dean hardin coleman, boston university · tenure to raising school performance through simple and...

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This issue of the Journal of Education is our second on the themeSchools and Schooling, for which I have served as the guest editor ofa special section. The articles in this issue, and in this section inparticular, reflect again the significant relationship between educa-tional research and the practice of education. Too often, this rela-tionship is either fractured or haphazard. Increasingly, however, weare seeing that real school improvement is achieved by the data-driven implementation of practices that have received empiricalvalidation. The reason this journal annually presents a case study ofeffective practice is to support the growth, development, and dis-semination of promising educational innovation. This year, a col-lective case study points to generalizable strategies to drive schoolimprovement, in particular, those schools that serve students inurban communities.That the structural analysis also derived similar conclusions

points to the combined power of the insights that are offered in this

section. School leadership, cooperative efforts, and the quality ofteachers matter. These findings are powerful because, in my view,the only way we will close the achievement gap and encourage edu-cators to prepare students to be leaders in the twenty-first centuryis to recognize that there are systematic and proven ways toimprove schools. Like the development of empirically validatedmedical practice that evolved in the last century, educators must beheld accountable for using practices that work, particularly in high-need schools and districts. We must be able to accurately assess astudent, school, or district; identify a valid strategy for change;evaluate the results; and then adjust accordingly. I believe that thisoutcome will be achieved when we honor the relationship betweeneducational research and the practice of education. We hope thatthis issue of the Journal of Education will be helpful in this endeavor.

A Letter from the Guest Editor

dean hardin coleman, boston university

ABSTRACT

Since 2006, the Thomas W. Payzant School on the Move (SOM)Prize has been awarded annually to a Boston public school that hasmade significant progress in improving student achievement. Thiscase study identifies the structures and strategies that best servestudents in prizewinning schools and provides a profile of each ofthe four winning schools from 2006 to 2009. We also exploresome of the opportunities and barriers the schools have faced insustaining their success over time. A common case study method-ology was employed, using school observations and interviewswith school leaders, staff, and students. Findings reveal the impor-tance of distributed leadership, data-driven instruction, and stu-dent-centered approaches to learning in achieving school success.

INTRODUCTION

The School on the Move Prize is a validation of the hard work that you do. Too often, especially in public education, it appears

that we are not really doing a good job . . . the Prize proves that students can get a good education in the city of Boston

and there is good teaching and learning happening.

—Principal, Samuel W. Mason Elementary SchoolJune 14, 2010

Since 2006, the Thomas W. Payzant School on the Move (SOM)Prize has been awarded annually to a Boston public school that hasmade significant progress in improving student achievement. ThePrize is awarded by EdVestors, an organization committed to driv-ing change in urban schools through strategic philanthropic invest-ment, and named after former Boston Public Schools (BPS)Superintendent Thomas W. Payzant, who dedicated his decade-longtenure to raising school performance through simple and attainablegoals, such as an increased focus on literacy and mathematics andimproved teacher training. Prize-winning schools are viewed asconsistent with Dr. Payzant’s example, employing straightforwardand effective practices that may be replicated across the largerschool district. The schools receive a cash award of $80,000 to sup-port continued improvement. An additional $20,000 is committedannually to produce case studies that document successful strate-gies. The SOM Prize acknowledges the exemplary progress of oth-erwise unrecognized schools and creates a vehicle for sharing theirwork with school leaders and educators who serve urban students.It is an integral part of EdVestors’ innovative program to effect pos-itive change by identifying successful initiatives and helping furthertheir influence as practical models for reform.

The process of selecting SOM Prize winners begins each springwhen schools with four-year rates of improvement on the Massa-chusetts Comprehensive Assessment System (MCAS) significantlygreater than the district average (50%) are invited to apply. To beeligible, a school’s demographic profile must also be representativeof the BPS district. An initial screening is conducted based on theComposite Performance Index (CPI), a 100-point index that com-bines student scores on the MCAS with those of special needs stu-dents who take the MCAS-Alternate assessment. Applicants arethen subjected to a rigorous quantitative and qualitative analysis thatconsiders broader school improvement strategies as well as otherperformance indicators, such as graduation rates; dropout rates;and the achievement of high-needs students, such as low-income,special education, or English Language Learners (MassachusettsDepartment of Elementary and Secondary Education, 2006). Thefinal decisions are made by an independent selection panel thatreviews all applications, and conducts interviews and school visits.In the first four years of the SOM Prize, a diverse group of

schools has emerged as winners, including two pilot schools—onea high school and the other an elementary school—a traditionalK–8 school, and a small high school occupying one floor of theSouth Boston Education Complex. Pilot schools, allowable under a1994 provision in the Boston Teachers’ Union contract, are districtpublic schools with autonomy over five key areas: staffing, budget,curriculum and assessment, governance and policies, and the schoolcalendar. Prize-winning schools represent the diverse Boston neigh-borhoods of Dorchester, Roxbury, Brighton, and South Boston. Yetdespite differences in location, governance, and grade levels served,all SOM Prize winners, selected through the same basic process,share important characteristics. First, they are all relatively smallschools in the district and have lower enrollments than most com-parable schools with the same grade-level configurations. Second,they are all led by experienced educators who are strong leaderswith deep knowledge of the Boston Public School system. Third,they all share common practices that have proven critical to theirsuccess in improving student achievement, including:

• Distributed leadership: Shared accountability between adminis-trators and teachers for achieving instructional excellence andimproved school performance;

• Data-driven instruction: Integrated data systems used to informdaily decisions about curriculum, instruction, and student sup-ports; and

• Student-centered learning: An approach to learning that balanceshigh academic expectations with developmental supports tar-geted to student needs.

Charting the Course: Four Years of the Thomas W. Payzant School on the Move Prize

chad d’entremont, jill norton, michael bennett, and peter piazza,rennie center for education research and policy

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In this article, the authors explore common practices acrossschools uncovered through a four-year summative study of SOMPrize winners from 2006–2009 conducted by the Rennie Centerfor Education Research & Policy with support from EdVestors. Arigorous and standardized selection process was leveraged toinvestigate varying methods for developing, initiating, and success-fully implementing effective school practices as components oflarger school improvement plans. The research draws upon theprevious case studies of individual SOM Prize winners also pro-duced by the Rennie Center for Education Research & Policy,along with new interviews with school leaders, staff, and students.Interviews were conducted with Boston Community LeadershipAcademy staff and students in May 2010. Follow-up interviewswith the headmasters and principals of the previous SOM-winningschools were conducted in June 2010. Findings highlight key les-sons for adapting successful practices in other school settings.

BACKGROUND

Previous research on school effectiveness is helpful in identifyingthe structures and processes that are common in high-performingurban schools. In particular, research notes that effective urbanschools take a holistic approach to student development; operatewith a high-trust, distributive leadership model; and foster mean-ingful teacher collaborative learning. In their recent case study ofseven schools serving high poverty student populations, Acker-Hocevar and colleagues (2012) found that school success is largelythe result of creating environments that support student socio-emotional growth in addition to nurturing increased academicachievement. Successful schools go beyond analyses of test scoresin understanding students’ strengths and academic needs. Holisticdiscussions of student learning goals help to create supportiveschool cultures, where all teachers share responsibility for improv-ing student learning.Leadership structures are critical to developing a school com-

munity capable of nurturing the “whole child.” In particular, suc-cessful schools often feature a distributive leadership model whereteachers and administrators work collaboratively to achieve sharedgoals. Ferguson (2005) has noted that supportive school environ-ments are typically characterized by broad consensus that all stu-dents can learn. Instruction is tailored to local needs with acommitment to the ongoing re-evaluation and revision of schoolpractices. In addition to encouraging student learning, researchshows that such environments often help reduce teacher attrition,a problem endemic to many urban schools. In their analysis ofurban schools with high retention rates, Stotko and colleagues(2007) identified teacher collaboration and “increased sharing ofinstructional and curricular control with teachers” (p. 47) as majorfactors in teachers’ decisions to remain in the profession.While researchers caution that there is no “cookie cutter”

(Acker-Hocevar, Cruz-Janzen, & Wilson, 2012) approach to urbanschool reform, and researchers suggest that policymakers recognizethe limitations of post-hoc analyses of schools that have alreadybeen deemed successful, the findings identified in the literature on

effective schooling practices are helpful in identifying broad themesfor analysis of SOM award winners. Using this frame, we present acase study of each award winner in order to gain an in-depth under-standing of what effective schooling structures and process maylook like when applied in practice.

METHODOLOGY

In order to distill the practices that contributed to school effective-ness among SOM Prize winners, we conducted a collective casestudy, or “a study that takes place in multiple sites or includes per-sonalized stories of several similar individuals” (Brantlinger,Jimenez, Klinger, Pugach, & Richardson, 2005, p. 197). As notedabove, we began with the assumption that, based on their recogni-tion as SOM Prize winners, each school exhibited certain prac-tices known to contribute to effective urban school renewal. Thecase study approach allowed us to conduct an in-depth examina-tion of what these practices looked like in real-life, localized con-texts (Yin, 2008). Researchers worked in partnership with local leadership teams

to refine interview protocols and identify research subjects toensure successful data collection. Interview participants were cho-sen to represent the diverse interests of the school community.Data collected from interviews, school observations, and availabledocuments were then coded and analyzed for common themes.Our data analysis approach was twofold. First we sought to

identify successful practices within each SOM Prize winningschool. Second, we looked across all four cases to identify thethemes or practices that all had in common. Open coding, orwithin-case analysis, allowed us to identify prominent themesfrom interviews and observations, laying the foundation fordomain analysis. Axial coding, or across-case analysis, then allowedus to examine how various themes related to each other across thefour schools (Rossman & Rallis, 2003). Codes were generated through collaborative discussion among

the members of the research team and were selected to capturecommon themes both within and across SOM award recipients. Inthis way, coding was used as an analytic strategy with the goal ofnoticing and indexing relevant phenomena, collecting examples ofthose phenomena, and beginning to analyze those phenomena forcommonalities or patterns (Coffey & Atkinson, 1996). Further, weused data triangulation to “search for convergence of, or consis-tency among, evidence from multiple and varied data sources,”(Brantlinger et al., 2005, p. 201) focusing especially on interviewand observation transcripts and document review.

ANALYSIS AND FINDINGS

Core Challenges

All four winning schools—Boston Community Leadership Acad-emy, Samuel W. Mason Elementary School, Excel High School, andSarah Greenwood K–8 School—cited common barriers to sus-taining student growth trajectories that led to specific schoolimprovement strategies.

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• Changes in staffing: A dearth of available teachers, increasingteacher turnover rates, and budget cuts combined to createstaffing challenges for SOM Prize winning schools. In someinstances, school leaders had to accept the loss of staff andteachers in whom they had invested years of training, energy,and money. For example, many teachers left Greenwood toaccept positions at newly designated turnaround schools, whichoffer teachers an annual stipend to supplement their salary.Such unintended consequences of broader district-wide initia-tives present another challenge to schools working to sustainhigh levels of student achievement.

