Don Boyd, Pam Grossman, Karen Hammerness, Hamp Lankford, Susanna Loeb, Matt Ronfeldt & Jim Wyckoff www.teacherpolicyresearch.org This work is supported by IES Grant R305E6025. The views expressed may not reflect those of the funder. Recruiting Effective Math Teachers, How Do Math Immersion Teachers Compare?: Evidence from New York City
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Don Boyd, Pam Grossman, Karen Hammerness, Hamp Lankford, Susanna Loeb, Matt Ronfeldt & Jim Wyckoff
www.teacherpolicyresearch.org
This work is supported by IES Grant R305E6025. The views expressed may not reflect those of the funder.
Recruiting Effective Math Teachers, How Do Math Immersion Teachers
New Math Certified Teachers Hired in New York City, by Pathway, 2002-2008
0
100
200
300
400
500
2002 2003 2004 2005 2006 2007 2008
CR
NYCTF-MI
NYCTF
TFA
Research Questions How does the preparation of Math Immersion
teachers compare to math teachers entering through other pathways?
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?
Achievement effects of alternate route teachers comparable to traditional preparation programs on average (Decker et al., 2004 (RCT); Boyd et al., 2006; Kane et al., 2007; Harris and Sass, 2008; Constantine et al., 2009 (RCT))
TFA in NC high schools exceeds other paths (Xu et al., 2007)
More limited work on aspects of preparation that may make a difference (Constantine et al., 2009; Boyd et al., 2009 and Harris and Sass, 2007)
Prior Research
Data Collection Program analysis
– State documents, program documents, accreditation reports, interviews, surveys, course syllabi;
– 5 Math Immersion programs,18 institutions, and TFA that prepare most traditional route teachers for NYC schools
Surveys – 603 new NYC middle and high school math teachers (2005)– Questions about their preparation in math– e.g, opportunities to
learn math content, math methods, etc.
Administrative data – All NYC teachers 2004-2008; rich measures of teacher qualifications,
including certification exams and areas, teacher retention. – Student achievement 2004-2008; value-added scores in math and
ELA, grades 6-8 linked to teachers. – Data on schools and students
Effect of Preparation PathwaysGeneral specificationAigcst=0+1Aig-1cst-1+Xigcst2+Cgcst3+Tgcst4+ Pgcst5 + s + igcst
Achievement as a function of: • prior achievement, • student characteristics• classroom characteristics • teacher characteristics (sometimes)• Preparation pathway (e.g., math immersion)• Student or school fixed-effects• random error
Attributes of Students Taught by First Year Grade 8 Math Teachers by Pathway, 2006
Student Attributes CR NYCTF NYCTF-MI
TFA other
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
Attributes of Entering Math Certified New York City Teachers by Pathway, 2004-2008
Simulation of Average Value Added by Pathway and Experience Accounting for Attrition
Simulation Average Value Added
Year NYCTF-MI CR NYCTF TFA
1 0.000 0.018 0.011 0.054
2 0.045 0.068 0.053 0.103
3 0.066 0.086 0.072 0.086
4 0.052 0.081 0.088 0.088
Value Added by Pathway and Experience
Experience NYCTF-MI CR NYCTF TFA
1st year 0.000 0.018 0.011 0.054
2nd year 0.051 0.075 0.061 0.107
3rd year 0.085 0.095 0.090 0.126
4th year 0.063 0.091 0.128 0.111
Conclusions MI teachers have about the same value-added as
College Recommended teachers Driven largely by selection, TFA performs much
better than either College Rec or Math Immersion Some evidence that both selection and preparation
make a difference
Hypothesis: selective post BA program with tailored coursework that includes content and high quality field experience can meaningfully improve student achievement
For papers and surveys:
www.teacherpolicyresearch.org
Pathways to Teaching in NYC, New Teachers, 2002-08
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
2002 2003 2004 2005 2006 2007 2008
CR
NYCTF
TFA
TL
Outline Research questions Data and methods Math preparation in Math Immersion and
College Recommending programs Achievement gains by pathway Retention by pathway Summing up
explore how to apply mathematical materials to real world problems
learn specific techniques for teaching Algebra (Geometry, Number Theory, Probability and Statistics, Calculus)
learn about typical difficulties students have with Algebra (Geometry, Calculus)
study or analyze student math work
study examples o secondary mathematics teaching in the form of videotapes, written cases, etc.
Practice what you learned about teaching math in your field experience
etc.
Survey of 1st year NYC Teachers—Middle and High School Math
Teachers' Perceptions of Preparation by Pathways Relative to NYCTF-MI, (2005 Survey of 1st Year Teachers)
Pathway
Preparation in Specific
Strategies
General Opps to Learn Teaching
Math
Subject Matter Prep in
MathCollege Recommending 0.331 0.386 0.038
[2.99]*** [3.54]*** [0.33]
Teaching Fellows 0.274 -0.350 -0.462
[2.50]** [-3.32]*** [-4.12]***
Teach For America 0.604 -0.007 -0.561
[2.74]*** [-0.03] [-2.48]**
Other Path 0.004 0.371 0.320
[0.04] [3.31]*** [2.74]***
N 558 543 541
Estimated Value Added Model*Student Measures Class Average Measures Experience Lag score 0.593 Hispanic -0.161 2nd year 0.050 [269.33]** [6.81]** [8.92]**Lag score sqrd -0.005 Black -0.152 3rd year 0.082 [3.70]** [6.11]** [12.70]**Female 0.010 Asian 0.099 4th year 0.091 [6.58]** [3.71]** [12.22]**Asian 0.126 Class size 0.000 5th year 0.100 [35.45]** [0.85] [12.64]**Hispanic -0.059 English home -0.026 6th year 0.096 [19.07]** [1.48] [11.01]**Black -0.060 Free lunch 0.014
[18.21]** [1.57] Pathways
Change school -0.078 Lagged absent -0.007 Coll. Recomm. 0.016 [16.22]** [13.30]** [1.86]English home -0.060 Lag suspended -0.002 NYCTF 0.021 [31.51]** [0.15] [1.87]Free Lunch -0.017 Lag ELA score 0.194 TFA 0.055 [10.46]** [24.73]** [3.71]**Lagged absent -0.005 Lag Math score 0.076 Other -0.011 [64.92]** [9.16]** [1.27]Lag suspended -0.024 Std Dev ELA score 0.043 [12.20]** [4.78]** N 651191
* Also includes student and class ELL status, std dev class math score, indicators for experience through 21 years, year and grade effects
Challenges of this type of analysis
Conceptualizing relationships Research designs Collecting appropriate data
Achievement tests, tested grades, subjects Strong controls from administrative data Other data about teachers
Legal/political Privacy Concerns about misuse
Technical/modeling Models that isolate contribution of teacher attributes