Uncategorized – Page 2 – Cities, Suburbs & Schools Project at Trinity College

Still Separate and Still Unequal: Understanding Racial Segregation in Connecticut Schools

May 12, 2015May 12, 2015Alix Liss

Forty thousand students in Connecticut are enrolled in failing schools. Eighty-seven percent of those students are Black or Hispanic, and ninety percent are low-income. ¹ The historic Brown v. Board of Ed. case (1954) reaches its’ sixty first anniversary this year; it is difficult to realize many students are still segregated by socioeconomic class and race. Much of this segregation is evident in choice based schools, institutions geared toward providing educational opportunity to less advantaged students. ² With an obligation to enroll 25-75% minority students, magnet schools are working to provide better education yet struggling to successfully integrate their classrooms. In this web essay, I aim to first understand how racial discrimination began in Connecticut schools and how it endures. Second, I will examine why classroom integration is important. Third, I will question whether choice based magnet schools, institutions initially created in an effort to provide greater opportunity to those who have less, are the best form of schooling to eradicate school-based segregation. Last, I will work to understand what the most effective solutions to racial segregation might be.

Why does racial segregation exist, and how can it be explained in schools in Hartford, CT?

Today, Connecticut is populated primarily by white, middle to upper class educated individuals. Some 81.6% of the population is white, with 11.3% Black and 14.7% Latino. For the years between 2009-2013, on average, 89.2% of residents across the state graduated from high school. Some 36.5% graduated from some form of secondary college education. ³ Even though Connecticut is white dominated, there are large pockets of minority individuals living throughout the state, especially in Bridgeport, Waterbury, New Haven, New Britain and East Hartford. Below is an interactive map application depicting the “racial breakdown of students since 1969, by district.” One can use the map to better understand school-based racial segregation throughout the state. ⁴

School-based segregation first began nationwide with Plessy v. Ferguson in 1896 and continued with Brown v. Board of Ed.’s (1954) poorly executed reparations. There are several ‘reasons’ for the ongoing segregation in Connecticut schools and in many others nationwide. Some of these explanations include proximity to schools, extracurricular activities, clubs, and religious temples, housing opportunities, white flight, gentrification and zoning. There are many causes; in this essay, I will speak of a few. In their work entitled “Smarter Charter,” Richard Kahlenberg and Haley Potter write, “Parents of all incomes were likely to value a strong reading and math curriculum. Learning good study habits and self-discipline also…the overall ranking of preferences was fairly consistent across income brackets”. ⁵. Even with the commonality of education goals, many still prefer enrolling their school-aged children in institutions of learning where in terms of race, ethnicity and culture, the student body is like their own. ⁶ People are motivated to live in communities with others like themselves as well. These areas of condensed poverty and wealth, and therefore further segregation, are depicted in the interactive map of Hartford below. ⁷

As demonstrated through self-selection practices, information neighborhood parents receive when enrolling their children in schools and the lottery system, school choice is yet another reason for much of the existing school-based segregation (131). ⁸ Many choice-based institutions target varied cultural and ethnic groups. ⁹ When they do this, certain racial groups have a stronger presence in the enrollment lotteries than others. The resulting grades and classes within schools become homogeneous. This has certainly been the case at Capital Preparatory Magnet School in Hartford, CT, which was “originally created to give students living in high poverty areas the chance to receive exceptional education in areas where it’s most needed.” ¹⁰

In 1980, Connecticut instituted the Racial Imbalance Law, requiring schools to report their racial make-up to the state. This information included proximity of home from schools and how many students require free or reduced price lunch. Schools were forced to communicate with their districts when the number of minority students was not aligned with that of the majority race students. ¹¹ This law failed, and so has the state government in eradicating school-based segregation. The law did however opened up a dialogue that had not been previously discussed; that conversation is centered heavily on integration, its importance and how it can change the future of education today. ¹²

Understanding Integration and its importance in Connecticut schools:

In 1989, Elizabeth Horton Sheff began a crusade against Connecticut on behalf of minority students in the state, demonstrating Black and Latino schools in urban areas were less privileged than those of the white suburban schools. In 1996, district lines were deemed unconstitutional and within the following year, “An Act Enhancing Educational Choices and Opportunities” was passed; it encouraged racial integration through school choice. In 2008, a greater number of spots for students from all areas and racial backgrounds were to be made available in magnet schools. ¹³ ¹⁴

As Sheff has helped to demonstrate, there are many benefits to racial integration in schools. Some include curtailing racism, providing a forum for mixed race friendships and increasing school-wide test scores in lesser performing schools. In her article entitled “What Will you Think of Me? Racial Integration, Peer Relationships and Achievement Among White Students and Students of Color,” Sabrina Zirkel writes, “Desegregated schools do produce more successful educational and professional outcomes for students of color. They do reduce prejudice and increase racial integration in the larger society. (58-59).” ¹⁵ Marguerite Spencer, a Professor at University of St. Thomas, finds children studying in integrated schools have “[a] higher level of parental involvement, higher graduation rates, complete more years of education, earn higher degrees and major in more varied disciplines, gain greater access to professional jobs and have higher incomes ¹⁶.

There are a surprising number of individuals disinterested and unwilling to send their children to integrated schools. In fact, the American Recovery and Reinvestment Act perpetuates school-based segregation, giving “upward of $70 billion to continue to reinforce patterns of racial and economic separation in American Schools.” ¹⁷ This is evidence racism is more alive than ever.

Are choice based schools the solution to limited diversity in schools?

Over the last decade, Connecticut state government has spent $1.4 billion on repairing those schools that are dilapidated and building new magnet schools. $140 million is spent on school upkeep each year. ¹⁸ One of the state’s goals has been to provide opportunities for 41% of Hartford’s minority students to enroll in integrated schools. Magnet schools initially seemed like the most likely approach. In the 2010-2011 school year, there were 2,722 magnet schools operating nationwide with 2,055,133 students enrolled. Fifty-four of those were in Connecticut. Today, there are seventy magnet schools in Connecticut serving 31,689 students, 70% of which are of minorities, mostly Black and Hispanic. ¹⁹

Hartford Magnet Middle School — Source: Trinity College

Magnet schools are often formed with a focused mission like STEM, Fine and Performing Arts, International Baccalaureate and Career and Technical Education. The curriculums are created in this manner in order to provide direction while also building family involvement. ²⁰ ²¹ These specialized programs were also created in an effort to attract white students to minority-majority schools, establishing a trend toward integration. Instead, as Boston University Professor Christine Rossell writes in her dissertation entitled “The Desegregation Efficiency of Magnet Schools,” “One possible explanation for why magnet schools [do] not have a more salutary effect on interracial exposure in the voluntary desegregation plans is that they may produce some white flight of their own.” (12) ²² “White flight” speaks to the racism many white people maintain and their resulting move to areas in which they make up the majority population (47) ²³ Choice schools were created to provide opportunity to varied racial groups to converge to learn together, but classrooms in these institutions, especially in magnets, are still homogeneous. In order to understand why this is, an analysis of their assessment processes must be done.