• Increased enrollment of high-needs student populations: For schoolssuch as Mason, their reputation for effectively serving studentswith special needs led to problems related to the over-enroll-ment of this student population. According to Mason’s princi-pal, special education students come to the school with a higherlevel of need, and teachers are not always well equipped toaddress such a wide range of learners with diverse learningstyles. Furthermore, there are too few funds to provide teach-ers with the support and training they need to improve instruc-tion for these special education students, or to build thecomprehensive system of safety nets that the staff believe isnecessary to address the needs of all students.

• Declining school budgets: Reductions in school resources limitedthe ability of these four schools to provide students with addi-tional academic support through supplementary programs.Some of the essential programs that have experienced severecutbacks or termination are advisory programs, ninth-gradeorientation, and after-school and summer programming.Despite these budgetary issues, leaders such as Greenwood’sprincipal have been able to maintain great flexibility and afuture goal-oriented perspective. To be sure, flexibility to adaptto various external and internal forces is a characteristic of allof the effective SOM leaders.

Within-Case Analysis of SOM Prize Winners, 2006–2009

All SOM Prize winners were able to overcome shared challengesand show significant and consistent improvements in their servicesto students, thereby enhancing student achievement. Below arebrief profiles of the four winning schools and the practices that setthem apart. For the first three winners of the SOM Prize, thesebriefs outline the themes identified in previously published casestudies as well as provide current demographic data for eachschool and selected MCAS data over the last three years. The 2009winner, Boston Community Leadership Academy, is also describedhere in a one-page overview that was developed primarily frominterviews with school leadership, staff, and students in the springof 2010.

Profile #1: Boston Community Leadership Academy, 2009. BostonCommunity Leadership Academy (BCLA), the 2009 SOM Prizewinner, has the distinction of being the only school to have quali-fied during the entire four years that the Prize has been in exis-tence. In a decade-long tenure, BCLA’s headmaster has guided the

school through its conversion from Boston High School to a smallpilot high school in 2002. Through this process, the headmasterand her staff worked to establish clear standards of behavior, highexpectations for student success, and a mission driven by students’academic and social/emotional needs. BCLA has a strong focus oncollege preparation, and student leadership and engagement in thecommunity. Not only are students required to apply to five col-leges as a prerequisite to graduate, but they must complete com-munity service hours and a senior year capstone project based ontheir work in the community. BCLA has been successful, in part, due to a strong collaborative

culture among students, teachers, and administrative staff; a highlevel of academic and social support for students; and instructionalpractices shaped by continual evaluation of student data. As theheadmaster notes, “We always look at our data and say, how can weimprove? We never say, ‘we are there.’”A BCLA English teacher describes the school as “learning-cen-

tered.” Staff communication consistently focuses on strategies toimprove instruction based on student needs. All students areassessed multiple times a year to create an academic profile thatcan be used to target supports, such as the school’s Aim Hightutoring program provided through Boston Partners for Educa-tion. As one student observed, “the adults in the school constantly pushus to improve academically and keep us focused on our goals.”One of the more unique strategies utilized by BCLA is their

community model approach to student support. Students aregrouped into grade-level learning communities coordinated by

5C H A R T I N G T H E C O U R S E

Table 1: BCLA Demographics, 2009–2010

BCLA District

Total enrollment: 431 55,371African American: 46.2% 36.5%Asian: 3.9% 8.6%Hispanic: 39.9% 39.6%White: 8.1% 13.1%LEP:* 20.6% 20.4%Low-Income: 81.7% 75.6%Special Education: 14.6% 19.6%Graduation Rate: 77.6% 61.4%Attendance Rate: 93.7% 91.2%*Limited English Proficient

MCAS Performance Data – % passing

10th Grade 2007 2008 2009

ELA** 93% 100% 99%Mathematics 95% 95% 99%Science n/a 90% 92%

Composite Performance Index (CPI)

10th Grade 2007 2008 2009

ELA** 80.7 88.8 90.2Mathematics 81.9 90.6 95.5Science n/a 64.1 72.0**English Language Arts

“community leaders.” According to interview responses fromteachers and students, the community model brings coherence tostudent support services by identifying a single point person ateach grade level to whom teachers, administrators, parents, andstudents can go for resources.

It is clear that BCLA’s strong commitment to effective, institu-tion-wide collaboration; targeted, data-driven assessment; andhigh academic expectations and support were instrumental inshaping this school’s present status as a leader in Boston’s educa-tional community. Another important element that helped to setBCLA apart from peers is its innovative approach to creating anddeveloping the learning communities, which not only provide stu-dents with a structured, supportive academic environment butalso streamline the administrative component that manages therunning of the program.

Profile #2: Samuel W. Mason Elementary School, 2008. Mason Ele-mentary, a small elementary school in Roxbury, utilizes a fullinclusion model for its population of students with special needs,a population that has grown in the two years since the school wonthe SOM Prize. The full inclusion model is based on the belief that

all students, regardless of any special needs, should receiveinstruction in regular classrooms with the non-disabled students.All necessary services are provided in that setting. To more effec-tively serve this population of students, most teachers are dualcertified in both general and special education. Mason convertedto pilot school status in 2003 and leveraged its autonomy over itsbudget and partnerships with community organizations and uni-versities to ensure there are at least two adults in every classroom.The strategies utilized by the school that were critical in its successinclude:

• Shared leadership structure focused on teacher quality andempowerment,

• Focus on students’ social and emotional needs and relationshipdevelopment,

• Strong commitment to families and community-building,• Data-driven instructional practices, and• An inclusion model structured around differentiated supportsfor special education students.

Because of the school’s pilot status, the principal has flexibilityin the curriculum and in the teacher selection process that allowsher to identify and hire faculty who fit the mission and culture ofthe school. The principal also provides teachers the opportunity togrow within their profession and take on additional roles andresponsibilities (Rennie Center for Education Research and Policy,2009). The Pilot School Work Election Agreement between teach-ers and the Mason School, moreover, provides for 80 hours of pro-fessional development annually, and 90 minutes of commonplanning time each week for teachers to collaborate and discusscurriculum, instructional strategies, and student work. Buildingupon efforts initiated by prior school leaders, the principal hasmaintained a culture in the school where teachers are integral tothe decision-making process and lead professional developmentfor all staff. As she explains:

When the teachers are leading [professional development], they seevalue in it. It also gives them a sense of empowerment. When they dowork here they are tapped to do work at the district level and it helpsthem grow . . . [having a leadership role in the school] can onlyenhance you as a teacher.

Mason Elementary was able to draw from and highlight its suc-cessful tradition of teacher involvement and empowerment to fuelits push toward overall academic excellence. A holistic approach tostudent support and development and the effective use of theinclusion model for students with special needs also differentiateMason from other similar institutions. Like BCLA, Mason alsoplaced great emphasis on both the importance of using currentdata to guide their instructional practices and community building.

Profile #3: Excel High School, 2007. Excel High School was estab-lished in 2001 as part of a high school restructuring initiative tocreate smaller, more personalized schools from underperformingcomprehensive high schools. The school is one of three createdfrom the former South Boston High School. In 2004, the incoming


Table 2: Mason Demographics: 2009–2010

Mason District

Total enrollment: 208 55,371African American: 51.4% 36.5%Asian: 1.0% 8.6%Hispanic: 31.3% 39.6%White: 11.1% 13.1%LEP*: 9.6% 20.4%Low Income: 71.2% 75.6%Special Education: 26.9% 19.6%Attendance Rate: 95.4% 91.2%*Limited English Proficient


4th Grade 2007 2008 2009

ELA** 97% 93% 100%Mathematics 94% 100% 84%

5th Grade 2007 2008 2009

ELA** 100% 100% 96%Mathematics 92% 81% 91%Science 96% 89% 94%


4th Grade 2007 2008 2009

ELA** 81.5 79.3 77.4Mathematics 85.5 88.8 71.8

5th Grade 2007 2008 2009

ELA** 89.0 87.0 84.4Mathematics 90.0 70.4 81.5Science 81.0 68.5 78.1**English Language Arts

headmaster worked with the staff to shift the focus of the schoolfrom a technology theme to college preparation. The school wasawarded the 2007 SOM Prize based on an impressive model ofteacher collaboration, an approach grounded in high expectationsand high student support, and a positive school climate that led tosignificant gains in student achievement.Teachers at Excel are organized into academic departments and

are provided common planning time to ensure both vertical andhorizontal alignment of the curriculum across all grade levels andsubjects. Department chairs, moreover, serve on the instructionalleadership team, which provides significant opportunities for col-laboration on issues of instruction and assessment.Since coming to the school, the headmaster has emphasized

teacher quality and focused her energy on evaluating teachers andproviding them with appropriate professional development andsupport. This effort allowed her to identify gaps in instructionalcompetency and align the school’s human resources in a way thatbetter served students. This process left the staff with a high levelof confidence in their ability to understand data and retoolinstructional strategies in a way that led to improved student per-formance. As described by the headmaster, Excel has “a faculty thatreflects on practice and takes action.”Excel’s success in winning the SOM Prize was predicated in

large part on maintaining consistent and high academic, civic, andbehavioral standards coupled with integrated supports that addressstudents’ academic and social/emotional needs. The school

ensures consistent communication with all students, maintains therigor and relevance of courses offered, and provides a number ofcredit recovery and after school programs to keep students ontrack, particularly in the 9th grade. Finally, the school has well-aligned support systems to coordinate services, communicate withparents, and keep students focused on their aspirations for collegeand career after high school (Rennie Center for EducationResearch and Policy, 2008).

