2016 Summer Seminar: Diversity in Education, June 13-16, 2016, University of Puerto Rico, Río Piedras.

1. Educational Policy and its Impact on
Teacher Preparation and Evaluation
in the United States and Puerto Rico
Summer Seminar
College of Education
UPR –Rio Pierdas
June 15, 2016
Peter M.Vigil, PhD
Associate Professor,Teacher Education
Metropolitan State University of Denver
pvigil7@msudenver.edu

2. Objetivos de la Sesión
— Los participantes desarrollarán una
comprensión de la “Every Student Succeeds
Act“ (ESSA)
— Los participantes serán capaces de identificar
diferencias importantes en las políticas
prescritas de ESSA en comparación con el
anterior “No Child Left Behind"
— Los participantes desarrollarán un
entendimiento sobre el impacto potencial de
ESSA en la preparación y la evaluación de
los maestros en los EE.UU. y Puerto Rico

3. Presentation Objectives:
• Par$cipants
will
develop
a
comprehensive
understanding
of
the
“Every
Student
Succeeds
Act”
(ESSA)
• Par$cipants
will
be
able
to
iden$fy
important
differences
in
the
prescribed
policies
of
ESSA
in
comparison
to
the
previous
“No-­‐Child
Le8
Behind
Act”
• Par$cipants
will
develop
an
understanding
on
the
poten$al
impact
of
ESSA
on
teacher
prepara$on
and
teacher
evalua$on
in
both
the
U.
S.
and
Puerto
Rico

4. “Every Student Succeeds
Act” (ESSA)
— Firmado como ley Federal el 10 de Diciembre, 2015
— Reautorización of the “Elementary and Secondary
Education Act de 1965, replaces the No Child Left
Behind Act
— Policy: Standards,Assessments, and Accountability
— La plena aplicación de ESSA en el año escolar
2017-2018
— La elaboración de normas y el proceso de orientación
requiere la participación de las partes interesadas, con
el desarrollo de un plan estatal para la implementación
de políticas y programas

5. The “Elementary and Secondary Education
Act” (ESEA) de 1965: un péndulo oscilante
— ESSA es la versión más reciente de la ESEA firmado originalmente por
Presidente L. B. Johnson en 1965
— “War on Poverty” – Movimeinto de derechos civiles
— Title I, Title III, Title IV
— 1970’s - strict federal rules and regulations guaranteed that funds would
be allocated solely to students in need – specifically students eligible for
services based on socioeconomic status and academic achievement
— 1980’s - During
the
Reagan
Administra$on,
Congress
passed
the
Educa$on
Consolida$on
and
Improvement
Act
(ECIA)
in
1981
to
reduce
federal
regula$ons
of
Title
I.
— 1990’s
-­‐
1994
Improving
America’s
Schools
Act
(IASA)
–added
math
and
language
arts
standards
for
accountability
— 2000’s
-­‐
NCLB
required
increased
accountability
from
schools
both
from
the
teachers
and
from
the
students

6. Every Student Succeeds Act (ESSA):
“Power to the States”
— Mucho menos restrictivo que la ley NCLB
— Proporciona un mayor control sustancial en el nivel estatal
y local
— ESEA da a los estados y las escuelas más autoridad y
libertad para innovar
— Limita la autoridad de la Secretaría de Educación y el
Gobierno Federal
— Mantiene un enfoque en la rendición de cuentas de los
distritos y las escuelas

7. Every Student Succeeds Act (ESSA):
Standards
— Requiere rigurosos estándares estatales en
matemáticas, lectura / lenguaje y la ciencia
— Normas alternativas para los estudiantes con
discapacidades cognitivas significativas
— Standards for English-language proficiency
— Estados no están obligados a utilizar los “Common
Core” standards

8. Every Student Succeeds Act (ESSA):
Evaluacion (Assessment)
— Las evaluaciones necesitan alinear con las normas (standards)
del Estado
— Matemáticas y lengua y artes Inglés en los grados 3-8 y una vez
en la escuela secundaria (Elementary, Middle, High-school)
— La ciencia cada vez en la escuela primaria, secundaria y
preparatoria
— Los estados pueden administrar evaluaciones como prueba
única o como un conjunto de pruebas provisionales que se
pueden enrollar en un único resultado anual para cada
estudiante