Connecticut Magnet Schools are evaluated through a series of state designed questions each year. The first is “What characteristics define inter-district magnet schools and how do inter-district magnet schools differ from other public schools?” The second is, “what impact have inter-district magnet schools had on reducing the racial, ethnic, and economic isolation of Connecticut students?” The third is, “what impact have inter-district magnet schools had on reducing the racial, ethnic, and economic isolation of students within the magnet school itself?” The fourth is “How does the performance of inter-district magnet school students compare with that of public school students state-wide?” And the final two are “how consistent are students, parents, and their public school professional staff in their perception of the effectiveness of their magnet schools, and what characteristics do the highly successful magnet school share?” (3-5) ²⁴. With these questions, magnet schools are clearly evaluated each year but their assesments are lacking. The questions themselves are fairly vague, keeping researchers from examining specific aspects of individual schools. Each inter-district magnet school does not maintain the same characteristics or the same students. Therefore, one school cannot be evaluated in the same exact way as another. Magnet schools are indeed effective in providing more opportunity than minority students would have previously had. They do not, however, provide the optimal education option of an integrated learning environment, especially when it comes to those students who are white or living with special needs. ²⁵

What are the solutions to school-based segregation?

Since Brown v. Board of Ed., school-based segregation has become a larger problem than ever before. Much of this is due to the federal government’s limited implementation of relevant policy. No Child Left Behind was one reformation initiating efforts; testing alone though cannot close the achievement gap. Instead, as Gary Orfield suggests, districts need to first look at the factors outside schools that affect children in order to best understand why some schools have the demographics they do. Some of these include housing, day care, wages and health care. A parent’s ability to pay for housing in a middle to high income school district can dictate a child’s potential to enroll in a school with high levels of student success. Teachers must be provided with cultural sensitivity training so that they are equipped to instruct individuals of all backgrounds. Orfield also suggests universal pre-k, smaller class sizes, counseling and early grade reading classes will not only help to desegregate but will also help to close the achievement gap. ²⁶ ²⁷ No one kind of institution can solve the conflict of racial segregation in schools. As Rod Dreher suggests, the US and Connecticut governments must make integration a priority, demanding a higher quality of teaching and a greater number of after school programs. Within the next ten years, white students will be in the minority. It is time individuals begin learning together with varied races now. ²⁸

Conclusion:

With limited school-based integration, goals for institutions of learning to “promote an American identity, social cohesion and democratic leadership” are further out of reach than ever before. ²⁹

Racial segregation can be an enormous impediment to learning, especially for low income minority students. Segregation encourages stereotypes, increases racism and widens the achievement gap by limiting access to quality education for Blacks and hispanics. Integration can help minorities more than it can the already racially privileged white students. It can assist in the efforts to raise scores on standardized tests, reduce violence, encourage parent involvement, create greater access to professional jobs and guarantee higher incomes. Integration and exposure to a variety of cultures and ethnicities can also help all students to eradicate racism and experience a life with greater perspective. ³⁰

Choice-based institutions, like magnet schools, are meant to be building universal change. They are creating more opportunity for minority students, but not together with their majority peers. In an effort to build an integrated education system for the future, we must first understand how racial segregation began in Connecticut, how it endures, why integration is important and what the best solutions are. If choice-based schools have not been an effective form of segregation eradication, can we truly think of them as the future of our multi-cultural education system? I don’t think we can. The United States and Connecticut governments must make integration a top priority, creating a system of change that includes a higher standard for teaching and a greater availability of before and after school enrichment programs to foster equal access for all students, no matter what race, together.

Notes:

2014. Connecticut Education in Crisis: 40,000 Children Trapped in Failing Schools. connCAN. Retrieved May 11, 2015. (http://www.conncan.org/media-room/press-releases/2014-11-connecticut-education-in-crisis-40000-children-trapp) ↩
Frankenberg, E., Siegel-Hawley, G., & Wang, J. 2010. Choice without Equity: Charter School Segregation and the Need for Civil Rights Standards. Civil Rights Project/Proyecto Derechos Civiles. Retrieved April 30, 2015. (http://civilrightsproject.ucla.edu/research/k-12-education/integration-and-diversity/choice-without-equity-2009-report/) ↩
2015. State and County QuickFacts: Connecticut. United States Census Bureau. Retrieved April 30, 2015.(http://quickfacts.census.gov/qfd/states/09000.html) ↩
(Thomas, Jacqueline Rabe. 2014. 60 Years after Brown vs. Board of Education: Still Separate in Connecticut. The CT Mirror. Retrieved May 1, 2015. (http://ctmirror.org/2014/05/16/60-years-after-brown-vs-board-of-education-still-separate-in-connecticut/). ↩
Kahlenberg, Richard D and Potter, Haley. 2014. A Smarter Charter: Finding what Works for Charter Schools and Public Education. New York, NY: Teachers College Press ↩
Kahlenberg, Richard D and Potter, Haley. 2014. A Smarter Charter: Finding what Works for Charter Schools and Public Education. New York, NY: Teachers College Press ↩
(Hartford, Connecticut (CT) Poverty Rate Data. City-Data.com. Advameg Inc. Retrieved May 10, 2015. (http://www.city-data.com/poverty/poverty-Hartford-Connecticut.html#mapOSM?) ↩
Orfield, Gary and Ee, Jongyeon. 2015. Connecticut School Integration: Moving Forward as the Northeast Retreats. UCLA The Civil Rights Project 5. Retrieved April 29, 2015. (http://civilrightsproject.ucla.edu/research/k-12-education/integration-and-diversity/connecticut-school-integration-moving-forward-as-the-northeast-retreats/orfield-ee-connecticut-school-integration-2015.pdf) ↩
Kahlenberg, Richard D and Potter, Haley. 2014. A Smarter Charter: Finding what Works for Charter Schools and Public Education. New York, NY: Teachers College Press ↩
2011. Are Magnet Schools Perpetuating Segregation? The Huffington Post. Retrieved May 11, 2015. (http://www.huffingtonpost.com/2011/06/01/school-segregation_n_860857.html). ↩
Lohman, Judith. 2010. The Racial Imbalance Law. OLR Research Report. Retrieved May 1, 2015 (http://www.cga.ct.gov/2010/rpt/2010-R-0228.htm) ↩
(Orfield, Gary and Ee, Jongyeon. 2015. Connecticut School Integration: Moving Forward as the Northeast Retreats. UCLA The Civil Rights Project 5. Retrieved April 29, 2015. (http://civilrightsproject.ucla.edu/research/k-12-education/integration-and-diversity/connecticut-school-integration-moving-forward-as-the-northeast-retreats/orfield-ee-connecticut-school-integration-2015.pdf) ↩
2014. History of Sheff v. O’Neill. Sheff Movement: Quality Integrated Education for All Children. Retrieved May 1, 2015. (http://www.sheffmovement.org/history-2/) ↩
2014. Sheff v. O’Neill. Wikipedia: The Free Encyclopedia. Retrieved May 1, 2015. (http://en.wikipedia.org/wiki/Sheff_v._O’Neill) ↩
Zirkel, Sabrina. 2004. What Will You Think of Me? Racial Integration, Peer Relationships and Achievement Among White Students and Students of Color. Mills College: Saybrook Graduate School and Research Center. Journal of Social Issues, Vol. 60, No. 1, 57-74 ↩
(Spencer, Marguerite. 2009. The Benefits of Racial and Economic Integration in Our Education System:Why this Matters for Our Democracy. The Kirwin Institute. 1-19.) ↩
(Tegeler, Philip, Mickelson, Roslyn Arlin and Bottia, Martha. 2011. “Research Brief No. 4: What we know about school integration, college attendance, and the reduction of poverty.” Poverty and Race Research Action Council. The National Coalition on Student Diversity 1-4.”) ↩
Thomas, Jacqueline Rabe. 2014. 60 Years after Brown vs. Board of Education: Still Separate in Connecticut. The CT Mirror. Retrieved May 1, 2015. (http://ctmirror.org/2014/05/16/60-years-after-brown-vs-board-of-education-still-separate-in-connecticut/) ↩
2015. Connecticut Magnet Schools. Public School Review. Retrieved May 11, 2015. (http://www.publicschoolreview.com/state_magnets/stateid/CT) ↩
Chen, Grace. 2015. What is a Magnet School? Public School Review. Retrieved May 3, 2015. (http://www.publicschoolreview.com/blog/what-is-a-magnet-school) ↩
2013. What are Magnet Schools? Magnet Schools of America. Retrieved April 29, 2015. (http://www.magnet.edu/about/what-are-magnet-schools) ↩
Rossell, Christine. 2003. “The Desegregation Efficiency of Magnet Schools.” Boston University Political Science Department. Urban Affairs Review 1-25) ↩
Kahlenberg, Richard D and Potter, Haley. “Charter Schools” Can Racial and Socioeconomic Integration Promote Better Outcomes for Students? Poverty and Race Research Action, The Century Foundation. ↩
Beaudin, Barbara Q. Interdistrict Magnet Schools in Connecticut. Connecticut State Department of Education Division of Evaluation and Research. Retrieved May 2, 2015. (http://www.sde.ct.gov/sde/lib/sde/PDF/Equity/magnet/magnet_presentation_caims_11_2003.pdf) ↩
Thomas, Jacqueline Rabe. 2014. Report: Many Connecticut charter schools ‘hyper-segregated’. The CT Mirror. Retrieved May 2, 2015. (http://ctmirror.org/2014/04/09/school-choice-many-schools-hyper-segregated/) ↩
Orfield, Gary. 2015. Race and Schools: The Need for Action. National Education Association. Retrieved May 11, 2015. (https://www.nea.org/home/13054.htm_) ↩
(2015. School Desegregation and Equal Education Opportunity. The Leadership Conference Civil Rights 101. Retrieved May 11, 2015. (http://www.civilrights.org/resources/civilrights101/desegregation.html)) ↩
Dreher, Rod. 2013. Can We Ever ‘Fix’ Segregated Schools? The American Conservative. Retrieved May 11, 2015. (http://www.theamericanconservative.com/dreher/can-we-ever-fix-segregated-schools/) ↩
Kahlenberg, Richard D and Potter, Haley. 2014. A Smarter Charter: Finding what Works for Charter Schools and Public Education. New York, NY: Teachers College Press ↩
Thomas, Jacqueline Rabe. 2015. School choice: Future of new magnet schools uncertain. The CT Mirror. Retrieved May 3, 2015. (http://ctmirror.org/2015/01/06/school-choice-future-of-new-magnet-schools-uncertain/) ↩

