.
education must take the lead in these efforts, given their role in determining teacher licensure

requirements. The federal Race to the Top initiative provides states with the policy and financial leverage

to embrace this challenge.

24

requirements. The federal Race to the Top initiative provides states with the policy and financial leverage

to embrace this challenge.

24

28.
Figure 1: Number of Teachers Entering New York City Public Schools by Pathway, 2002-

4,500

4,000

3,500

CR

3,000

2,500

2,000

NYCTF

1,500

TL

1,000

TFA

500

0

2002 2003 2004 2005 2006 2007 2008

Figure 2: Number of Entering Math Certified Teachers New York City,

by Pathway, 2002-2008

450

400

350

NYCTF-MI

300

250

CR

200

150

100

NYCTF

50

TFA

0

2003 2004 2005 2006 2007 2008

25

4,500

4,000

3,500

CR

3,000

2,500

2,000

NYCTF

1,500

TL

1,000

TFA

500

0

2002 2003 2004 2005 2006 2007 2008

Figure 2: Number of Entering Math Certified Teachers New York City,

by Pathway, 2002-2008

450

400

350

NYCTF-MI

300

250

CR

200

150

100

NYCTF

50

TFA

0

2003 2004 2005 2006 2007 2008

25

29.
Figure 3: Distribution of Teacher Value Added by Pathway, with Empirical Bayes

Shrinking, 2004-2008

0

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

CR NYCTF-MI TFA

26

Shrinking, 2004-2008

0

-0.6 -0.4 -0.2 0 0.2 0.4 0.6

CR NYCTF-MI TFA

26

30.
Table 1: Attributes of Students Taught by First-year Teachers by Pathway, Grade 8, 2006

Student Attributes CR NYCTF NYCTF- TFA other

MI

Lagged Math Achievement 0.238 -0.125 -0.051 -0.139 -0.061

Proportion Black 0.292 0.277 0.322 0.442 0.403

Proportion Hispanic 0.358 0.496 0.493 0.527 0.372

Proportion Free Lunch 0.547 0.664 0.635 0.619 0.66

Classsize 27.6 27.8 26.9 26.3 26.1

Lagged Student Absences 12.3 13.4 13.1 14.8 13.5

Lagged Suspensions 0.037 0.064 0.062 0.023 0.042

Table 2: Attributes of Entering Math Certified New York City Teachers by Pathway, 2004-2008

CR NYCTF NYCTF-MI TFA

Teacher Attributes High Middle High Middle High Middle High Middle

School School School School School School School School

Female 0.648 0.732 0.446 0.563 0.479 0.546 0.492 0.551

Black 0.073 0.105 0.130 0.197 0.142 0.200 0.082 0.141

Hispanic 0.065 0.046 0.068 0.066 0.085 0.074 0.066 0.043

Age 29.7 28.9 30.4 29.1 31.1 30 23.6 23.5

Last Score 255 251 273 268 274 271 279 279

CST Math Score 262 251 268 263 257 251 268 269

SAT Math 600 556 626 611 616 589 710 648

SAT Verbal 506 483 580 545 577 564 627 623

Number of Teachers 478 157 195 64 1098 542 61 98

27

Student Attributes CR NYCTF NYCTF- TFA other

MI

Lagged Math Achievement 0.238 -0.125 -0.051 -0.139 -0.061

Proportion Black 0.292 0.277 0.322 0.442 0.403

Proportion Hispanic 0.358 0.496 0.493 0.527 0.372

Proportion Free Lunch 0.547 0.664 0.635 0.619 0.66

Classsize 27.6 27.8 26.9 26.3 26.1

Lagged Student Absences 12.3 13.4 13.1 14.8 13.5

Lagged Suspensions 0.037 0.064 0.062 0.023 0.042

Table 2: Attributes of Entering Math Certified New York City Teachers by Pathway, 2004-2008