9. Every Student Succeeds Act (ESSA):
State Accountability
— Achievement on State Tests (overall & disaggregated)
— Growth on State Tests (overall & disaggregated)
— Graduation Rates (overall & disaggregated)
— English Language Proficiency of ELLs
— Other School Quality or Student Success Indicator:
◦ Student Engagement; school climate; community engagement;
student access to advanced (AP classes)
— 95% Participation Rate:
◦ States will determine opt-out policies for state tests

10. Every Student Succeeds Act (ESSA):
School Accountability
— State is required to identify “Comprehensive Support
Schools” once every three (3) years
◦ Lowest performing 5% of Title I Schools
◦ High schools with graduation rate below 67%
— States notify districts of any comprehensive support
schools
— In partnership with stakeholders, districts must develop and
implement a comprehensive support and improvement plan
for each school
— If a school does not meet the State’s exit criteria, it must
implement more rigorous interventions, determined by the
State

11. Every Student Succeeds Act (ESSA):
English Language Learners
— Los Estados deben elaborar normas (standards) para los estudiantes
ELL que reflejan las habilidades de lenguaje y prácticas los ELLs deben
participar con éxito en los estándares académicos
— Los Estados deben administrar e informar sobre el rendimiento
escolar anuales basados en estándares evaluaciones de dominio del
idioma Inglés para los ELLs
— Los Estados deben elaborar “exit-criteria” para los estudiantes ELL
— Los estudiantes clasificados anteriormente como ELL deben ser
incluidos en la presentación de informes y la rendición de cuentas,
por un período de 4 años después de que hayan sido reclasificados.

12. Every Student Succeeds Act (ESSA):
English Language Learners
— Se ofrecen dos opciones para evaluar, informar, e incluir en la
rendición de cuentas los ELLs recién llegados:
1. Los Estados pueden excluir los ELLs recientemente llegaron de
una administración de la evaluación de lectura / artes del lenguaje
y evaluar e incorporar estos resultados de las pruebas de ELLs
después de que hayan sido inscritos en una escuela de EE.UU.
durante un año, como fue el caso en virtud de la ley NCLB
2. La segunda opción requiere que los estados ponen a prueba los
ELLs recién llegados y informe de resultados para los dos
primeros años, pero permite a los Estados excluir los resultados
de estos estudiantes desde el sistema de responsabilidad del
estado en el primer año, incluir una medida de estudiante el
crecimiento en el segundo año, e incluir el dominio en las
evaluaciones académicas a partir de tercer año del estudiante en
una escuela de EE.UU.

13. Every Student Succeeds Act (ESSA):
Quality Instruction and Teacher Evaluation
— ESSA eliminates “highly qualified teacher” requirement
from No Child Left Behind: States
must
report
data
on
whether
low-­‐income
and
minority
students
are
being
served
at
dispropor$onate
rates
by
“ineffec<ve,
out-­‐of-­‐
field,
or
inexperienced
teacher”
— ESSA da a los estados la capacidad para definir la
definición de los siguientes::
◦ “Teacher Effectiveness”: Student scores are NOT required to be
factored into teacher evaluation
◦ Teacher Preparacion Performance standards. Content exams, etc.

14. Colorado:The state of the State
— Common Core Standards: Embedded into new Colorado
Academic Standards
— New Assessments for new Standards
◦ Transitional Colorado Assessment Program (TCAP)
◦ Partnership for Assessment of Readiness for College and
Careers (PARCC)
◦ ACCESS for ELLs (WIDA)
— SB 10-191 “Educator Effectiveness”
◦ 50% multiple measure of student learning
◦ 50% Professional Practice: Observation & PD
— “Student Progress” and “Teacher Effectiveness”
determined by ‘”Growth-model” system

15. Teacher Evaluation: Puerto Rico
— Departamento de Educaion de Puerto Rico
— http://www.de.gobierno.pr/

16. Value-added Models (VAM)
— Many states and school districts have adoptedValue-
Added Models (VAMs) as part of educational
accountability systems
— The goal of these models is to estimate effects of
individual teachers or schools on student achievement
while accounting for differences in student background.
— VAMs are increasingly promoted or mandated as a
component in high-stakes decisions such as determining
compensation, evaluating and ranking teachers, hiring or
dismissing teachers, awarding tenure, and closing schools.