Technical Document

April 29, 2015May 3, 2015Leib Sutcher

Data:
The data set used in this paper is the High School Longitudinal Study of 2009 (HSLS:09). This data is a longitudinal national survey that follows individual high school freshman through school and on to further educational pursuits and/or the work force. The sample was comprised of 944 schools, where administrative and support staff, over 23,000 students and their parents, and one math and one science teacher for each student were questioned. The schools were selected first (randomly), then 9th graders were randomly selected within those schools. Students were first surveyed in the fall of 2009, the base year, by administering cognitive math and science tests, logging experiences, and recording aspirations. The survey followed a multilevel model, which collects information by questionnaire from multiple sources such as students, their parents, their teachers, their librarians, and their schools. Two and a half years later (11th grade), in spring 2012, the same students were re-tested, and a new round of information was collected. A short round of data collection focused on transcripts and college planning happened when these students graduated in the spring of 2013, however this data is not yet available. The Fourth data collection is scheduled to take place in 2016, which will collect information about postgraduate trajectories, earnings and employment information, postsecondary education outcomes, and lots more. The study culminates in 2021 where these same students are interviewed about transitions to the labor force, education attainment, and future plans (NCES, 2009).

Model:
This project suggests, using the existing education literature, that the true underlying high school math achievement is modeled as follows. We represent math achievement, Ai, by students i ‘s school type (choice school or not), ʗi , family background, ɲi ,student motivation, Ƴi, parent characteristics and support, Բi , and teacher quality, Ți . The hypothesized underlying achievement function can be expressed as:

Ai =Ɓ0 +Ɓ1(ʗi)+Ɓ2(ɲi)+Ɓ3(Ƴi)+Ɓ4( Բi)+Ɓ5(Ți)+Ɛi (1)

where ,Ɛ, is mean zero normally distributed error.

It is important to note here that this model is capturing aspects about the student themselves, their school, and arguably most importantly their home life. In the true model these vectors would be filled with variables containing accurate data. However in reality many of these desired variables are unobserved or not measured. This leaves us with the option of omitting unobtainable variables resulting in substantial bias, or measuring what we can and using proxies for the rest. If our proxies are good we can obtain unbiased and consistent estimates. This project fills each one of these vectors with variables keeping in mind this goal.

Dependent variable
Math scores were tested on two occasions, first in the base year of 2009 and again two and half years later in 2012. The test administered uses what is known as a math item response theory (IRT). IRT rates the difficulty for each item by comparing the likelihood of students correctly getting some items against others. After the difficulty of all the items has been set, the ability of each student is estimated even if the actual assessments are different (NCES 2009). This allows us to compare across different types of tests. These scores are not integers because in the IRT calculation the probability of a correct item is given, instead of simply just counting right and wrong answers.

The dependent variables is mathgain. mathgain is the sample member’s gain (or loss) in their math IRT estimated right scores between the base year (9th grade) and the first follow up (11th grade). This measurement of test score gain is critical in controlling for innate cognitive ability. Differencing creates test score gains. Therefore ability is controlled for because it was the students own past test score subtracted from their current score, essentially subtracting out innate ability for all students. This is possible because innate ability is time invariant, so by differencing the test scores we clean out the inherent ability each student has. This is a crucial step for most education analysis, because omitted variables correlated with independent variables can cause substantial bias in our estimates. Using test score gains also allow us to move past the limitations of using static cross sectional data and take advantage of the longitudinal data structure.

Independent Variables
-choice school
The main focus of this project is examining the effect that attending a choice school (charter, magnet, or voucher program) has on student achievement. Whether or not a student attends a choice school is measurable. choice_school is a variable equal to one if the school the student attends participates in a public school choice program. School choice program is defined in the question as a magnet school, charter school, or school voucher program. This question is answered by a school administrator, which in most cases is the principle. This fact is important because many students, parents, and even teachers might not actually know how how their school is classified in terms of choice programs.

In addition to this dummy variable, this project also includes two interaction terms to dig deeper into the relationship of choice and student achievement. The literature suggests choice schools might not have impacts on student achievement as a whole, but they have shown to be beneficial to students of color and students coming from more disadvantaged families (Gleason et. al, 2010). choice_stud_col is the interaction term between choice and minority, to see if there is a differential effect of choice when the student identifies as black, hispanic, or mixed. choice_ses is included to see if attending a choice school has if you are also from the bottom two socio-economic background quartiles. Together these interactions help investigate key details from the choice literature.