Profile #4: Sarah Greenwood K–8 School, 2006. The Sarah Green-wood K–8 School has the distinction of being the inaugural SOMPrize winner, an honor that signaled a significant change in theschool and validated the leadership of its principal of 22 years.Improving student achievement has been an ongoing and at timesdifficult process that has seen the school maneuver through a tran-sition from a K–5 to a K–8 school, the adoption of a “dual lan-guage” curriculum through which all students learn in bothEnglish and Spanish, and movement toward an inclusion model forspecial education students (Rennie Center for Education Researchand Policy, 2007). With a population of Limited English Proficientstudents that is more than double the district average, Greenwood


Table 3: Excel Demographics: 2009–2010

Excel District

Total enrollment: 392 55,371African American: 33.9% 36.5%Asian: 31.4% 8.6%Hispanic: 20.9% 39.6%White: 12.8% 13.1%LEP:* 24% 20.4%Low Income: 73.5% 75.6%Special Education: 17.9% 19.6%Graduation Rate: 53.6% 61.4%Attendance Rate: 89.3% 91.2%*Limited English Proficient


10th Grade 2007 2008 2009

ELA** 94% 95% 94%Mathematics 94% 89% 90%Science n/a 81% 88%


10th Grade 2007 2008 2009

ELA** 83.3 83.1 84.3Mathematics 84.5 82.1 82.5Science n/a 61.8 70.5**English Language Arts

Table 4: Greenwood Demographics: 2009–2010

Greenwood District

Total enrollment: 374 55,371African American: 27.3% 36.5%Asian: 0.8% 8.6%Hispanic: 67.6% 39.6%White: 2.4% 13.1%LEP:* 45.2% 20.4%Low Income: 91.2% 75.6%Special Education: 18.7% 19.6%Attendance Rate: 93.1% 91.2%*Limited English Proficient


4th Grade 2007 2008 2009

ELA** 97% 92% 86%Mathematics 90% 95% 89%

8th Grade 2007 2008 2009

ELA** 100% 89% 100%Mathematics 74% 72% 62%Science 79% 50% 58%


4th Grade 2007 2008 2009

ELA** 79.0 76.4 68.9Mathematics 79.0 86.5 75.0

8th Grade 2007 2008 2009

ELA** 98.7 84.7 88.5Mathematics 67.1 63.9 58.7Science 53.3 44.4 43.3**English Language Arts

has had remarkable success in moving students to higher levels ofproficiency, particularly students in higher-grade levels. Despitechanges to the school’s structure, the staff has maintained a clearfocus on high expectations for student learning and, under theprincipal’s direction, has supported this goal through a number ofkey strategies: shared instructional leadership, data-driven deci-sion making, and integrated academic and social and emotionalsupports for all students. Greenwood uses a number of leadership teams to promote

school-wide collaboration; curriculum alignment; and instruc-tional practices, including content committees for literacy, mathe-matics, science, and social studies, and five grade-level studygroups. These teams ensure that everyone is involved in decision-making. They provide the school with the flexibility to adjustquickly to a variety of data indicators. Faculty members aretrained to build their capacity to analyze and discuss data in a waythat improves student achievement. The outcomes of these discus-sions, moreover, shape professional development. This focus oninstructional excellence is further supported by a range of supple-mental programs:

• Before and after school programs on literacy, mathematics, andMCAS preparation;

• Student Support Team composed of teachers, mental healthspecialists, nurses, counselors, and administrators to serve thewhole child; and

• Health services provided through a partnership with FranciscanChildren’s Hospital.

Overall, the themes of data-driven decision making and the philos-ophy of providing integrated academic and social/emotional sup-ports to the students have proven integral to Greenwood’s success.

Cross-Case Analysis of SOM Prize Winners, 2006–2009

Looking across all four SOM Prize winners, common themesemerge that have been integral to their success in addressing stu-dent academic performance and closing achievement gaps. Theseinclude an emphasis on shared leadership grounded in strong col-laborative structures and teacher empowerment, a focus on datato drive decisions about instructional practices and supplementaryservices, and a balance of high expectations for student successwith strong student support systems. Implementation of thesestrategies looks different from school to school, but their impor-tance to overall school success is evident to students, staff, andparents. Good structures and practices, however, are only part ofthe equation for school success. Perhaps most important is thequality of leadership and staff, and their commitment to continu-ous improvement.All these schools pay particular attention to teacher fit and

development and provide a variety of supports, including induc-tion, coaching, and differentiated opportunities for staff to growand take on additional responsibilities. Furthermore, each school’ssuccess in implementing a shared leadership model that both sup-ports and empowers teachers is directly linked to strong leadership

and commitment at the top. As a result, the faculty reports a highlevel of job satisfaction; a commitment to the values and mission ofeach individual school; and a willingness to put in extra hours, takerisks, and innovate. The smaller size of these schools also providesmore opportunity for individualized, student-centered instructionand social support. Additionally, all the schools benefited fromgoing through significant change in structure prior to winning thePrize, providing an opportunity to reflect on their values and lever-age additional resources.

Distributed Leadership. SOM Prize winners are committed to sharedaccountability for school success among all school leaders andstaff. Decisions regarding curriculum, instruction, and studentsupports are made collectively among teachers and administra-tors, fostering a shared sense of accountability for the implemen-tation of school-wide strategies to address overall learning goals.The literature on school reform has frequently mentioned the ben-efits of teacher collaboration and professional learning communi-ties in overall school improvement strategies (Stoll, Bolam,McMahon, Wallace, & Thomas, 2006; Vescio, Ross, & Adams,2008). Moreover, there is a growing body of empirical evidencethat suggests a positive relationship between high levels of teachercollaboration in schools and student achievement (Goddard, God-dard, & Tschannen-Moran, 2007; Herman et al., 2008; Louis,Lethwood, Wahlstrom, & Anderson, 2010; National Commissionon Teaching and America’s Future, 2010). In a recent survey ofteacher learning communities, Talbert (2010), for example, callsfor more emphasis on “professional strategies” for change, such asbuilding shared vision and leadership capacity that use “profes-sional expertise and knowledge resources, and leader modelingand feedback to engender change” (p. 561). These data suggest thatorganizational approaches to school improvement that build uponthe interdependence of staff within a school to address sharedgoals create a more coherent approach to meet student needs. As described by Hargreaves and Shirley (2009), the best collab-

orative environments feature “living communities and lively cul-tures dedicated to improving the lifelong learning of students andadults” (p. 92). These communities are characterized, in part, by“using quantifiable evidence and shared experience to inquire intoteaching and learning issues and to make judgments about how toimprove them” (p. 92). The two pilot schools that have won theSOM Prize, BCLA and Mason, have used their autonomy andteacher work agreements to create time for developing collabora-tive teacher relationships. Schedules are structured to create dailyopportunities to meet in school-wide, departmental, or grade-level teams. Learning communities provide a formal structure todiscuss curriculum and instructional approaches, student workand behavior, and common assessments. In these schools, commu-nity activity mirrors that described by Young (2008), in her analy-sis of collaborative decision-making. She argues that effectivecommunities help teachers develop common professional stan-dards toward both the types of questions teachers ask and thetypes of data they use. Further, these teams encourage communal


acceptance of responsibility for student learning and concern forteaching the “whole child.” Although they do not have the autonomies of pilot schools,

both Greenwood and Excel have found innovative ways to incor-porate the use of instructional leadership teams and commonplanning time to ensure a high level of teacher collaboration.Excel, for instance, has used grant money and other funds to payteachers stipends for extra time to engage in this work. Teacherteams also have input on budget decisions that have a direct impacton classroom instruction. These structures foster consistencyacross the curriculum and grade levels, and empower teachers toreflect more critically on their practices and how they align withthe overall goals of the school.

Data-driven Instruction. Critical to the success of teacher leadershipmodels in each of the winning schools is a stated commitment toteacher development. Each school leader focuses on teaching qual-ity and provides opportunities for staff to analyze and use data ina way that leads to instructional or curricular changes designed toimprove outcomes for students. In their overview of the literatureon data-driven decision-making, Mandinach and colleagues (2008)highlight that it is rare to find teachers engaged in “thinking criti-cally about the relationship between instructional practices andstudent outcomes” (p. 17). In SOM Prize winning schools, how-ever, discussions of data allow teachers to remain flexible and nim-ble to adjust to student needs by engaging in continual analysis ofstudent work and assessment data. Although data-driven discus-sions may exclude deeper theoretical conversations about studentlearning or effective teaching (Datnow, Park, & Kennedy, 2008),there is a strong culture among the SOM schools to continuallyimprove their skills and strategies to bring more clarity and con-sistency to how they use data to make decisions. While this can bedifficult for many teachers who are focused on the day-to-day rig-ors of instruction, it provides more opportunities for reflection onthe ultimate objective—student learning. As stated by Excel’sheadmaster, “[w]e are always looking for that new perspective on studentdata that will get us to think about teaching and learning in a new way.”A good example of this is BCLA’s recent shift to school-wide

authentic assessments based on student portfolios and exhibitions.Determining how to analyze this rich information has been a chal-lenge, but teachers see the value in exploring alternative ways toevaluate students’ work and academic growth. In interviewresponses, teachers noted that they have found portfolios andexhibitions particularly helpful in assessing special education stu-dents and improving strategies to support these students. Withingrade-level teams at Mason and Greenwood, moreover, teachersuse a wide range of formative assessments, including focusedattention on guided reading and writing assignments, and themathematics assessments that are given multiple times a year.Teachers spend a considerable amount of time and energy toensure that the scoring rubrics they use to guide formative assess-ment are consistent and aligned to grade-level standards andexpectations. It is not uncommon in these schools for first-grade

mathematics teachers to be discussing fourth-grade mathematicsto determine how to better align instruction to build the rightfoundation for successful grade-to-grade progression.Excel developed a comprehensive data inventory that outlines

the types and purposes of the data the school collects. The inven-tory includes ten different types of assessments, including school-based, district, and state assessments, as well as data collected onfifteen different indicators from attendance to frequency of visitsto the school nurse. According to the headmaster, analyzing allthese data has expanded the school’s perspective, not only on howindividual students are doing, but also on why students may or maynot be achieving at a high level. Excel has been particularly effec-tive at disaggregating data for certain subgroups of students to cre-ate instructional practices that were developed to help closeachievement gaps. Specifically, Excel was able to successfullyaddress two key issues in its school improvement plan—mathe-matics proficiency among African-American males and ELA profi-ciency among the school’s large population of Vietnamesestudents—through focused, data-driven instructional change. Byfocusing on both academic and non-academic indicators of studentprogress, Excel can better ensure that its academic support pro-grams are responsive to students’ unique needs.

Student-centered Learning. The BCLA headmaster expressed a senti-ment common to all winning schools when she said, “[y]ou cannothave high standards just for the sake of having high standards, withouthigh levels of support.” High academic expectations and effectivesupport systems have long been seen by educators as essential toschool success, particularly in urban areas (Payne, 2008; Pianta &Allen, 2008). Research suggests that high academic expectationsprovide direction and motivation for students to attain goals andview themselves as intellectual learners (Payne, 2008; Shapiro,1994). Further, research notes that knowledge of youth develop-ment can help teachers to form motivation-producing relation-ships with students that “engage and motivate them to learn anddevelop personally” (Pianta & Allen, 2008, p. 23). Social support builds motivation by creating a sense of trust,

confidence, and emotional connectedness, and can help studentsmaneuver through the developmental changes of childhood.Increasingly, researchers focusing on education and youth develop-ment see value in balancing these two elements as a strategy toimprove student achievement and student engagement, particu-larly among high-need urban populations (Blum, 2005; Lee,Smith, Perry, & Smylie, 1999).As an example, Charles Payne’s (2008) Authoritative-Support-

ive Model of teaching calls upon teachers to approach their peda-gogy with high levels of both “social support” and “academicpress.” Authoritative-Supportive teaching has four main compo-nents: high level of intellectual/academic demand, high level ofsocial demand, holistic concern for children and their future, anda strong sense of teacher efficacy and legitimacy. Payne’s ownresearch has shown that students who have learned under theseconditions have done remarkably well. A look across the SOM


Prize winners demonstrates that each takes a unique approach toimplementing the Authoritative-Supportive model. As high schools, both Excel and BCLA reinforce high academic

and social expectations through a mission and vision that empha-size achievement and college readiness. These expectations arecommunicated to students through multiple methods—dialoguewith teachers and other staff, a rigorous college preparation cur-riculum that includes Advanced Placement (AP) courses, andstrong counseling services to provide students information andsupport for post-secondary success. As one BCLA student put it,teachers “begin to talk to you about college in ninth grade.”These mes-sages are reinforced at all levels of the school. BCLA communityleaders and Excel student development counselors play an activerole in setting expectations as well as monitoring students’ aca-demic progress, engaging parents, and linking students to addi-tional services that support academic success and collegereadiness. These expectations are supported by a wide range of ini-tiatives, including before and after school enrichment, targetedtutoring, and counseling services. As a result, both schools areamong the top selections of Boston students who aspire to go onto college after high school. As noted by Excel’s headmaster,

Even though we have students who are not well prepared even for high school, we have a vision here that is very high, and wehave come up with solutions to support students . . . we can makethem high school material, we can make them college materialwhile at the same time being good human beings because we areexposing them to different things they are not being exposed to intheir communities.