17. Value-added Models (VAM)
— El objetivo de estos modelos es estimar los
efectos de los maestros o escuelas individuales en
el rendimiento estudiantil mientras cuenta las
diferencias en la formación del estudiante
— VAM (“Growth Model”) son promovidos como
un componente obligatorio en las decisiones de
altas apuestas, tales como la determinación de la
compensación, la evaluación y la clasificación de
los profesores, contratación o despido de los
maestros, la concesión de la tenencia, y el cierre
de las escuelas cada vez

18. VAM Statistical Methods
— Value-added models typically use a form of regression model
predicting student scores or growth on standardized tests from
background variables (including prior test scores), with terms in
the model for the teachers who have taught the student.
— The model coefficients for the teachers are used to calculate
theirVAM scores.
— In related models known as “growth models” a regression
model is fit to predict students’ current test scores from
previous test scores.
— A percentile is calculated for each student from the model,
relating his or her growth to the growth of other students with
similar previous test scores.
— The median or average of the percentiles of a teacher’s
students is then used to calculate the teacher’sVAM score.
(ASA, 2014)

19. VAM Promises
— VAM results are often regarded as more
objective or authoritative than other
types of information because they:
— Are based on student outcomes,
— Use complex quantitative statistical
models
— Rely on standardized test scores and
common procedures for all teachers or
schools.

20. VAM Problems Methodological
— Statistical Error Rate: there is a statistical error rate of 35 percent
when using one year’s worth of test data to measure a teacher’s
effectiveness, and an error rate of 25 percent when using data from
three years, researchers (Schochet, P. Z. & Chiang, H. S. 2010)
— Year-to-year Instability: value-added rankings of teachers fluctuate
wildly from year to year (ASA 2014)
— Non-Random Student Assignments
— “The assumptions required to obtain commonly estimated models and derive
econometric tests of those assumptions are easily rejected.”
(Sass, et.al.2013, 2014 )

21. VAM Problems Logical
— VAMs typically measure correlation, not causation: Effects – positive or
negative – attributed to a teacher may actually be caused by other
factors that are not captured in the model (ASA 2014)
— The use of VAMs assumes “that student learning is measured by a given
test, is influenced by the teacher alone, and is independent from the
growth of classmates and other aspects of the classroom context.
(Darling, Hammond, L. 2012)
— MostVAM studies find that teachers account for about 1% to 14% of the
variability in test scores, and that the majority of opportunities for quality
improvement are found in the system-level conditions.
(ASA 2014)
— Fifty to 80 percent of any improvement or decline in a student’s
standardized test scores can be attributed to one-time, randomly
occurring factors (Kane & Staiger, 2002)
— Model assumes/ attributes growth to individual teacher
(homeroom/content)

22. Schoolview Data & Accountability
Colorado Department of Education
— http://www.cde.state.co.us/schoolview/
coloradogrowthmodel
— https://edx.cde.state.co.us/SchoolView/
DataCenter/reports.jspx?
_afrWindowMode=0&_afrLoop=2356270
37710306&_adf.ctrl-state=zn8fh9jdq_4

23. Colorado Growth Model Ratings 2014: Reading
School
Rating
% at/ above
Proficient
Growth
Percentile
“White”
“Economically
Disadvantaged”
“ELL”
Students
w/
Disabilities
1
99
61
80 %
3 %
4 %
4 %
2
97
58
83 %
10 %
9 %
4%
3
95
79
78 %
10 %
10 %
3 %
4
92
51
83 %
8 %
4 %
7 %
5
92
64
78 %
8 %
8 %
8 %
6
90
79
74 %
6 %
5 %
6 %
7
89
66
68 %
30 %
15 %
8 %
8
89
65
85 %
13 %
4 %
7 %
9
88
55
70 %
16 %
5 %
7 %
10
88
56
56 %
30 %
5 %
3 %

24. Colorado Growth Model Ratings 2014: Reading
School
Rating
% at/ above
Proficient
Growth
Percentile
“White”
“Economically
Disadvantaged”
ELL
“Students
w/
Disabilities”
100
48
70
2 %
97 %
77 %
15 %
101
35
36
1 %
96 %
48 %
9 %
102
34
51
5 %
98 %
41 %
12 %
103
34
39
4 %
95 %
48 %
10 %
104
32
47
12 %
83 %
39 %
8 %
105
30
36
3 %
97 %
36 %
20 %
106
29
35
3 %
98 %
59 %
12 %
107
28
47
3 %
97 %
67 %
10 %
108
25
41
4 %
95 %
48 %
12 %
109
25
42
1 %
95 %
60 %
8 %