-family background
In order to control for family background this project uses a socio-economic status construct. This composite variable is comprised of parent/guardian’s’ education, occupation, and income. The environment outside of school is very important in modeling achievement. The seminal Coleman report highlights this fact, finding 80 percent of variation of achievement was within schools and only 20 percent between school, suggesting the majority of student outcomes are determined by the difference between student rather than the schools they attend (Coleman, 1966). It continues to be the case that SES is one of the strongest predictors of student achievement.

The race of the student is included in this paper as a control. stud_col is equal to one is the student identifies as Black/African-American, hispanic, or more than one race and is equal to zero if the student responds with anything else.

Student ability is controlled for by using the math gain scores as the dependent variable. Because innate ability is time invariant, differencing the 2009 test scores with the 2012 scores we can clean out the effect of innate or inherent ability each student had. For more information see, the dependent variable section.

-student motivation
In order to control for students intrinsic motivation, which surely has an impact on student achievement two proxies are used. goodgrades is a dummy equal to one if students strongly agreed with the statement good grades matter to them and zero if they agreed, disagreed, or strongly disagreed. Because we are looking at student motivation, only students who strongly agreed with the statement would be the ones we would consider motivated. This allows for a little more variation and tries to distinguish between students more effectively.

Second, this project includes a continuous variable, student_expect that measures student expectations in terms expected of years in school. Students were asked how far they expected to go out of 11 options: drop out of high school, graduate high school only, attend a 2 year college or university but not finish, graduate from a 2 year college or university, attend a 4 year college or university but not graduate, graduate from a four year college or university, start a master’s degree, complete a masters degree, start a PhD, MD, or other professional degree, or complete a PhD, MD, or other professional degree. These variable are included to gauge and control for student motivation on student achievement.

-parent characteristics
In order to account for selection bias, this project attempts to control for the underlying differences between parents that might affect student achievement and also whether or not a student attends a choice school. Nine variables from the parent questionnaire are included to do this. First, six dummy variables equal to one if they responded yes and zero if they responded no to the following questions: Since the beginning of the school you have you attended a general school meeting?, a parent teacher organization or association?, a parent-teacher conference?, a museum?, served as a volunteer?, and participated in school fundraiser?.

Next, this project includes a continuous variable, parent_expect that measures parent expectations of their student in terms expected of years in school. If parents have high expectations for their child we expect they will be involved and attempt to help their student do well more than those with low expectations of their future education attainment for their child. Parents were asked how far they expected each student to go out of 11 options: drop out of high school, graduate high school only, attend a 2 year college or university but not finish, graduate from a 2 year college or university, attend a 4 year college or university but not graduate, graduate from a four year college or university, start a master’s degree, complete a masters degree, start a PhD, MD, or other professional degree, or complete a PhD, MD, or other professional degree. Each option was assigned a number of years in school to create a continuous variable. The literature stresses the importance of labels and expectations on student performance.

The variable langnoteng equal to one if the language spoken in the student’s home was not english and equal to zero otherwise, was included to control for the differences that face students when english is not spoken in the home.

Lastly, the dummy variable help_hw was included to control for parent involvement and help on school work. help_hw is equal to one if the parent help their child with homework every week and equal to zero if they never do or help less than a week. It is worth noting that parents are not the most trustworthy when talking about their parenting practices. Often they answer more as an ideal than a reality. In all the above proxies do a strong job of controlling for unobserved parent characteristics we want to control for in order to move closer to a valid comparison.

-teacher quality
A variable that controls for teacher quality, or perceived teacher quality is added to the independent variables. Teacher quality is very difficult if not impossible to measure.This paper includes a continuous variable, teach_expect which is a composite variable developed by the HSLS 09 that aims to capture a teachers perception of their peers expectations of students.This composite is composed of questions that get at teachers expectations of students ideas, futures, and performance. Knowing that teachers can evaluate other teachers well, this project uses this composite to control for teacher quality.

In addition, teach_expr, a continuous variable that measures the number of years a teacher has taught math is included to control for teacher quality. Experience and quality have been shown to be correlated (Murnane & Phillips, 1981; Klecker, 2002).

A dummy variable teach_degree was included to continue to get at teacher quality. A teachers credentials and qualifications have shown to be one of the biggest factors in determining teacher quality (Darling-Hammond, 2000). teach_degree is equal to one when a teacher has received an advanced degree and equal to zero if they have not. The idea being that teachers who completed degrees signal a variety of characteristics, such as, perseverance, the extra effort, and hard work.

Finally, several dummy variables were included to account for student perceptions of their teachers. Students often have a good idea if they have a “good” or “bad” teacher, these variables are an attempt to take advantage of that fact. The dummy variables were coded equal to one if the student agreed or strongly agreed with the question and to zero if they disagreed or strongly disagreed. Dummy variables were made for the following questions: does your math teacher value and listen to student ideas?, treat students with respect?, think all students can be successful?, make math interesting?, and make math easy to understand? These controls are attempting to get at teacher quality. These are crude controls but this project argues that these factors all together do a decent job accounting for teacher quality.

Descriptive statistics
Before Multiple Imputation

Variable	Observations	Mean	SD	Minimum	Maximum
mathBY	21444	40.18	11.97	15.8527	69.93
mathF1	20594	67.22	19.21	25.0057	155.1
choice	17754	0.209	0.406	0	1
ses	21444	0.054	0.78	-1.9202	2.88807
ses1	21444	0.16	0.366	0	1
ses2	21444	0.172	0.378	0	1
ses3	21444	0.197	0.398	0	1
ses4	21444	0.212	0.408	0	1
stucol	22409	0.363	0.481	0	1
studentexpect	16813	19.34	4.16	11	25
goodgrades	21062	0.592	0.491	0	1
anymeeting	15525	0.83	0.375	0	1
ptomeeting	15492	0.383	0.486	0	1
ptconf	15480	0.569	0.495	0	1
volenteer	15519	0.305	0.46	0	1
fundraise	15513	0.53	0.499	0	1
museum	15448	0.534	0.498	0	1
hwhelp	15711	0.483	0.499	0	1
pexpect	21658	11.06	13.04	11	25
langnoteng	15985	0.219	0.413	0	1
teachexpect	1524	0.118	0.954	-5.13	1.29
advdegree	17067	0.505	0.499	0	1
experience	17020	10.14	8.48	1	31
tvalues	18973	0.855	0.3351	0	1
ttreats	18964	0.914	0.28	0	1
tsuccess	18905	0.922	0.267	0	1
tlisten	18933	0.883	0.32	0	1
tinterest	19936	0.629	0.482	0	1
tmatheas	18939	0.74	0.438	0	1

After Multiple Imputation

Variable	Mean	Standard Error
mathBY	39.96	0.08
mathF1	66.42	0.13
choice	0.209	0.003
ses	0.041	0.005
ses1	0.159	0.002
ses2	0.174	0.002
ses3	0.202	0.002
ses4	0.215	0.002
stucol	0.364	0.002
studentexpect	19.11	0.003
goodgrades	0.587	0.033
anymeeting	0.7795	0.003
ptomeeting	0.322	0.01
ptconf	0.552	0.007
volenteer	0.254	0.011
fundraise	0.483	0.009
museum	0.462	0.006
hwhelp	0.501	0.009
pexpect	11.09	0.086
langnoteng	0.158	0.008
teachexpect	0.111	0.007
advdegree	0.504	0.003
experience	10.09	0.064
tvalues	0.851	0.002
ttreats	0.911	0.001
tsuccess	0.92	0.001
tlisten	0.881	0.002
tinterest	0.628	0.004
tmatheas	0.736	0.003

Missing data
A big issue with survey data is missing values. Sample members taking the survey would sometimes leave answers blank or respond that they did not know.This issue would be okay if we knew the missing values were random, however most often there is systematic reasons for why some people leave answers blank thus presents bias into the model. One way to deal with this issue is the imputation of missing values. This method uses other characteristics about the respondent to impute an estimation of the missing values. The problem with imputation is that it tames the data, reducing outliers and reinforcing the means. In this project, multiple imputation is used to deal with missing data. Multiple imputation has the benefit of imputing missing values, however, because it creates multiple values for a given imputation, it reintroduces randomness, avoiding over precision that is caused by standard imputation. For all variables with missing values, the project uses STATA implementation of the Monte Carlo Markov Chain (MCMC) multiple imputation algorithm that generates five plausible values for each variable based on non-missing values for every other variable. The random seed that was used was 12061992. The analyses were replicated for each of the five imputed data sets and the final coefficients and standard errors were merged using Rubin’s Rules. Using multiple imputations is ideal because it sufficiently mitigates non-random missing values by imputing missing values.