At Greenwood and Mason, high expectations for studentachievement are balanced with a strong emphasis on students’social and emotional development. Both schools rely on what theprincipal calls the “emotional part of the data,” to gain a deeper under-standing of individual students’ needs to better target supports andinterventions (Rennie Center for Education Research and Policy,2007). Teachers constantly work to engage students on multiplelevels and provide differentiated opportunities to learn. Green-wood utilizes a student support team to provide counseling serv-ices, referrals, and other supports to students with a wide range ofsocial, behavioral, and developmental issues. Mason addresses thesesame issues through an ongoing partnership with City Connects, aBoston College initiative that works with schools to better coordi-nate in- and out-of-school supports for students. By creating safetynets and other mechanisms to address the “whole child,” theseschools provide students with a variety of individualized supportsto move them to higher levels of achievement. In all the winning schools, strong leadership from principals

ensures that the focus on high expectations is centered as much onthe staff as it is on the students. At Mason, this is expressed as the“Mason Way”—an understanding that all teachers and staff striveto provide all students with the opportunities to be successful inschool and life (Rennie Center for Education Research and Policy,2009). It is a commitment and dedication to high expectations that

shapes the culture of these schools, and it attracts professionalswho share that commitment. For the headmaster of BCLA,teacher commitment to students is integral to their commitmentto each other and the school. As she observes, “Unless all staff careabout the children, there will be no community among the staff.” Highexpectations and support also extend to families and communities.These schools work hard to provide parents and caregivers withinformation and other services that will help them support theirchildren’s academic success.

IMPLICATIONS: LESSONS FROM THE LEADERS

The SOM Prize created opportunities for winning schools toreflect upon their practices and make investments to help themgrow as communities and build upon their successes. For Excel’sheadmaster, the process was also a valuable learning experience,

It brought a different sense that this is a success. Teachers began tosee that they had something to do with the success. [The Prize also]brings a lot of reflection of practice. Winning the award made uslook at the data more critically. What does it mean to improve con-tinuously and how do you sustain that level of achievement?

Through this reflection, school leaders identified a number of keylessons that they view as critical to implementing the strategiesand practices described in this study that have improved studentoutcomes:

• Open and honest communication is critical. School leadersmust be transparent with everyone about where they are andwhere they want to go as a school community.

• School leaders must create clear expectations for all membersof the school community (e.g., students, teachers, staff, andparents). Without buy-in from all levels, success will be elusive.

• Fostering balanced leadership is key. It is essential to provideeveryone with an opportunity to contribute and to have a realstake in school leadership, but school leaders must also knowwhen to step in and make a decision.

• School leaders must be in classrooms as often as possible andtalk to students about what they are doing. School leaders mustsee firsthand that their students understand the objectives ofthe work.

• Teachers are key and need to be supported in comprehensiveways.

• School climate and culture are the most effective pathways tostudent engagement.

• School leaders should challenge assumptions and not jump toconclusions, particularly in regard to special education studentsand students who are English Language Learners. Knowing theresearch is vital.

Winning the SOM Prize has also provided the four schoolsdescribed here with a unique opportunity to celebrate their suc-cess within the broader Boston school community. Excel’s head-master observed,


It was amazing. It recognized the hard work of the teachers and thestudents. It brought some pride to the community, saying we arereally working, and the work is being displayed outside the Excelwalls. It was beautiful.

For Greenwood’s principal, the reaction was similar:

Students felt that they were doing well and that we are doing wellin Boston. We may have to walk the streets, but when we get to schoolwe are learning. It felt like that for the parents too. It felt like upliftfor the entire community.

CONCLUSION

Amidst the constant challenges of urban public education, theSchool on the Move Prize rewards and brings well-earned attentionto schools that are improving outcomes for urban students. Thethree common strategies employed by all of the winning schools:1) shared leadership and meaningful collaboration, 2) data-drivendecision making, and 3) high expectations for academic successenabled by intense and student-centered academic and social/emo-tional supports are validated by a growing body of research onschool reform (e.g., Acker-Hocevar et al., 2012; Blum, 2005; Leeet al., 1999). The winning schools are clearly unique places oflearning where a confluence of strong leadership, skilled teachers,and high expectations led to impressive gains in student achieve-ment. Yet, the strategies employed by these schools are not depend-ent upon special circumstances. These are strategies that otherschool leaders and educators can incorporate into their ownschools to improve the opportunities for all students to be success-ful learners who are well-prepared for college, career, and life.

References

Acker-Hocevar, M. A., Cruz-Janzen, M. I., & Wilson, C. L. (2012). Lead-ership from the ground up: Effective schooling in traditionally low performingschools. Charlotte, NC: Information Age Publishing.

Blum, R. (2005). School connectedness: Improving the lives of students. Balti-more, MD: Johns Hopkins Bloomberg School of Public Health.

Brantlinger, E., Jimenez, R., Klinger, J., Pugach, M., & Richardson, V.(2005). Qualitative studies in special education. Council for ExceptionalChildren, 71(2), 195–207.

Coffey, A., & Atkinson, P. (1996). Making sense of qualitative data: Comple-mentary research strategies. Thousand Oaks, CA: Sage Publications.

Datnow, A., Park, V., & Kennedy, B. (2008). Acting on data: How urban highschools use data to improve instruction. Los Angeles, CA: University ofSouthern California: Center on Educational Governance.

Ferguson, R. F. (2005). Toward skilled parenting and transformed schools:Inside a national movement for Excellence with Equity. Paper presented atthe Wiener Center for Social Policy, Harvard University: October 21,2005.

Goddard, Y. L., Goddard, R. D., & Tschannen-Moran, M. (2007). A the-oretical and empirical investigation of teacher collaboration forschool improvement and student achievement in public elementaryschools. Teachers College Record, 109(4), 877–896.

Hargreaves, A., & Shirley, D. (2009). The fourth way.Thousand Oaks, CA:Corwin Press.

Herman, R., Dawson, P., Dee, T., Greene, J., Maynard, R., Redding, S.,& Darwin, M. (2008). Turning around chronically low-performing schools:A practical guide. Washington, DC: National Center for EducationEvaluation and Regional Assistance, Institute for Education Sciences,US Department of Education.

Lee, V., Smith, J. B., Perry, T. E., & Smylie, M. A. (1999). Social support,academic press, and student achievement: A view from the middle grades inChicago. Chicago, IL: Consortium on Chicago School Research.

Louis, K. S., Leithwood, K., Wahlstrom, K. L., & Anderson, S. E.(2010). Investigating the links to improved student learning. Final Reportof the Learning from Leadership Project, Minneapolis, MN: Univer-sity of Minnesota.

Mandinach, E. B., Honey, M., Light, D., & Brunner, C. (2008). A con-ceptual framework for data-driven decision making. In E. B. Mandi-nach & M. Honey (Eds.), Data-driven school improvement: Linking dataand learning (pp. 13–31). New York, NY: Teachers College Press.

Massachusetts Department of Elementary and Secondary Education.(2006). School Leaders’ Guide to the 2006 Cycle IV Accountability and Ade-quate Yearly Progress (AYP) Reports. Retrieved from http://www.doe.mass.edu/news/news.aspx?id=2995.

National Commission on Teaching and America’s Future. (2010). Team upfor 21st century teaching and learning: What research and practice revealabout professional learning. Washington, DC: National Commission onTeaching and America’s Future.

Payne, C. (2008). So much reform, so little change: The persistence of failure inurban schools. Cambridge, MA: Harvard Education Press.

Pianta, R. C., & Allen, J. P. (2008). Building capacity for positive youthdevelopment in secondary school classrooms: Changing teachers’interactions with students. In M. Shinn & H. Yoshikawa (Eds.), Towardpositive youth development: Transforming schools and community programs(pp. 21–39). New York, NY: Oxford University Press.

Rennie Center for Education Research and Policy (2007). Continuouseffort, continuous improvement: Student achievement at the Sarah GreenwoodSchool. Cambridge, MA: Rennie Center for Education Research andPolicy.

Rennie Center for Education Research and Policy. (2008). A Focus onachievement at Excel High School: A best practices case study. Cambridge,MA: Rennie Center for Education Research and Policy.

Rennie Center for Education Research and Policy. (2009). Samuel W.Mason Elementary School case study. Cambridge, MA: Rennie Center forEducation Research and Policy.

Rossman, G. B., & Rallis, S. F. (2003). Learning in the field: An introductionto qualitative research (2nd ed.). Thousand Oaks, CA: Sage Publications.

Shapiro, B. L. (1994). What children bring to light: A constructivist perspec-tive on children’s learning in science. New York, NY: Teachers CollegePress.

Stoll, L., Bolam, R., McMahon, A., Wallace, M., & Thomas, S. (2006).Professional learning communities: A review of the literature. Journalof Educational Change, 7(4), 221–258.

Stotko, E. M., Ingram, R., & Beaty-O’Ferrall, M. E. (2007). Promisingstrategies for attracting and retaining successful urban teachers. UrbanEducation, 42(1), 30–51.

Talbert, J. E. (2010). Professional learning communities at the cross-roads: How systems hinder or engender change. In A. Hargreaves, A.Lieberman, M. Fullan, & D. Hopkins (Eds.), The second internationalhandbook of educational change, (Vol. 23, pp. 555–571). New York, NY:Springer Publishing.


Vescio, V., Ross, D., & Adams, A. (2008). A review of research on theimpact of professional learning communities on teaching practice andstudent learning. Teaching and Teacher Education, 24(1), 80–91.

Yin, R. K. (2008). Case study research: Design and methods. Thousand Oaks,CA: Sage Publications.

Young, V. M. (2008). Supporting teachers’ use of data: The role of organ-ization and policy. In E. B. Mandinach & M. Honey (Eds.), Data-drivenschool improvement: Linking data and learning (pp. 87–106). New York,NY: Teachers College Press.

Authors’ Notes

The authors would like to recognize the work of Angelina Hong inpreparing this article for publication.

This material is based upon work supported by EdVestors. Any opinions,findings, and conclusions or recommendations expressed in this materi-als are those of the authors and do not necessarily reflect the views ofEdVestors.

Dr. Chad d’Entremont is the Executive Director of the Rennie Center forEducation Research & Policy in Cambridge, MA. Dr. d’Entremont can bereached at [email protected].