25. Colorado Growth Model Ratings 2014 : Math
School
Rating
% at/ above
Proficient
Growth
Percentile
“White”
“Economically
Disadvantaged”
“ELL”
Students
w/
Disabilities
1
96
82
78 %
10 %
10 %
3 %
2
95
71
83 %
10 %
9 %
4 %
3
93
61
83 %
8 %
4 %
7 %
4
92
53
78 %
8 %
8 %
8 %
5
91
57
85 %
13 %
4 %
7 %
6
90
76
74 %
6 %
5 %
6 %
7
89
63
70 %
16 %
5 %
7 %
8
87
59
70 %
15 %
9 %
8 %
9
87
52
68 %
30 %
15 %
8 %
10
86
53
56 %
30 %
5 %
3 %

26. Colorado Growth Model Ratings 2014: Math
School
Rating
% at/ above
Proficient
Growth
Percentile
“White”
“Economically
Disadvantaged”
ELL
Students
w/ Disabilities
100
41
48
4 %
95 %
64 %
9 %
101
38
40
22 %
78 %
17 %
14 %
102
37
45
3 %
97 %
87 %
10 %
103
37
43
4 %
95 %
48 %
10 %
104
34
36
3 %
97 %
36 %
20 %
105
33
40
12 %
83 %
39 %
8 %
106
33
47
12 %
83 %
39 %
8 %
107
32
41
2 %
97 %
77 %
15 %
108
31
39
2 %
98 %
59 %
12 %
109
24
34
4 %
95 %
48 %
12 %

27. Tentative Timeline to implement ESEA
(Colorado)
— Enero- Abril (2016): Interpretacion de ley
— Mayo-Junio (2016): “Listening Tour”
— Julio – Sept. (2016): Escribiendo el Plan
— Sept. (2016) - Enero (2017): Modifcar el plan
— Feb. – Abril (2017): Aprobar el plan
— Implementar ESSA 2017-2018 año escolar
— We are working hard in Colorado to prepare for
2017-2018 implementation of ESSA
— ESSA addresses the authorization/ requirements NOT
the appropriations (e.g. funding levels) for various
educational programs

28. References
— American Statistical Association (2014). ASA statement on using value-added models for
educational assessment. Retrieved from:
https://www.amstat.org/policy/pdfs/ASA_VAM_Statement.pdf
— Au,W. (2011). Neither fair nor accurate: Research-based reasons why high-stakes tests
should not be used to evaluateachers. Rethinking Schools.
— Darling-Hammond, L.,Amrein-Beardsley,A., Haertel, E., & Rothstein, J. (2012). Evaluating
teacher evaluation. Phi Delta Kappan, 93(6), 8-15. Retrieved from:
http://www.kappanmagazine.org/content/93/6/8.full.pdf+html
— Kane, J.T., & Staiger , D.O. (2002.Volatility in School Test Scores: Implications for Test-
Based Accountability Systems. Brookings Papers on Education Policy. Retrieved from:
http://www.dartmouth.edu/~dstaiger/Papers/KaneStaiger_brookings2002.pdf
— Sass,Tim R.; Semykina,Anastasia; Harris, Douglas N. (2014).Value-added models and the
measure of teacher productivity Economics of Education Review,.Vol. 38, p9-23.
— Schochet, P. Z. & Chiang, H. S. (2010). Error rates in measuring teacher and school
performance based on student test score gains.Washington DC: U.S. Department of
Education. Retrieved from: http://ies.ed.gov/ncee/pubs/20104004/
— Colorado Department of Education; http://www.cde.state.co.us/

29. Recursos
— Read the Every Student Succeeds Act
https://www.cde.state.co.us/fedprograms/essapdf
— US Department of Education
http://www.ed.gov/ESSA
— Transitioning to the “Every Student Succeeds Act”
Frequently asked questions:
http://www2.ed.gov/policy/elsec/leg/essa/faq/
essatransitionfaqs050316.pdf