Attrition
If we are using student test score gains, which are measured in 11th grade, should we be concerned that some students in the sample went to choice schools for the beginning of high school but then transfered or vice versa? If this is the case, we would expect there to be bias in our results because it would ether under or overestimate the true effect. Luckily the HSLS of 2009 collects data on which students transfer. Looking at the conditional descriptive statistics, of the 3,290 student who were enrolled in choice programs, none of them ended up in different schools by 11th grade. It is worth noting that 334 survey members did not respond and for 90 respondents the question was no longer applicable. We will assume this is not an issue.

Econometric issues
To represent many of the variables in our underlying model we used plug-in proxies because the true variable is unobservable. When a variable is unobserved it is hard to know for sure the strength of the proxy. If the proxy is weak, it could be that case that it should not even be included in the model, a reason why we may see little significant on some of our variables. This could be particularly true for some of the variables trying to represent student motivation, parent characteristics, and teacher quality. The variables included were indicators of the underlying process but not the process itself.

Measurement error is another way to look at our proxy strength problem. For example, let say there is some variable we are measuring, and the measurement of that variable contains a certain amount of error. We can think of this as measurement error because unlike many proxies, we care about the specific estimates of these variables. Under the CEV if the covariance of our true variable and our measurement error is equal to 0 then although our estimates will be biased, this bias will be in the downward direction. This is an unfortunate reality of education research there are some many unobserved variables and good strong data is rare. This measurement error can be considered a data problem.

Another data problem is which is cut from the same wool is the data at hand was not designed or collected with the specific questions of this paper in mind. For that reason the data is applied to the model, instead of collected for the model. This results in many compromises when doing analysis.

Maybe most influential on our statistical significance and explanatory leverage, is just the general complexity in student outcomes. Education achievement in general is a very stochastic process; there is a lot of natural randomness. These are just a few more reasons why education data is hard to work with.

Another issue could be clustering. This is common in education research. Students are nested within classrooms, schools, and communities. It is possible, and even probable that there are unobserved factors affecting students who, for example are all in the same classroom that has no heat, no computer, and a power plant directly outside. We can not assume all of our observations to be independent because they share conditions at the school and classroom level. The school codes were suppressed in the public data file so we were not able to carry out HLM or even clustering our standard errors at the school level to account for the nested structure of the data.

Results Table
Students IRT Math Score Gains Estimated by Ordinary Least Squares

	Model1	Model2	Model3	Model4	Model 5
choice	0.219	0.378	0.362	0.412	0.447
	¹	²	³	⁴	²
choice_SES		1.045*	1.068**	0.78	0.792
		⁶	⁷	⁸	[.417]
choice_race		-0.894	-0.973*	-0.842	-0.843
		⁹	¹⁰	[.452]	¹¹
ses		4.109***	3.439***	3.197***	3.089***
		¹²	¹³	¹⁴	¹⁵
stucol		-1.01***	-1.183***	-1.32***	-1.299***
		¹⁶	¹⁷	¹⁸	¹⁸
studentexpect			0.328***	0.261***	0.253***
			²⁰	²¹	²¹
goodgrades			1.303***	1.051***	0.963***
			²³	²⁴	²⁴
anymeeting				0.489	0.494
				²⁶	²⁷
ptomeeting				-0.509*	-0.517*
				¹	²⁹
ptconf				-0.233	-0.254
				³⁰	³¹
volenteer				0.755*	0.693
				³²	³³
fundraise				0.470*	0.438
				³⁴	³⁵
museum				0.707**	0.693***
				³⁶	¹⁶
hwhelp				-2.136***	-2.05***
				³⁵	³⁹
pexpect				0.044***	0.042***
				⁴⁰	⁴⁰
langnoteng				2.258***	2.207***
				⁴²	⁴²
teachexpect					0.260*
					⁴⁴
advdegree					0.401*
					⁴⁵
experience					0.054***
					⁴⁶
values					0.980*
					⁷
ttreats					-0.039
					[.377]
tsuccess					-0.379
					⁴⁸
tlisten					0.647
					⁴⁹
tinterest					-0.329
					⁵⁰
tmatheasy					0.803***
					⁵¹
constant	26.417	26.586***	19.647***	20.499***	18.55
	⁵²	⁵³	⁵⁴	[.846]	⁵⁵

N	23415	23415	23415	23415	23415

ledgend: * p<0.05; **<0.01; ***<0.001; note: standard errors are clustered at the school level and displayed in parenthesis

Works Cited
Darling-Hammond, Linda. “Teacher quality and student achievement.”Education policy analysis archives 8 (2000): 1.

Coleman, James S. Equality of Educational Opportunity. [Washington]: U.S. Dept. of Health, Education, and Welfare, Office of Education; [for Sale by the Superintendent of Documents, U.S. Govt. Print. Off., 1966.

Klecker, Beverly M. “The Relationship between Teachers’ Years-of-Teaching Experience and Students’ Mathematics Achievement.” (2002).

Murnane, Richard J., and Barbara R. Phillips. “What do effective teachers of inner-city children have in common?.” Social Science Research 10.1 (1981): 83-100.

United States. Department of Education. National Center for Education Statistics. EDAT. Web. < http://nces.ed.gov/surveys/hsls09/>.

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A Smarter Charter: Self-Selection Biases in Charter School Studies

April 14, 2015April 14, 2015Alyssa Glanzer

Chapter 4 of A Smarter Charter summarizes research comparing student outcomes between charter and traditional public schools. The authors concluded that students in most charter schools perform about the same as students in comparable public schools (68). However, since students choose to enroll in charter schools, this comparison is difficult to make due to self-selection biases that make students attending a charter school and traditional public schools different.

The authors discussed a CREDO study that found low-income and ELL students in charter schools outperformed students in traditional district schools, but without a randomized controlled experiment, an alternative explanation could be that families who sought out charter schools were more motivated (70). Bifulco et al., who studied magnet schools, also listed motivation and parental support as potential confounding variables. Thus, the authors stress that although the findings may look promising for charter schools, it is unclear if charters are directly responsible for gains in student achievement or if the gains are due to other factors, such as family motivation.