Jill Norton is an independent policy consultant and the former ExecutiveDirector of the Rennie Center for Education Research & Policy.

Michael Bennett is the Managing Editor of Catholic Education: A Journal ofInquiry and Practice at the Lynch School of Education, Boston College.

Peter Piazza is a doctoral student in Curriculum and Instruction at the LynchSchool of Education, Boston College.


ABSTRACT

Computer simulation analysis was used to illustrate the thesis thatthe typical American public school is structured in a way that rein-forces the entry characteristics of its students so that by the timethey graduate—if they graduate—after twelve years, studentswho enter the school in kindergarten or first grade with high“readiness” perform academically better-than-average, while stu-dents who enter the school with low “readiness” perform worsethan average, thus creating the well-known and widely discussed“achievement gap.” The conclusion of this argumentation is that this structure must

be changed if school reform is to be effective, and that it is strongschool leadership that, over time, builds teacher quality and com-munity and parent interest in the school, and changes teacherexpectations for all students, especially those who are initially andtraditionally low-achieving. The position taken in the paper, and supported by the computer

simulation modeling data, is that, in this way, strong school leader-ship enhances the quality and intensity of instruction, the closenessof student-teacher relationships, and the rigor of instructional con-tent for all students, resulting in further advances in student moti-vation, work effort, and academic performance, and, finally,improving the attractiveness of the school for high-quality teachersand continuing the upward cycle.

INTRODUCTION

In this article I renew and expand upon an argument that KarlClauset and I first made some thirty years ago (Clauset & Gaynor,1982). The main point of this argument is that the typical Ameri-can public school is structured in a way that reinforces the entrycharacteristics of its students so that by the time they graduate—ifthey graduate—after twelve years, students who enter the schoolin kindergarten or first grade with high “readiness” perform aca-demically better-than-average, while students who enter the schoolwith low “readiness” perform worse than average, thus creating thewell-known and widely discussed “achievement gap” (see Figure 1).School readiness includes such factors as high levels of English

language development and self-discipline; high levels of academicmotivation based on high aspirations for life achievement; and astrong cultural belief in the empowering role of education inachieving these aspirations. School readiness also includes having

the kinds of “intelligence” and learning styles that are consistentwith standard schooling and standard models of academicinstruction. The central thesis of this article is that this outcome is driven

fundamentally by three central structural elements—self-rein-forcing causal feedback loops—one mainly between teachers andstudents around teacher expectations (Clauset & Gaynor, 1982;Rist, 1973), another between student performance in a school andthe attractiveness of the school for high-quality teachers (Betts,Rueben, & Danenberg, 2000; Bonesrønning, Falch, & Strøm,2005; Clotfelter, Ladd, & Vigdor, 2006; Lankford, Loeb, & Wyck-off., 2002; Peske & Haycock, 2006), and the third between stu-dent performance and the level of school funding (Klein, 2011).(See Figure 2.)

Different Students: How Typical Schools Are Built to Fail and Need to Change: A Structural Analysis

alan kibbe gaynor, boston university

13

Low Initial Readiness Grade Level of Achievement

–5

0

2 3 4 5 6 7 8 9 10 11 12 13

5

10

15

Average Grade Level of Achievement

1

Figure 1. The Typical School Achievement Gap between Average and Low Initial Readiness Students

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+Average Student Academic Effort

Average Student Academic Motivation

Teacher Expectationsfor Student Academic

Performance

Quality and Intensity of Instruction

Average Student Academic

Performance

Level of SchoolFunding

TeacherQuality

Attractiveness of the School for Teacher

Recruitment

Figure 2. Endogenous Self-Referencing Feedback Loops That Drive the Achievement Gaps in Schools


Ratio of SAPto Grade Level

Attractiveness of the School to

Recruiting

Comparative Levelof Community

Resources

Teacher Quality

StudentPersonal

Intelligence

Quality & Intensity of Instruction

Quality & Closeness of Teacher-Student

RelationshipsExtra Help for

UnderachievingStudents

Intensity & Quality ofSupervision, Professional

Development, andRecruitment & Selection

of Teachers

Parental Pressure from Student

Underachievement

Community andParent Interest in

the School

Quality of School

Leadership

Community Efforts toImprove the Quality of

School Leadership

StudentAcademicMotivation

Student Effortin School

StudentAcademic

AchievementDropout

Rate

Ratio of Out-of-School

Suspensions to NormalRatio of StudentAchievement to

Normal Grade LevelExpectations

StudentResilience

Teacher Perceptionof Student Ability and Motivation

Normal GradeLevel

Expectations

Level of School Funding

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(+)

(+)(–)(–)

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(+)(+)

(+)

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Figure 3. Structural Context of Schooling

Quality of School Leadership

Teacher Effort to HelpUnderachieving Students

Interest of Sch Ldshp inUnderachieving Student Help

Parental Pressure fromStudent Underachievement

Effect of PerformanceRatio on Parent Pressure

Effect of SAP onTeacher Recruitment

Effect of SAP onResources for Schooling

Expected ASAP

Effect of QSL on RCEAASAP

Rate of Change in LCLIS

Effect of QSL on LCLIS

Community & ParentInterest in the School

Leadership Change Multiplier

Effect of LCM Multiplier onthe Rate of Leadership Change

Rate of Increase inStudent’s True Grade Level

Student’s True Grade Level

Ratio of Out of SchoolSuspensions to Normal

Change in SAM Multiplier

Effect of Various Factorson Changes in Motivation

Ratio of SAP to STGL

Effect of QSL on TPSA&MQuality & Closeness ofStudent Teacher Relationship

Student Personal Intelligence

Teacher Perception ofStudent Ability & Motivation

Average StudentAcademic Performance

Rate of Increase in SAP

Student Effort in School Quality & Intensity of Instruction

Effect of Student Resilienceon Student Effort in School

Effect of Extra Help on Student Effort

Teacher Quality

Rate of Change inTeacher Quality

Effect of TeacherQuality on Content Rigor

Effect of TQCM on RCTQ

Ratio of ASAP to Expected ASAP

Extra Help forUnderachieving

Students

Student Resilience

Content Rigor

Teacher Quality Change Multiplier

Effect of AQTR&S on the TeacherQuality Change Multiplier

Average Quality of TeacherRecruitment and Selection

Comparative Level of CommunityResources for Schooling

Effect of AQPDA Multiplier

Effect of School Leadership onTeacher Recruitment and Selection

Rate of Change in the Quality of

School LeadershipEffect of LCLIS on

Community Resources for Schooling

Quality of School Leadership

AQPDA Multiplier

Effect of PD on TeacherQuality Change

Amount and Quality of Supervision& Prof Development Activities

Effect of TQ on Q&II

Effect of Content Rigor on Q&II

Effect of SAP to STGLRatio on TPSA&M

Effect of Pers Intell on theQuality & Intensity of Instruction

Student Academic Motivation

Rate of Increase in SAMQuality of School Leadership

Effect of School Leadershipon Content Rigor

Teacher Quality

Community & ParentInterest in the School

Ratio of SAP to STGL

Average StudentAcademic Performance

Figure 4. The School Simulation Model

I believe that it is precisely this dysfunctional feedback structurethat must be changed if school reform is to succeed. In the follow-ing pages I present the results of several computer simulations thatinclude the essential elements of a school—shown first as a causal-loop diagram (Figure 3) and then as a full-blown System Dynamicscomputer-simulation model (Figure 4).These simulations first display the results of the basic model,

described above, and then examine the effects on the system ofhigher teacher quality, school leadership, and community-parentinterest in the school. Finally, I examine the effects of studentcharacteristics that are widely alleged to be important, especiallyfor initially and traditionally low-achieving students: personalintelligence (Gardner, 2011; Goleman, 2006) and resilience(Allen, 2004; Brown, 2004; Carnes, 2009; Coleman, 2007; Craw-ford, 2006; Marshall, 2008; Nears, 2007; Salley, 2005). For thisarticle, in all cases, the emphasis is on the effects of these changeson initially low-achieving students.The equations and table functions that specify the model are

displayed in Appendix A. Appendix B contains a list of many of thenon-school developmental factors that have been tied to differen-tial academic achievement. Appendix C lists an extensive catego-rized bibliography of sources related to the factors listed inAppendix B.

RESULTS

The Basic Dynamics of a Typical School

The first set of simulation experiments tested the effects of thebasic dynamics discussed earlier in this paper (see Figure 1) thatdrive the problematic reference mode shown in Figure 2. In theseruns, the only changes made are to the initial level of “Student Aca-demic Performance” so as to represent these initial differencesmathematically in the model. All other variables in the model areheld constant with values that represent a typical school.It should be noted at this point that student academic perform-

ance is shown in real physical units: the grade-level correspon-dence of the student’s actual academic performance vs. theexpected grade-level performance (i.e., the student’s “true” age-correspondent grade level). Other variables in the model areshown as what is called “dimensionless.” They are, in essence,scaled values, with “1” as the “normal” value and with higher andlower values showing proportionately greater and lesser values.Thus, in the basic simulation runs, the other variables all are ini-tialized at “1.” In subsequent runs “high” values of teacher quality,school leadership, student resilience, etc. are initialized as “1.2”whereas “low” values are initialized as “0.8.”

The Effects of the Typical (Baseline) School on Studentswith Average Entry Characteristics

Figure 5 shows the typical progress of an initially average studentover twelve years in a typical school. It is evident that the student’sacademic progress tracks her or his age-grade-level.

The Effects of the Typical (Baseline) School on Studentswith Above-Average Entry Characteristics

Figure 6 shows the typical progress of an initially above-averagestudent over twelve years in a typical school. It is evident that thestudent’s academic progress tracks well above age-grade-level.

15A S T R U C T U R A L A N A LY S I S

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1: Student’s True Grade Level 2: Average Student Academic Performance

Figure 5. Model Output: The Academic Progress of an Initially Average Student in Comparison to Normal

Grade-Level Progression in a Typical School

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1:2:

1:2:

1:2:

10

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20


Years

Figure 6. Model Output: The Academic Progress of an Initially Above-Average Student in Comparison to Normal



The Effects of the Typical School on Students withBelow-Average Entry Characteristics

Figure 7 shows the typical progress of an initially below-averagestudent over twelve years as a student in a typical school. It is evi-dent that the student’s academic progress lies consistently belowthat expected for her or his age-grade-level, which is the essenceof the so-called “achievement gap.”

Tests of Experimental Effects

Tests were run to determine—given the structure of the modeland the theory of the structure of schooling represented in it—the effects of improvements in different elements of schooling oninitially below-average students.

High levels of the following elements were graphed:

• teacher quality, • school leadership and interest in school on the part of parentsand community leaders,

• student personal intelligence, and• individual student resilience.

Combinations of the following elements were also graphed:

• high teacher and school leadership quality, • high teacher quality and a high level of interest in school on thepart of parents and community leaders,

• high level of school leadership and a high level of interest inschool on the part of parents and community leaders,

• high-quality teaching, and• a high level of school leadership and a high level of interest inschool on the part of parents and community leaders.