CR NYCTF NYCTF-MI TFA

Teacher Attributes High Middle High Middle High Middle High Middle

School School School School School School School School

Female 0.648 0.732 0.446 0.563 0.479 0.546 0.492 0.551

Black 0.073 0.105 0.130 0.197 0.142 0.200 0.082 0.141

Hispanic 0.065 0.046 0.068 0.066 0.085 0.074 0.066 0.043

Age 29.7 28.9 30.4 29.1 31.1 30 23.6 23.5

Last Score 255 251 273 268 274 271 279 279

CST Math Score 262 251 268 263 257 251 268 269

SAT Math 600 556 626 611 616 589 710 648

SAT Verbal 506 483 580 545 577 564 627 623

Number of Teachers 478 157 195 64 1098 542 61 98

27

31.
Table 3: Attributes of Entering Math Certified Math Immersion Teachers by Whether They

Had Math Related Major or Math Related Work Experience, 2004-2008*

Math Not Math

Teacher Attributes Related Related

Female 0.444 0.556

Black 0.192 0.132

Hispanic 0.090 0.073

Age 32.0 29.5

Last Score 270 277

Last Science/math sub-score 275 283

CST Math Score 255 254

ATS Secondary Score 251 253

SAT Math 594 622

SAT Verbal 554 595

* Coded as math related if individual had either math related undergraduate major or math related work

experience, not math related otherwise if not missing. Math related majors included: math, accounting

astronomy, biochemistry, biology, business, chemistry, computer science, economics, engineering,

finance, information systems, physics, and statistics. Math related work experiences included:

engineering, financial, and public accounting.

Table 4: Number of New York City Teaching Fellows Prepared by Various Campuses by

Math Immersion and Math Certification Status, 2004-2007

All Teachers by Institution

Math Immersion

Status A B C D Z

NYCTF-MI 290 536 75 270 441

NYCTF-Not MI 1082 1077 751 185 1431

Total 1372 1613 826 455 1872

Math Certified Teachers by Institution

A B C D Z

NYCTF-MI 290 536 75 270 441

NYCTF-Not MI 46 78 19 35 75

Total 336 614 94 305 516

28

Had Math Related Major or Math Related Work Experience, 2004-2008*

Math Not Math

Teacher Attributes Related Related

Female 0.444 0.556

Black 0.192 0.132

Hispanic 0.090 0.073

Age 32.0 29.5

Last Score 270 277

Last Science/math sub-score 275 283

CST Math Score 255 254

ATS Secondary Score 251 253

SAT Math 594 622

SAT Verbal 554 595

* Coded as math related if individual had either math related undergraduate major or math related work

experience, not math related otherwise if not missing. Math related majors included: math, accounting

astronomy, biochemistry, biology, business, chemistry, computer science, economics, engineering,

finance, information systems, physics, and statistics. Math related work experiences included:

engineering, financial, and public accounting.

Table 4: Number of New York City Teaching Fellows Prepared by Various Campuses by