An IES study controlled for family motivation by comparing students admitted to charter schools by random lottery with students who applied but were not admitted. The authors explain that although this may eliminate the concern with the CREDO study about family motivation, peer influence is still a potential bias, with lottery winners surrounded by classmates from similarly motivated families, while lottery losers are educated with many peers who did not apply to a choice school, and hence may not be as motivated (72). Again, as in Bifulco et al.’s analyses, we cannot determine if the school is directly responsible for improving student achievement or if another factor is driving the relationship.

The authors also discussed KIPP, a charter organization that emphasizes tough love and boasts demanding expectations (78). This program undoubtedly uses many of the strategies explained by Welner to influence student enrollment, including the “bum steer,” by driving away ELL and special needs students from applying with their tough love mentality. KIPP also makes use of Welner’s “flunk or leave” tactic, and only students that survive the demanding expectations remain by high school, as KIPP does not replace students who leave. Although KIPP students have shown substantial academic gains, when KIPP took over a regular, high-poverty public school, serving a non self-selected population, the program failed, indicating that the academic achievement at KIPP may be due to the high motivation levels of the students, and not the charter program itself.

The authors highlight the self-selection biases that make it difficult to definitively state that charter schools cause gains in student achievement. It is possible that influence of parental motivation and peers may be driving the apparent improvement among charter school students. Additionally, a close look at the KIPP organization indicates that student achievement may in fact be due to the types of students the school enrolls than the actual school itself. Is it the quality of instruction or the students who choose to enroll that make charter schools successful? Can these self-selection biases ever be completely controlled for?

Housing Development Arguments in “Climbing Mt. Laurel”

March 31, 2015April 1, 2015Angus Macdonald

Douglas S. Massey et. al. describe in Climbing Mount Laurel the ways in which the market for housing in the United States, more specifically New Jersey, is not free and fair. Zoning laws and real estate agent interests have made housing markets inaccessible to some, especially racial minorities and lower class families.

Solutions to these sorts of problems do not come easy, and lower income housing developments are contentious due to the many stakes involved. Real estate agents and homeowners have a stake in the value of housing, and inviting low income housing to the neighborhood is generally seen as a risk to be avoided. There is also the fear that with poverty comes crime and other problems less prevalent in wealthy areas, not to mention fears of higher tax burdens, sagging schools, and the like. These sentiments are often fueled by xenophobia. Of course, in the case of Mt. Laurel, the potential residents of such housing aren’t all strangers moving into the community, many are families who have either been forced out or are at risk of losing their housing in Mt. Laurel.

It is no surprise that there was quite a lot of backlash, disagreement, and dragging of feet in the wake of Mt. Laurel II. While the most evident misgivings about developing low-income housing are self interested or bigoted, many are more complicated than this. Effectively integrating low-income residents into an established suburban community is a difficult task, even without the stumbling blocks that the city puts in place. A Mt. Laurel resident posed the argument that integrating low income residents into suburban communities isn’t as beneficial as it seems, because the location and structure of the neighborhood practically requires a car, an expense potential residents might not be able to afford (44).

Others doubt the effectiveness of creating deliberate, low-income developments. For one, potential residents might not appreciate the stigma attached to living in such a development (45). Second, some question that such a development would be effective at integrating low-income residents into a wealthier community (44). Massey engages the idea that in the past, people on all political sides agreed that housing developments created for low-income citizens perpetuated cycles of poverty and did little to combat discrimination (23). Many worry that the results of Mt. Laurel could lead to similarly dismal results.

The property value argument pushed by realtors and homeowners has deeper implications, too. While realtors are worried about the wealthy neighborhoods becoming stigmatized due to poor developments, the stigma could be just as harmful to the residents of the new development. If a community such as Mt. Laurel experienced a severe case of “white flight,” some argue that the poor members of the community would be left in an even worse place – in an empty shell of a residential suburb (47).

While many of the arguments against Mt. Laurel are easily dismissed, several articulate many of the challenges that must be faced in tackling problems of social stratification and segregation.

Charter School Renewal in CT: The Accountability Is Flexible

February 21, 2015Robert Cotto Jr.

Over the next few months, the public and Legislature will debate whether charter schools in Connecticut are sufficiently regulated or not. The State Department of Education and Board of Education will also decide whether or not to renew six (6) existing charter schools in Connecticut.

Already this legislative session, there is a bill for a moratorium on new charter schools and a review of existing ones. There are also proposals for more charter schools in CT. A missing aspect of this debate has been the existing charter school renewal process. This process merits more scrutiny because the firm “accountability” it promises is actually more flexible than advertised and it stands in contrast with how other public schools are treated by the State.

When Connecticut lawmakers initially allowed charter schools to operate in 1997, a major guiding principle was an exchange of “flexibility” for “accountability”. In other words, private non-profit “entities” receive public funds to operate public charter schools with permission to operate outside of various state and local laws, such as limited or no requirements for teacher certification and collective bargaining; but only if they met State educational goals. Charter school laws and guiding principles are similar around the country.

In 2014, the State’s charter school report claimed that, “Connecticut’s charter school law and accountability plan administered by CSDE require charter schools to demonstrate their success and compliance with the law in exchange for their charters.” In 2010, the report put it more directly as success and compliance, “in exchange for autonomy from local boards of education.”

This concept suggests that if charter schools don’t meet defined goals or state educational interests, they will face concrete, firm, and predictable consequences. The case of charter schools renewals, past and present, shows that the concept of “accountability” for “flexibility” is more theory than practice. Instead, when it comes to charter schools, the “accountability” is “flexible” and consequences do not come their way in a regular or predictable fashion.

For other public schools, the concrete goals usually mean some test-score target defined each year; and the firm, predictable consequences for not meeting those targets can mean mandatory state or local intervention in managing the school, firing most of the staff, or converting the school to a private management company, or a charter school. Examples of this “test and punish” approach throughout Connecticut include, but aren’t limited to:

Lewis Fox Middle School in Hartford was closed and later replaced with an Achievement First Charter School
Milner Elementary School in Hartford and Paul L. Dunbar School in Bridgeport were reconstituted and then operated by Jumoke/FUSE charter management corporation through the controversial “commissioner’s network”. This experiment ended with the demise of FUSE/Jumoke.
Last year the State of CT and Hartford Public Schools attempted to close Clark Elementary and replace it with an Achievement First-managed charter school, but that effort failed.

There is a different approach for charter school renewal and evaluation. Depending on the particular charter, the non-profit, private organizations that operate a public charter school must go through a process to determine whether they can keep their charter or lose permission from the state to operate the school. This process happens every three to five years for each charter school. The process is a way to regulate all charter schools and make sure they are serving the goals of public education.

The process to renew a charter has multiple parts and extends over several months. The charter operator must first submit an application to the State Department of Education explaining their work, including areas such as students’ academic progress (interpreted by the state as standardized test results), curriculum, staff development, finances, and governance (management & administration) of the school.

Six schools will go through the charter renewal process this school year (2014/2015). Those schools include: (click on the school name link for the 2014/15 renewal applications.)

Explorations in Winsted
Odyssey in Manchester
New Beginnings Family Academy in Bridgeport
Stamford Academy in Stamford
Interdistrict School for Communication and the Arts (ISAAC) in New London
Common Ground High School in New Haven

These aren’t new charter schools, but have enrolled children for ten to twenty years at this point. Having opened in 1997, Odyssey, Common Ground, and ISAAC were among the first state charter schools created in Connecticut. Here’s a list from The CT Mirror for future charter school renewal years.

The red tags on the map are the six charter schools up for renewal this year. Blue dots on the map are all other charter schools in CT. Click on the dots/tags for more basic info on each school, including its website.