These model effects are shown in a series of graphs (Figures8–16).

1.00 4.00 7.00 10.00 13.00

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Years

Figure 8. Effects of High Teacher Quality on Initially Below-Average Students

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Figure 9. Effects of a High Level of School Leadership on Initially Below-Average Students

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Figure 10. Effects of a High Level of Community and ParentInterest in School on Initially Below-Average Students

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Figure 7. Model Output: The Academic Progress of an Initially Below-Average Student in Comparison to Normal



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Figure 13. Effects of a High Combination of School Leadership and Community and Parent Interest on Initially

Below-Average Students

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10

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Figure 14. Effects of a High Combination of Teacher Quality,School Leadership, and Community Leader Interest on

Initially Below-Average Students

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10

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Figure 15. Effects of High Student Personal Intelligence on Initially Below-Average Students

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Figure 16. Effects of High Student Resilience on Initially Below-Average Students

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Figure 11. Effects of a High Combination of Teacher Quality andSchool Leadership on Initially Below-Average Students

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Figure 12. Effects of a High Combination of Teacher Quality andCommunity Parent Interest on Initially Below-Average Students

CONCLUSIONS

Given the structure of the model as formulated, high teacher qual-ity, school leadership, and community and parent interest inschools all have quite significant effects on the academic perform-ance of initially low-achieving students, to the point of closing thegap with grade level standards. The combinations of pairs of thesevariables have even greater effects, and the greatest effects areachieved by the combination of all three of these variables. The effects of student personal intelligence and resilience were

tested as well. It is important to note, however, that these variablesare exogenous to the structure of the school, residing in the stu-dents as personal characteristics. However, they are often men-tioned as important characteristics, especially for initiallylow-achieving students. Student interpersonal intelligence is posited to influence posi-

tively both the closeness of teacher-student relationships, which,in turn, affects student motivation and work effort, and the qual-ity and intensity of instruction, which have a direct positive impactupon academic performance and an indirect positive effect uponstudent motivation and work effort. Student resilience is posited to have positive effects for low-

achieving students on their work effort, which affects their academicperformance and, in turn, their motivation, teacher expectations,and further work effort. Both student interpersonal intelligence andresilience have positive effects on the achievement of initially low-achieving students, helping to bring their achievement up to averagelevels or beyond, at least given the theory described above of howthey interact with other variables in the school.

IMPLICATIONS

Since the results presented are simulation results and, therefore,are theoretical, not empirical, the implications of these findingsare twofold. First, to the extent that the structure of the model isviewed as sound—including the configuration of variables andcausal interactions and the proposed “effect sizes” represented inthe equations and table functions in Appendix A—the results con-firm the importance of teacher quality and school leadership,especially when joined with the level of interest in schools of par-ents and the community. While teacher quality in the model has slightly greater effects

than school leadership, it seems important to keep in mind thatchanges in the overall level of teacher quality probably cannot beachieved in the real world without strong school leadership—through the effects of leadership on the recruitment and selectionof teachers and on professional development, instructional super-vision, and the rigor of the content presented, especially to low-achieving students. In the same way, community and parentinterest in schools is probably essentially what in the world ofresearch is called a fixed effect, amenable to deliberate strategicinitiatives only in the long term—by improving the school incre-mentally over time, which, again in my view, seems cruciallydependent on strong school leadership.

With this in mind, it seems that in the real world the mostimportant variable amenable to purposeful policy action is schoolleadership. The implications for the selection, recruitment, andpreparation of a pool of both high-quality teachers and strongschool leaders seem evident. Second, the model provides a theoretical foundation for further

empirical research. There is a need for a careful examination byscholars of the structure of the model to assess its validity as a rep-resentation of the critical interactions that affect student academicperformance, for all students but particularly for initially low-per-forming students.Finally, there is a need for experimental research to test for the

empirical significance and size of the causal effects among thepaired variables in the model, almost all of which are currentlyempirically unconfirmed. To put it another way, each of theparameters in the model (see model equations in Appendix A) rep-resents an object of potential experimental research.Thus, the model lays out a potential research agenda for those

interested in the existing socio-economic, racial, and ethnicachievement gaps.

References

Allen, D. L. (2004). An examination of the relationship between teachers’ per-ceptions of their school’s ability to foster a culture of resilience and studentoutcomes on the Ohio Sixth Grade Reading Proficiency Test (Unpublisheddoctoral dissertation). The University of Cincinnati, Cincinnati, OH.

Betts, J., Rueben, K., & Danenberg, K. (2000). Equal resources, equal out-comes? The distribution of school resources and student achievement in Cali-fornia. San Francisco, CA: Public Policy Institute of California.

Brown, A. P. (2004). Learning environment and attitudinal differences betweenresilient, average, and non-resilient fourth- and fifth-grade Hispanic students.(Unpublished doctoral dissertation). University of Houston, Hous-ton, TX.

Bonesrønning, H., Falch, T., & Strøm, B. (2005). Teacher sorting, teacherquality, and student composition. European Economic Review, 49,457–483.

Carnes, S. J. (2009). Resilience in action: A portrait of one high-poverty/high-performing school (Unpublished doctoral dissertation). Aurora Univer-sity, Aurora, IL.

Clauset, K. H., Jr., & Gaynor, A. K. (1982). Effective schooling: A sys-tems perspective. Educational Leadership, 40(3), 54–59.

Clotfelter, C., Ladd, H, & Vigdor, J. (2006). Teacher-student matchingand the assessment of teacher effectiveness. Journal of HumanResources, 41(4), 778–820.

Coleman, H. L. K., (2007). Minority student achievement: A resilientoutcome? In D. Zinga (Ed.), Navigating multiculturalism: Negotiatingchange (pp. 296–326). Newcastle upon Tyne, UK: Cambridge Schol-ars Publishing.

Crawford, K. M. (2006). Risk and protective factors related to resilience inadolescents in an alternative education program (Unpublished doctoral dis-sertation). University of South Florida, Tampa, FL.

Gardner, H. (2011). Frames of mind: The theory of multiple intelligences.New York, NY: Basic Books.

Goleman, D. (2006). Social intelligence: The revolutionary new science ofhuman relationships. New York, NY: Bantam Books.


Klein, A. (November 30, 2011). Poor schools shortchanged on funding,Education Department says. Education Week. Retrieved fromhttp://www.edweek.org/.

Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and theplight of urban schools: A descriptive analysis. Educational Evaluationand Policy Analysis, 24(1), 37–62.

Marshall, M. P. (2008). Overcoming the odds: An interpretive phenomenologi-cal analysis of academic resilience among urban young adults (Unpublisheddoctoral dissertation). Wright Institute, Berkeley, CA.

Nears, K. (2007). The achievement gap: Effects of a resilience-based after schoolprogram on indicators of academic achievement (Unpublished doctoral dis-sertation). North Carolina State University. Raleigh, NC.

Peske, H., & Haycock, K. (June 2006). Teaching inequality: How poor andminority students are shortchanged on teacher quality. Washington, DC:The Education Trust.

Rist, R. C. (1973). The urban school: A factory for failure. Cambridge, MA:MIT Press.

Salley, L. D. (2005). Exploring the relationship between personal motivation,persistence, and resilience and their effects on academic achievement amongdifferent groups of African-American males in high schools (Unpublisheddoctoral dissertation). University of Maryland, College Park, MD.

Alan Kibbe Gaynor is an associate professor at Boston University. ProfessorGaynor can be reached at [email protected].


APPENDIX A. MODEL EQUATIONS AND TABLE FUNCTIONS

Average_Student_Academic_Performance(t) = Average_Student_Academic_Performance(t - dt) + (Rate_of_Increase_in_SAP) * dt

INIT Average_Student_Academic_Performance = .8

INFLOWS:

Rate_of_Increase_in_SAP = Quality_&_Intensity_of_Instruction * Student_Effort_in_School

Community_&_Parent_Interest_in_the_School(t) = Community_&_Parent_Interest_in_the_School(t - dt) +(Rate_of_Change_in_LCLIS) * dt

INIT Community_&_Parent_Interest_in_the_School = 1

INFLOWS:

Rate_of_Change_in_LCLIS = Effect_of_QSL_on_LCLIS * Community_&_Parent_Interest_in_the_School

Quality_of_School_Leadership(t) = Quality_of_School_Leadership(t - dt) +(Rate_of_Change_in_the_Quality_of_School_Leadership) * dt

INIT Quality_of_School_Leadership = 1

INFLOWS:

Rate_of_Change_in_the_Quality_of_School_Leadership = Effect_of_LCM_Multiplier_on_the_Rate_of_Leadership_Change *Quality_of_School_Leadership

Student’s_True_Grade_Level(t) = Student’s_True_Grade_Level(t - dt) + (Rate_of_Increase_in_Student’s_True_Grade_Level) * dt

INIT Student’s_True_Grade_Level = 1

INFLOWS:

Rate_of_Increase_in_Student’s_True_Grade_Level = 1

Student_Academic_Motivation(t) = Student_Academic_Motivation(t - dt) + (Rate_of_Increase_in_SAM) * dt

INIT Student_Academic_Motivation = 1

INFLOWS:

Rate_of_Increase_in_SAM = Student_Academic_Motivation * Effect_of_Various_Factors_on_Changes_in_Motivation

Teacher_Quality(t) = Teacher_Quality(t - dt) + (Rate_of_Change_in_Teacher_Quality) * dt

INIT Teacher_Quality = 1

INFLOWS:

Rate_of_Change_in_Teacher_Quality = Effect_of_TQCM_on_RCTQ/DELAY(3,0)

Amount_and_Quality_of_Supervision_&_Prof_Development_Activities = Effect_of_AQPDA_Multiplier

AQPDA_Multiplier = Comparative_Level_of_Community_Resources_for_Schooling * Quality_of_School_Leadership

Average_Quality_of_Teacher_Recruitment_and_Selection = Comparative_Level_of_Community_Resources_for_Schooling *Effect_of_School_Leaderfship_on_Teacher_Recruitment_and_Selection * Effect_of_SAP_on_Teacher_Recruitment

Change_in_SAM_Multiplier = (Quality_&_Closeness_of_StudentTeacher_Relationship + Quality_&_Intensity_of_Instruction +1/Ratio_of_Out_of_School_Suspensions_to_Normal + Ratio_of_SAP_to_STGL) / 4

Comparative_Level_of_Community_Resources_for_Schooling = Effect_of_LCLIS_on_Community_Resources_for_Schooling

content_rigor = Effect_of_School_Leadership_on_Content_Rigor * Effect_of_Teacher_Quality_on_Content_Rigor

Effect_of_Performance_Ratio_on_Parent_Pressure = IF(Ratio_of_SAP_to_STGL<1)THEN(1/Ratio_of_SAP_to_STGL)ELSE(1)

Expected_ASAP = Average_Student_Academic_Performance * Effect_of_QSL_on_RCEAASAP

Extra_Help_for_Underachieving_Students = Interest_of_Sch_Ldshp_in_Underachieving_Student_Help *Teacher_Effort_to_Help_Underachieving_Students * Parental_Pressure_from_Student_Underachievement