Math Immersion and Math Certification Status, 2004-2007

All Teachers by Institution

Math Immersion

Status A B C D Z

NYCTF-MI 290 536 75 270 441

NYCTF-Not MI 1082 1077 751 185 1431

Total 1372 1613 826 455 1872

Math Certified Teachers by Institution

A B C D Z

NYCTF-MI 290 536 75 270 441

NYCTF-Not MI 46 78 19 35 75

Total 336 614 94 305 516

28

32.
Table 5: Attributes of Entering Math Certified NYCTF Teachers by Math Immersion Status

and Preparing Campus, 2004-2008

A B C D Z

Teacher Attributes High Middle High Middle High Middle High Middle High Middle

Scho School School School School School School School School School

ol

Female 0.484 0.566 0.477 0.558 0.360 0.520 0.468 0.534 0.509 0.538

Black 0.222 0.320 0.139 0.209 0.082 0.240 0.108 0.165 0.125 0.127

Hispanic 0.032 0.062 0.123 0.086 0.102 0.080 0.088 0.046 0.069 0.093

Age 30.4 29.2 31.1 29.2 37.2 30.2 31.7 31.9 29.9 29.7

Last Score 274 270 275 272 271 268 272 268 277 276

Last Science/math sub-score 278 272 281 276 275 280 281 279 284 282

CST Math Score 259 251 255 250 258 243 255 249 257 257

ATS Secondary Score 241 241 262 250 247 276 253 256 245 254

SAT Math 611 563 609 589 633 582 271 262 625 616

SAT Verbal 567 542 578 576 566 594 618 586 589 586

573 542

Number of Teachers 191 99 371 165 50 25 154 116 322 119

29

and Preparing Campus, 2004-2008

A B C D Z

Teacher Attributes High Middle High Middle High Middle High Middle High Middle

Scho School School School School School School School School School

ol

Female 0.484 0.566 0.477 0.558 0.360 0.520 0.468 0.534 0.509 0.538

Black 0.222 0.320 0.139 0.209 0.082 0.240 0.108 0.165 0.125 0.127

Hispanic 0.032 0.062 0.123 0.086 0.102 0.080 0.088 0.046 0.069 0.093

Age 30.4 29.2 31.1 29.2 37.2 30.2 31.7 31.9 29.9 29.7

Last Score 274 270 275 272 271 268 272 268 277 276

Last Science/math sub-score 278 272 281 276 275 280 281 279 284 282

CST Math Score 259 251 255 250 258 243 255 249 257 257

ATS Secondary Score 241 241 262 250 247 276 253 256 245 254

SAT Math 611 563 609 589 633 582 271 262 625 616

SAT Verbal 567 542 578 576 566 594 618 586 589 586

573 542

Number of Teachers 191 99 371 165 50 25 154 116 322 119

29

33.
Table 6: Required Courses and Credit Hours for Key Courses, College Recommending

and Math Immersion Programs, Means and Standard Deviations

College Math Math Classroom Learning Assess- Special Ed Diversity

Recommending Courses Methods Management ment

Programs

Graduate programs

Courses

Mean 1.64 2.00 0.29 1.29 0.50 0.57 0.50

Standard deviation 1.78 1.11 0.61 0.73 0.52 0.65 0.65

Credits

Mean 4.93 5.79 0.86 3.75 1.29 1.71 1.36

Standard deviation 5.34 3.29 1.83 2.16 1.44 1.94 1.91

Undergraduate programs

Courses

Mean 3.82 1.36 0.64 1.73 0.00 0.36 0.36

Standard deviation 3.76 0.50 0.67 0.90 0.00 0.50 0.67

Credits

Mean 11.00 4.71 1.75 4.50 0.25 1.33 1.58

Standard deviation 11.29 1.38 2.26 2.70 0.00 1.66 2.46

Math Immersion

Programs

Courses

Mean 4.20 2.80 0.33 1.00 0.40 0.40 0.25

Standard deviation 1.92 0.84 0.58 0.00 0.55 0.55 0.50

Credits

Mean 12.60 8.40 0.60 2.40 1.20 1.20 0.60

Standard deviation 5.77 2.51 1.34 1.34 1.64 1.64 1.34

30

and Math Immersion Programs, Means and Standard Deviations

College Math Math Classroom Learning Assess- Special Ed Diversity

Recommending Courses Methods Management ment

Programs

Graduate programs

Courses

Mean 1.64 2.00 0.29 1.29 0.50 0.57 0.50

Standard deviation 1.78 1.11 0.61 0.73 0.52 0.65 0.65

Credits

Mean 4.93 5.79 0.86 3.75 1.29 1.71 1.36