The next step is that the State Department of Education reviews the application and conducts a site visit to observe how a school operates compared to the description in their renewal application. A look into this process can be seen in this letter from CT SDE’s charter school program manager to administrators at the Common Ground High School in 2009, when the school was last up for a review. The letter shows some of the criteria for the charter renewal, which includes categories listed above, such as finance, test results, etc. If the school is meeting its goals and the educational interests of the state, then the State Board of Education can renew the school’s charter.

The state’s charter school law, specifically Connecticut General Statutes Section 10-66bb(g), outlines basic criteria that should guide the State Board of Education in deciding whether or not to renew a school’s charter. The criteria include, but are not limited to:

“student progress”,
administrative irresponsibility or misuse of public funds,
non-compliance with applicable state laws,
and failure to attract, enroll, and retain certain demographic groups such as students with disabilities and emerging bilingual children, identified as “English Language Learners.

It’s worth reading the CT charter school renewal law here.

The law leaves the door open for flexibility in this process. The text states that the State Board of Education “shall” (must) take into account the findings of a holistic, independent appraisal, but “may” (can) deny the application based on criteria in four categories, but not necessarily others. In short, the law does not require the State Board of Education to deny a charter renewal application for any particular reason, although it may do so.

In this way, lawmakers created loose rules in the charter renewal process. Like a judge may have discretion on a legal matter, or a psychologist uses clinical judgement, the CT State Department of Education reviews charter schools on a case by case basis and has a wide range of options in responding to their strengths and weaknesses. This provides administrative leeway or flexibility for state charter schools in Connecticut in the charter renewal process, but is contrary to this apparently strict mantra of “more accountability for more flexibility.”

Not included in the above section of charter school renewal law or the checklist are requirements to reduce racial, ethnic, and economic isolation or other state laws. To that point, the very next section of the charter school law states:

(h) The Commissioner of Education may at any time place a charter school on probation if (1) the school has failed to

(A) adequately demonstrate student progress, as determined by the commissioner,
(B) comply with the terms of its charter or with applicable laws and regulations,
(C) achieve measurable progress in reducing racial, ethnic and economic isolation, (continued…)

Finally, the state can revoke a charter at any time in cases of an emergency, or with written notice for failure in any of the areas listed above. The commissioner has to provide notice in writing about why she/he moved to revoke the charter. The law states:

(i) The State Board of Education may revoke a charter if a charter school has failed to:

(1) Comply with the terms of probation, including the failure to file or implement a corrective action plan;
(2) demonstrate satisfactory student progress, as determined by the commissioner;
(3) comply with the terms of its charter or applicable laws and regulations; or
(4) manage its public funds in a prudent or legal manner.

Even if the State Board of Education moves to revoke a charter, the “governing council”, or a charter school’s managing board, can provide an oral or written presentation to contest the State’s decision to revoke the charter and demonstrate compliance in areas deemed deficient.

Perhaps because of the flexibility in the charter renewal law, there have been times when charter schools have been renewed despite apparent examples of not meeting specified goals, the listed criteria in statute, or educational interests of the State. Another possibility is that the implementation of the policy has not been sufficiently discerning to identify major problems such as financial malfeasance or the mistreatment of children.

As a result of this flexibility, the state Board nearly always renews charters. Between 2010-2013, all 17 charter schools in the state obtained a renewed charter from the State Board of Education, according to this list from the CT Mirror. (excluding one that became an interdistrict magnet school) Non-charter public schools have not been so fortunate as they have had to follow strict federal and state rules and consequences, primarily on the basis of standardized test results. Since 2007, at least ten non-charter schools in Hartford, CT alone were closed or the staff fired on the basis of rigid test-based targets and subsequent punishments as outlined in state, federal, and local policy.

(Note: To my knowledge, there isn’t a list of all CT schools that have been closed, reconstituted, converted to charters, turn(ed) around, or restarted as a result of NCLB/RttT test-based accountability. If you know of a list, please share!)

Take the charter schools requirements to enroll representative populations of emerging bilingual students and students with disabilities and the reduction of racial and ethnic isolation. In my report with Kenny Feder, “Choice Watch,” over at CT Voices for Children, we reported that charter schools in CT tend to have smaller proportions of emerging bilingual children and children with disabilities when compared to local school districts, and are often more racially segregated than local school districts. Yet, no charter school was revoked because it didn’t include emerging bilingual students, children with disabilities, or because it was racially segregated, as state law would suggest.

When problems are found, the State Board of Education has often allowed schools to keep their charters rather than closing the school through a non-renewal. In some cases, the State board required more frequent review of charter schools, such as a renewal process after three years rather than five, for example. This scenario happened in 2007 with Common Ground and Odyssey Community School (due to poor test data) and Achievement First-Hartford in 2013 (due to excessive suspensions/special education/civil rights complaints). In other cases, schools received “probation” by the State Board of Education before a charter was revoked or non-renewed. Examples of this action included Highville/Mustard Seed (due to financial malfeasance) and Jumoke (due to financial malfeasance).

According to past and recent State Department of Education reports on the operation of charter schools, only five charter schools closed their doors since 1999. Three closed because of insufficient funds, despite the fact that the State Dept. of Education was required to review their financial plans before a charter was granted. Additionally, the CT State Board of Education shut down one charter school for health/safety violations and closed one charter school because of lack of academic progress.

Even relatively low test scores haven’t been a sufficient reason to deny a charter renewal. When its charter was renewed in 2012, Trailblazers Academy charter school had among the lowest aggregate test results in CT. By the rules of the No Child Left Behind Act of 2001, Trailblazers had not met “Annual Yearly Progress” for six years.

Stamford Academy, which had among the lowest aggregate test results in 2013 is now in a similar situation this year as it faces a charter renewal process. (They are up for a renewal after only three years.) By 2010-11, Stamford Academy hadn’t made “Annual Yearly Progress” for five years.

(Note: Annual Yearly Progress was such a problematic measure that it was abandoned by the CT State and U.S. Federal Departments of Education in Connecticut’s 2012 waiver to parts of the NCLB Act.)

According to the logic of more “accountability” for more “flexibility”, shouldn’t these schools have lost their charters?

Despite not making AYP (the goal back then) and the State reporting this negative status, it is still unclear why these charter schools never faced the same sorts of clear, strict punishments as other public schools under NCLB. While the CT State Department of Education and State Board of Education delegated the responsibility of implementing NCLB sanctions to local districts for schools under local control, they apparently haven’t assumed that responsibility for schools under their own supervision in recent years.

Under the No Child Left Behind Act, if these schools had been non-charter public schools, they would have been targeted for punishments such as firing the entire staff, notifying parents that they could choose to go to another school, closing the school, state takeover, conversion to charter schools, or taking away public governance in favor of private management. Ironically, Stamford Academy and Trailblazers were the end goal of No Child Left Behind – privately managed, publicly-financed state charter schools that parents chose to enroll their children, ostensibly to produce higher test scores. Yet, they were still amongst the most struggling academically and the state renewed their charters in 2012.

In defense of these schools, (Trailblazers, Stamford Academy, and others) perhaps they are offering educational benefits not captured by overall low test results. Stamford Academy and Trailblazers Academy enrolled high proportions of children that struggled in school. These schools also served a much more historically under-served group of children, mostly Black, Latino, low-income, and many more students with disabilities when compared to the more affluent Stamford Public Schools, which also have higher proportions of white students.