Interest_of_Sch_Ldshp_in_Underachieving_Student_Help = IF(Quality_of_School_Leadership) >1.15THEN(1/Ratio_of_ASAP_to_Expected_ASAP)ELSE(1)

Leadership_Change_Multiplier = Community_&_Parent_Interest_in_the_School * (1/Ratio_of_ASAP_to_Expected_ASAP)

Parental_Pressure_from_Student_Underachievement =IF(Community_&_Parent_Interest_in_the_School>1.5)THEN(Effect_of_Performance_Ratio_on_Parent_Pressure)ELSE(1)

Quality_&_Closeness_of_StudentTeacher_Relationship = Student_Personal_Intelligence *Teacher_Perception_of_Student_Ability_&_Motivation

Quality_&_Intensity_of_Instruction = Effect_of_TQ_on_Q&II * Teacher_Perception_of_Student_Ability_&_Motivation *Effect_of_Pers_Intell_on_the_Quality_&_Intensity_of_Instruction * Effect_of_Content_Rigor_on_QII

Ratio_of_ASAP_to_Expected_ASAP = Average_Student_Academic_Performance/Expected_ASAP

Ratio_of_Out_of_School_Suspensions_to_Normal = (1/Student_Academic_Motivation) * 1/Ratio_of_SAP_to_STGL

Ratio_of_SAP_to_STGL = Average_Student_Academic_Performance/Student’s_True_Grade_Level

Student_Effort_in_School =IF(Student_Academic_Motivation=1OR(Student_Academic_Motivation>1))THEN(Student_Academic_Motivation *Effect_of_Extra_Help_on_Student_Effort)ELSE(Student_Academic_Motivation * Effect_of_Extra_Help_on_Student_Effort *Effect_of_Student_Resilience_on_Student_Effort_in_School)

Student_Personal_Intelligence = 1

Student_Resilience = 1.2

Teacher_Effort_to_Help_Underachieving_Students =IF(Teacher_Quality>1.5)THEN(1/Ratio_of_ASAP_to_Expected_ASAP)ELSE(1)

Teacher_Perception_of_Student_Ability_&_Motivation = Effect_of_SAP_to_STGL_Ratio_on_TPSA&M *Effect_of_QSL_on_TPSA&M

Teacher_Quality_Change_Multiplier = Effect_of_AQTR&S_on_the_Teacher_Quality_Change_Multiplier *Effect_of_PD_on_Teacher_Quality_Change

Effect_of_AQPDA_Multiplier = GRAPH(AQPDA_Multiplier)

(0.5, 0.85), (0.6, 0.88), (0.7, 0.9), (0.8, 0.95), (0.9, 0.98), (1, 1.00), (1.10, 1.00), (1.20, 1.05), (1.30, 1.10), (1.40, 1.15), (1.50, 1.20)

Effect_of_AQTR&S_on_the_Teacher_Quality_Change_Multiplier =GRAPH(Average_Quality_of_Teacher_Recruitment_and_Selection)

(0.5, 0.875), (0.6, 0.9), (0.7, 0.925), (0.8, 0.95), (0.9, 0.975), (1, 1.00), (1.10, 1.00), (1.20, 1.01), (1.30, 1.01), (1.40, 1.02), (1.50, 1.02)

Effect_of_Content_Rigor_on_QII = GRAPH(content_rigor)

(0.5, 0.755), (0.6, 0.85), (0.7, 0.855), (0.8, 0.95), (0.9, 0.955), (1, 1.00), (1.10, 1.01), (1.20, 1.01), (1.30, 1.02), (1.40, 1.02), (1.50, 1.03)

Effect_of_Extra_Help_on_Student_Effort = GRAPH(Extra_Help_for_Underachieving_Students)

(0.5, 1.00), (0.6, 1.00), (0.7, 1.00), (0.8, 1.00), (0.9, 1.00), (1, 1.00), (1.10, 1.03), (1.20, 1.06), (1.30, 1.09), (1.40, 1.12), (1.50, 1.15)

Effect_of_LCLIS_on_Community_Resources_for_Schooling = GRAPH(Community_&_Parent_Interest_in_the_School)

(0.5, 1.00), (0.6, 1.00), (0.7, 1.00), (0.8, 1.00), (0.9, 1.00), (1, 1.00), (1.10, 1.03), (1.20, 1.06), (1.30, 1.09), (1.40, 1.12), (1.50, 1.15)

Effect_of_LCM_Multiplier_on_the_Rate_of_Leadership_Change = GRAPH(Leadership_Change_Multiplier)

(0.00, -0.08), (0.167, -0.07), (0.333, -0.06), (0.5, -0.0575), (0.667, -0.055), (0.833, -0.05), (1, 0.00), (1.17, 0.05), (1.33, 0.075), (1.50, 0.1)



Effect_of_PD_on_Teacher_Quality_Change = GRAPH(Amount_and_Quality_of_Supervision_&_Prof_Development_Activities)

(0.5, 0.875), (0.6, 0.9), (0.7, 0.925), (0.8, 0.95), (0.9, 0.975), (1, 1.00), (1.10, 1.00), (1.20, 1.01), (1.30, 1.01), (1.40, 1.02), (1.50, 1.02)

Effect_of_Pers_Intell_on_the_Quality_&_Intensity_of_Instruction = GRAPH(Student_Personal_Intelligence)

(0.5, 0.75), (0.6, 0.8), (0.7, 0.85), (0.8, 0.9), (0.9, 0.95), (1, 1.00), (1.10, 1.05), (1.20, 1.10), (1.30, 1.15), (1.40, 1.20), (1.50, 1.25)

Effect_of_QSL_on_LCLIS = GRAPH(Quality_of_School_Leadership)

(0.00, 0.1), (0.167, 0.075), (0.333, 0.05), (0.5, -0.01), (0.667, -0.02), (0.833, -0.01), (1, 0.00), (1.17, 0.02), (1.33, 0.025), (1.50, 0.03)

Effect_of_QSL_on_RCEAASAP = GRAPH(Quality_of_School_Leadership)

(0.00, 1.00), (0.167, 1.00), (0.333, 1.00), (0.5, 1.00), (0.667, 1.00), (0.833, 1.00), (1, 1.00), (1.17, 1.01), (1.33, 1.01), (1.50, 1.02)

Effect_of_QSL_on_TPSA&M = GRAPH(Quality_of_School_Leadership)

(0.5, 1.00), (0.6, 1.00), (0.7, 1.00), (0.8, 1.00), (0.9, 1.00), (1, 1.00), (1.10, 1.01), (1.20, 1.03), (1.30, 1.04), (1.40, 1.06), (1.50, 1.07)

Effect_of_SAP_on_Teacher_Recruitment = GRAPH(Ratio_of_SAP_to_STGL)

(0.5, 0.9), (0.6, 0.92), (0.7, 0.94), (0.8, 0.96), (0.9, 0.98), (1, 1.00), (1.10, 1.02), (1.20, 1.04), (1.30, 1.06), (1.40, 1.08), (1.50, 1.10)

Effect_of_SAP_to_STGL_Ratio_on_TPSA&M = GRAPH(Ratio_of_SAP_to_STGL)

(0.5, 0.5), (0.6, 0.6), (0.7, 0.7), (0.8, 0.8), (0.9, 0.9), (1, 1.00), (1.10, 1.10), (1.20, 1.20), (1.30, 1.30), (1.40, 1.40), (1.50, 1.50)

Effect_of_School_Leaderfship_on_Teacher_Recruitment_and_Selection = GRAPH(Quality_of_School_Leadership)

(0.5, 0.85), (0.6, 0.88), (0.7, 0.91), (0.8, 0.94), (0.9, 0.97), (1, 1.00), (1.10, 1.03), (1.20, 1.06), (1.30, 1.09), (1.40, 1.12), (1.50, 1.15)

Effect_of_School_Leadership_on_Content_Rigor = GRAPH(Quality_of_School_Leadership)

(0.5, 0.875), (0.6, 0.9), (0.7, 0.925), (0.8, 0.95), (0.9, 0.975), (1, 1.00), (1.10, 1.02), (1.20, 1.05), (1.30, 1.07), (1.40, 1.10), (1.50, 1.12)

Effect_of_Student_Resilience_on_Student_Effort_in_School = GRAPH(Student_Resilience)

(0.5, 0.75), (0.6, 0.8), (0.7, 0.85), (0.8, 0.9), (0.9, 0.95), (1, 1.00), (1.10, 1.10), (1.20, 1.20), (1.30, 1.30), (1.40, 1.40), (1.50, 1.50)

Effect_of_Teacher_Quality_on_Content_Rigor = GRAPH(Teacher_Quality)

(0.5, 0.875), (0.6, 0.9), (0.7, 0.925), (0.8, 0.95), (0.9, 0.975), (1, 1.00), (1.10, 1.02), (1.20, 1.05), (1.30, 1.07), (1.40, 1.10), (1.50, 1.12)

Effect_of_TQCM_on_RCTQ = GRAPH(Teacher_Quality_Change_Multiplier)

(0.5, –0.3), (0.6, –0.15), (0.7, –0.1), (0.8, –0.06), (0.9, –0.03), (1, 0.00), (1.10, 1.00), (1.20, 1.00), (1.30, 1.01), (1.40, 1.01), (1.50, 1.01)

Effect_of_TQ_on_Q&II = GRAPH(Teacher_Quality)

(0.5, 0.955), (0.6, 0.96), (0.7, 0.965), (0.8, 0.97), (0.9, 0.975), (1, 1.00), (1.10, 1.05), (1.20, 1.10), (1.30, 1.15), (1.40, 1.20),(1.50, 1.25)

Effect_of_Various_Factors_on_Changes_in_Motivation = GRAPH(Change_in_SAM_Multiplier)

(0.00, -0.025), (0.167, -0.025), (0.333, -0.02), (0.5, -0.02), (0.667, -0.015), (0.833, -0.01), (1, 0.00), (1.17, 0.01), (1.33, 0.02),(1.50, 0.03)

APPENDIX B. FACTORS AFFECTING DIFFERENTIAL STUDENT ACHIEVEMENT

Family Wealth

• Family education• Family nutrition• Family health care• Prenatal nutrition• Prenatal health care• Prenatal maternal trauma• Early childhood nutrition• Early childhood health care• Parenting practice


APPENDIX C. CATEGORIZED BIBLIOGRAPHY: ACHIEVEMENT GAP

Brain Plasticity

Gopnik, A., Meltzoff, A. N., & Kuhl, P. K. (1999). The baby in the crib. New York, NY: William Morrow and Company.Kotulak, R. (1997). Inside the brain: Revolutionary discoveries of how the mind works. Kansas City, MO: Andrews McMeel Publishing. Shore, R. (1997). Rethinking the brain: New insights into early development. New York, NY: Families and Work Institute.Siegel, D. J. (1999). The developing mind. New York, NY: Guilford.University of Pittsburgh, Office of Child Development (Spring, 1998). Brain development: The role experience plays in shaping the livesof children. Children, Youth, and Family Background, Report No. 12. Pittsburgh, PA: University Center for Social and Urban Research.