Standard deviation 5.34 3.29 1.83 2.16 1.44 1.94 1.91

Undergraduate programs

Courses

Mean 3.82 1.36 0.64 1.73 0.00 0.36 0.36

Standard deviation 3.76 0.50 0.67 0.90 0.00 0.50 0.67

Credits

Mean 11.00 4.71 1.75 4.50 0.25 1.33 1.58

Standard deviation 11.29 1.38 2.26 2.70 0.00 1.66 2.46

Math Immersion

Programs

Courses

Mean 4.20 2.80 0.33 1.00 0.40 0.40 0.25

Standard deviation 1.92 0.84 0.58 0.00 0.55 0.55 0.50

Credits

Mean 12.60 8.40 0.60 2.40 1.20 1.20 0.60

Standard deviation 5.77 2.51 1.34 1.34 1.64 1.64 1.34

30

34.
Table 7: Teachers' Perceptions of Their Preparation by Preparation Pathways, 2005 Survey of First Year Teachers

Preparation in Field General Opps Subject Matter

Specific Experience to Learn Preparation in Preparation for

Pathway Strategies Quality Teaching Math Math SPED students

College

Recommending 0.331 0.441 0.386 0.038 0.358

[2.99]*** [3.91]*** [3.54]*** [0.33] [3.13]***

Teaching Fellows 0.274 -0.052 -0.350 -0.462 0.215

[2.50]** [-0.46] [-3.32]*** [-4.12]*** [1.91]*

Teach For America 0.604 0.810 -0.007 -0.561 0.272

[2.74]*** [3.65]*** [-0.03] [-2.48]** [1.22]

Other Path 0.004 0.230 0.371 0.320 0.436

[0.04] [1.87]* [3.31]*** [2.74]*** [3.73]***

N 558 528 543 541 551

* In addition to the pathway indicator variables each regression contains school context factors, which include a factor representing: teacher

influence on planning and teaching, administrative quality, staff collegiality and support, student attitudes and behavior, school facilities, and

school safety.

31

Preparation in Field General Opps Subject Matter

Specific Experience to Learn Preparation in Preparation for

Pathway Strategies Quality Teaching Math Math SPED students

College

Recommending 0.331 0.441 0.386 0.038 0.358

[2.99]*** [3.91]*** [3.54]*** [0.33] [3.13]***

Teaching Fellows 0.274 -0.052 -0.350 -0.462 0.215

[2.50]** [-0.46] [-3.32]*** [-4.12]*** [1.91]*

Teach For America 0.604 0.810 -0.007 -0.561 0.272

[2.74]*** [3.65]*** [-0.03] [-2.48]** [1.22]

Other Path 0.004 0.230 0.371 0.320 0.436

[0.04] [1.87]* [3.31]*** [2.74]*** [3.73]***

N 558 528 543 541 551

* In addition to the pathway indicator variables each regression contains school context factors, which include a factor representing: teacher

influence on planning and teaching, administrative quality, staff collegiality and support, student attitudes and behavior, school facilities, and

school safety.

31

35.
Table 8: Math Immersion Programs: Key Course Requirements (Courses and Credits)

Campus Math Math Classroom Learning Assessment Special Diversity Technology Total Req’d

Course Methods Mgt Education Credits

Campus A 5 (15) 3 (9) 1 (3) 1 (3) 0 0 0 0 46-49

Campus B 5 (15) 3(9) 0 1(3) 0 0 0 0 48

Campus C 6 (18) 4 (12) 0 1(3) 0 0 0 0 47

Campus D 2 (6) 1 (6) 0 0 1(3) 1 (3) 0 1 (3) 39

+ 2 courses

(6 credits)

prior to

entering

program*

Campus Z 1 (3) 2 (6) 0 1 (3) 1 (3) 1 (3) 1 (3) 0 39

*Program does not pay for or provide for these two prior mathematics courses.

32

Campus Math Math Classroom Learning Assessment Special Diversity Technology Total Req’d

Course Methods Mgt Education Credits

Campus A 5 (15) 3 (9) 1 (3) 1 (3) 0 0 0 0 46-49

Campus B 5 (15) 3(9) 0 1(3) 0 0 0 0 48

Campus C 6 (18) 4 (12) 0 1(3) 0 0 0 0 47

Campus D 2 (6) 1 (6) 0 0 1(3) 1 (3) 0 1 (3) 39

+ 2 courses

(6 credits)

prior to

entering

program*

Campus Z 1 (3) 2 (6) 0 1 (3) 1 (3) 1 (3) 1 (3) 0 39

*Program does not pay for or provide for these two prior mathematics courses.

32