I am not advocating that Trailblazers and Stamford Academy should close because I don’t have enough information on either one to make a judgement, nor would closing the schools improve them. But I am pointing out that there have been two sets of rules when it comes to state “accountability”. Several years ago, Wendy Lecker also pointed to what appeared to be “double standards” in evaluating charter and other public schools in her column at The Stamford Advocate.

Let’s also consider what the renewal process has looked like for some of Connecticut’s charter schools that look better as measured by test score data. When its charter was renewed in 2012, the State touted Amistad Academy’s high test results compared to New Haven schools in 5th grade, and particularly for 8th grade students.

The state’s resolution on Amistad Academy noted that the school didn’t meet “Annual Yearly Progress” in the elementary grades, but did in the high school grades in 2010-11. But there didn’t appear to be any firm academic goals apart from the AYP metric, just general description of its test results and how they were better than the New Haven Public Schools overall. There was a presentation of test results with some narrative, particularly of the vertical scale scores offered as evidence in the final resolution to approve the charter renewal.

Undiscussed however, was the fact that the test participation data showed massive student attrition at Amistad Academy. In 2008, there were 76 students in grade 5, but there were only 53 students that matched that group in grade 8 in 2011. This was a loss of 30% of the student population from the original 76 students that started 5th grade in 2008.

So the high overall test results in 8th grade only accounted for 70% of the kids that stayed at the school-those students that took the standard CMT in math in both grade 5 and grade 8 at Amistad Academy. This attrition happened in CT and New Haven overall, but not to the same degree. Such attrition impacted the way the test results were interpreted (we are only looking at 70% of an already selected cohort) and the manner in which the test results were obtained (removing low-scoring or undesirable students can inflate results at this school and impact other local schools that later enroll these students). This attrition went unmentioned in the State Board’s renewal resolution despite one of the questions in the State checklist being, “Is there a high turnover of students?”

The State’s resolution, referencing the audit and site visit, also explained that the school lacked curricula in grades 3-8 science, K-12 health, physical education, and the arts. There were also problems with financial controls and safeguards between Achievement First, Inc, the private charter management corporation, and Amistad Academy, the public charter school; and many of the school’s teachers lacked proper state certification. The school was allowed to remedy, or begin fixing these deficiencies before their hearing at the State Board of Education, thus securing a renewed charter.

In Connecticut, there are laws against both excessive suspensions of students and racial/ethnic segregation of students, particularly for charter schools. [see above CGS Sec. 10-66bb(h)] But the renewal process for Amistad Academy ignored its exclusionary disciplinary policies, racial and ethnic segregation, and provided no analysis of representative populations of bilingual children and students with disabilities, among others. To be sure, these issues aren’t specified in the renewal checklist, but the school is required to follow applicable laws and regulations, including laws about students suspensions, special education rights, and racial and ethnic segregation, among others. A year after the Amistad renewal, The CT Mirror and The Hartford Courant reported that Amistad Academy and its Achievement First affiliates had the highest numbers and rates of suspensions of children in CT. As Choice Watch reported, the school was (and still is) racially segregated, as well as most charter schools in CT.

Amistad Academy may be a school that people want their children to attend amidst the relative disinvestment, neglect, and mis-education of children of color in other schools. However, parent and families’ decisions about schools happen in the context of State over-investment and policy in favor of public school choice programs and under-investment in other public schools with high proportions of low income and Black, Puerto Rican, and Latino children. This arrangement is a key feature of Connecticut educational policy, like other states. (See M. Apple, P. Lipman, & K. Buras writing on this issue.)

Regardless of Admistad Academy’s status, the State’s own charter renewal report documented educational concerns and overlooked substantial problems. It was not until then-State Child Advocate Jamey Bell intervened that the suspension information and the depth of the problem became known to the public, particularly throughout the Achievement First charter school chain. As a result of State and public pressure, Achievement First/Amistad has reportedly made improvements to its disciplinary policies; and lately the company has explored the idea of alternative methods in addition to its current “no excuses” schooling.

Like all schools, Amistad Academy has both its strengths and weaknesses. Recognizing this point, the State’s charter renewal process has been flexible in its approach towards renewal and remediation of charter schools, instead of responding with rigid “accountability.” In addition to flexible, the state’s approach has also been selective in valuing particular types of “achievement” data first, and everything else after.

Accountability at Traditional Public Schools

In Connecticut, however, plenty of other non-charter public schools have similar groups of children as Stamford Academy and Trailblazers Academy charter schools, may need more support, and struggled on overall test results. Unlike these two charter schools, other public schools faced crude forms of high-stakes test accountability under federal, state, and local rules.

This flexible “accountability” stands in stark contrast to the regimented consequences that other public schools face under the No Child Left Behind Act, NCLB Waiver, and other high-stakes test accountability systems such as in Hartford, Connecticut. These systems outline firm, test-based numerical targets and emphasize clear punishments when the goals aren’t met, such as school closings, conversion to charter schools or private management. Unlike the charter renewal process, there are rarely second or third chances for other non-charter public schools, and excuses aren’t acceptable when it comes their “accountability” process.

So here’s a dilemma: Carefully implemented, the ability of authorities to have administrative discretion (reviewing each school on a case by case basis) and assess schools holistically may be pragmatic and humane policy in some cases. In other cases, this flexibility can result in vague, selective accountability. It’s worth considering this local administrative judgement and holistic assessment in the context of all public schools. So I will explore this idea in a future post.

In the meantime, let’s watch this charter renewal process. The charter renewal process offers the possibility for people and groups to weigh in through letters to the State Department of Education, a public hearing for people to testify about the school’s work, and, ultimately, people can testify at the CT State Board of Education before a school’s charter is renewed.

The dates, times, and locations for the local public hearings on these charter school renewals are here and the chart is below. So take a look at the charter school applications and the process documents. In the meantime, here are a few questions to consider:

Is the State of Connecticut exercising sufficient oversight of charter schools through the renewal process? Is the law sufficient?
Are these charter schools meeting their goals and the educational interests of the State?
What evidence should be weighed in this process of charter renewal?
Can the holistic process of reviewing charter schools be applied to other public schools?

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Charter Renewal
Public Hearings 2014-15

School Name	Dates	Time	Hearing Location
Robert Trefry New Beginnings Family Academy	Tuesday February 24, 2015	6:00 -8:00 pm	Bridgeport City Hall Common Council Chambers 45 Lyon Terrace Bridgeport, CT 06604

Estela Lopez Odyssey	Wednesday February 25, 2015	6:00 -8:00 pm	Howell Cheney Technical High Multi-Purpose Room 791 W. Middle Turnpike Manchester, CT 06040

Stephen Wright Stamford Academy	Thursday February 26, 2015	6:00 -8:00 pm	J. M. Wright Technical High School Gymnasium 120 Bridge St. Stamford, CT 06905

Charles Jaskiewicz ISAAC	Tuesday March 3, 2015	6:00 -8:00 pm	Science and Technology Magnet High School of Southeastern CT Lecture Hall 490 Jefferson Avenue New London, CT 06320

Allan Taylor Explorations	Thursday March 5, 2015	6:00 -8:00 pm	Winsted Town Hall P. Francis Hicks Room 338 Main Street Winsted, CT 06098

Maria Mojica Common Ground	Tuesday March 10, 2015	6:00 -8:00 pm	Wilbur Cross High School Auditorium 181 Mitchell Drive New Haven, CT 06511