Wexler, B. E. (2006). Brain and culture: Neurobiology, ideology, and social change. Cambridge, MA: MIT Press.

Class Size Effects on Achievement

Krueger, A. B., & Whitmore, D. M. (March 2001). Would smaller classes help close the Black-White achievement gap? Working PaperNo. 451. Princeton University, Industrial Relations Section.

Community and Neighborhood Effects on Achievement

Briggs, X., Ferryman, K. S., Pokin, S. J., & Randon, M. (2008). Why did the moving to opportunity experiment not get young peopleinto better schools? Housing Policy Debate, 19(1), 53–91.

Morris, J. E., & Monroe, C. R. (2009). Why study the U. S. south? The nexus of race and place in investigating Black student achieve-ment. Educational Researcher, 38(1), 21–36.

Culture and Reform

Godwin, P. (2006). When a crocodile eats the sun: A memoir of Africa. Boston, MA: Little, Brown and Company.Hatch, M. J. (1993). The dynamics of organizational culture. Academy of Management Review, 18(4), 657–693.Wexler, B. E. (2006). Brain and culture: Neurobiology, ideology, and social change. Cambridge, MA: MIT Press.

Early Childhood Education Effects on Achievement

Magnuson, K., Meyers, M. K., Ruhm, C. J., & Waldfogel, J. (2004). Inequality in preschool education and school readiness, American Edu-cational Research Journal, 41(1), 115–157.

Family and Parenting

Arthur, A. J. (2007). Relation of student and family characteristics to academic achievement for adolescents in low performing schools (Unpublisheddoctoral dissertation). University of California, Berkeley, Berkeley, CA.

Davis, J. L. (2007). An exploration of the impact of family on the achievement of African American gifted learners originating from low-income envi-ronments. (Unpublished doctoral dissertation). The College of William and Mary, Williamsburg, VA.

Ferguson, R. F. (October 21, 2005). Toward skilled parenting & transformed schools inside a national movement for excellence with equity. WienerCenter for Social Policy, John F. Kennedy School of Government, Harvard University. Retrieved from www.schoolfunding.info/news/policy/71_Ferguson_paper.ed.pdf.

Lareau, A. (2002). Invisible inequality: Social class and child rearing in Black families and White families. American Sociological Review, 67,747–776.

Importance of Closing the Achievement Gap

McKinsey & Company (April 2009). The economic impact of the achievement gap in American schools: Summary of findings. Retrieved fromhttp://mckinseyonsociety.com/the-economic-impact-of-the-achievement-gap-in-americas-schools/.

International Achievement Gap

Darling-Hammond, L. (2010). The flat world and education: How America’s commitment to equity will determine our future. New York, NY: Teach-ers College Press.

Tucker, M. S. (2012) (Ed.). Surpassing Shanghai: An agenda for American education built on the world’s leading systems. Cambridge, MA: Har-vard Education Press.

Wagner, T. (2008). The global achievement gap: Why even our best schools don’t teach the new survival skills our children need—and what we can doabout it. New York, NY: Basic Books.


IQ, Heritability, Neuroscience, and Mutability

Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: The IQ paradox resolved. PsychologicalReview, 108(2), 346–369.

Farah, M. J., Shera, D. M., Savage, J. H., Betancourt, L., Giannetta, J. M., Brodsky, N. L., Malmud, E. K., & Hurt, H. (2006). Childhoodpoverty: Specific associations with neurocognitive development. Brain Research, 1110, 166–174.

Goleman, D. (2006). Social intelligence: The revolutionary new science of human relationships. New York, NY: Bantam Books.Nelson, C. A., & Bloom, F. E. (1997). Child development and neuroscience. Child Development, 68(5), 970–987.Nisbett, R. E. (2009). Intelligence and how to get it: Why schools and cultures count. New York, NY: W. W. Norton and Company.Noble, K. G., Frank, M. F., & Farah, M. J. L. (2005). Neurocognitive correlates of socioeconomic status in kindergarten children. Devel-opmental Science, 8(1), 74–87.

Law, Litigation, and Achievement

Glenn, W. J. (2006). Separate but not yet equal: The relation between school finance adequacy litigation and African American studentachievement. Peabody Journal of Education, 8(3), 63–93.

O’Brien, M. T. (August 2006). Two book reviews: Peter Schrag and Richard Rothstein. Educational Studies, 40(1), 87–93.

Poverty and Achievement

Berliner, D. C. (2009). Poverty and potential: Out-of-school factors and school success. Education and the Public Interest Center & EducationPolicy Research Unit. Retrieved from http://epicpolicy.org/publication/poverty-and-potential.

Payne, R. K. (1996). A framework for understanding poverty (4th rev. ed.). Highlands, TX: Aha! Process, Inc.Payne, R. K. (2008). Underresourced learners: 8 strategies to boost student achievement. Highlands, TX: Aha! Process, Inc.

Problem Analysis and Systems Thinking

Berlin, I. (1953). The hedgehog and the fox. Chicago, IL: Elephant Paperbacks.Meadows, D. (2008). Thinking in systems: A primer.White River Junction, VT: Chelsea Green Publishing.

Psychology of Effort and Achievement

Ackerloff, G. A., & Shiller, R. (2009). Animal spirits: How human psychology drives the economy, and why it matters for global capitalism. Prince-ton, NJ: Princeton University Press.

Racial Identity, Oppositional Culture, and Achievement

Carter, D. J. (2005). “In a sea of White people”: An analysis of the experiences and behaviors of high achieving Black students in a predominantly Whitehigh school (Unpublished doctoral dissertation). Harvard University, Cambridge, MA.

Coley, D. W. (2008). Afrocentric identity and high school students’ perception of academic achievement (Unpublished doctoral dissertation). Uni-versity of Hartford, Hartford, CT.

Ferguson, R. F. (Draft, September 2006). New evidence on why Black high schoolers get accused of “Acting White.” The Achievement Gap Initia-tive at Harvard University. Retrieved from www.agi.harvard.edu/Search/download.php?id=104.

Fleming, P. R. (2005). Academic engagement, racial identity development, and school success among middle school students. (Unpublished doctoraldissertation). University of Missouri-Columbia, Columbia, MO.

Graham, S., (1994). Motivation in African Americans. Review of Educational Research, 64(1), 55–117.Graves, D. A. (2006). School culture, racial identity performance and the academic achievement of Black adolescents (Unpublished doctoral disser-tation). Harvard University, Cambridge, MA.

Harlapalani, V. (2005). Racial stereotypes and achievement-linked identity formation during adolescence: An investigation of athletic investment andacademic resilience (Unpublished doctoral dissertation). The University of Pennsylvania, Philadelphia, PA.

Harris, A. L. (2005). Do African Americans really resist school: An In-depth examination of the oppositional culture theory (Unpublished doctoraldissertation). The University of Michigan, Ann Arbor, MI.

Lowe, A. N. (2006). Contending with legacy: Stereotype threat, racial identity, and school culture (Unpublished doctoral dissertation). StanfordUniversity, Stanford, CA.

Noguera, P. (2003). The trouble with Black boys: The role and influence of environmental and cultural factors on the academic perform-ance of African American males. Urban Education, 38(4), 431–459.

Ogbu, J. U., & Simons, H. D. (1998). Voluntary and involuntary minorities: A cultural-ecological theory of school performance withsome implications for education. Anthropology and Education Quarterly, 29(2), 155–188.


Ogbu, J. U. (2004). Collective identity and the burden of “Acting White” in Black history, community, and education. The Urban Review,36(1), 1–35.

Perry, J. C. (2006). School engagement among urban youths of color: Criterion pattern effects of vocational exploration and racial identity (Unpub-lished doctoral dissertation). Boston College, Chestnut Hill, MA.

Rivera, K. A. (2004). Academically high achieving African Americans: An exploratory study of a middle class sample (Unpublished doctoral disser-tation). Rutgers, The State University of New Jersey, New Brunswick, NJ.

Woods, T. A. (2006). Racial socialization, racial identity, and achievement in the context of perceived discrimination: Understanding the developmentof African American middle school youth (Unpublished doctoral dissertation). The University of North Carolina at Chapel Hill, ChapelHill, NC.

Resilience and Achievement

Allen, D. L. (2004). An examination of the relationship between teachers’ perceptions of their school’s ability to foster a culture of resilience and stu-dent outcomes on the Ohio Sixth Grade Reading Proficiency Test (Unpublished doctoral dissertation). The University of Cincinnati, Cincin-nati, OH.

Brown, A. P. (2004). Learning environment and attitudinal differences between resilient, average, and non-resilient fourth- and fifth-grade Hispanicstudents (Unpublished doctoral dissertation). University of Houston, Houston, TX.

Carnes, S, J. (2009). Resilience in action: A portrait of one high-poverty/high-performing school (Unpublished doctoral dissertation). AuroraUniversity, Aurora, IL.

Coleman, H. L. K. (2007). Minority student achievement: A resilient outcome? In D. Zinga (Ed.), Investigating multiculturalism: Negotiat-ing change. (pp. 296–326). Newcastle upon Tyne, UK: Cambridge Scholars Publishing.

Crawford, K. M. (2006). Risk and protective factors related to resilience in adolescents in an alternative education program (Unpublished doctoraldissertation). University of South Florida, Tampa, FL.

Marshall, M. P. (2008). Overcoming the odds: An interpretive phenomenological analysis of academic resilience among urban young adults (Unpub-lished doctoral dissertation). Wright Institute, Berkeley, CA.

Salley, L. D. (2005). Exploring the relationship between personal motivation, persistence, and resilience and their effects on academic achievement amongdifferent groups of African-American males in high schools (Unpublished doctoral dissertation). University of Maryland, College Park, MD.

School and School District Reform Effects on Achievement

Borman, G. D., Hewes, G. M., Overman, L. T., & Brown, S. (2003). Comprehensive school reform and achievement: A meta-analysis.Review of Educational Research, 73(2), 125–230.

Comer, J. P. (2001). Schools that develop children. American Prospect, 12(7), 30–36.Delpit, L. (2006). Lessons from teachers. Journal of Teacher Education, 57(3), 220–231. Harris, D. N. (June 16, 2006). High flying schools, student disadvantage, and the logic of NCLB. The Harvard Achievement Gap Initiative. Henig, J. R. (November 2008). What do we know about the outcomes of KIPP schools?The Great Lakes Center for Education Research & Prac-tice. Retrieved from nepc.colorado.edu/publication/outcomes-of-kipp-schools.

Paul, A. M. (2010). Origins: How the nine months before birth shape the rest of our lives. New York, NY: Simon and Schuster.Tough, P. (2008). Whatever it takes: Geoffrey Canada’s quest to change Harlem and America. Boston, MA: Houghton Mifflin.

Segregation and Achievement

Eaton, S. (2007). The children in Room E4: American education on trial. Chapel Hill, NC: Algonquin Books of Chapel Hill.

Self-Efficacy, Self-Concept, Self-Esteem, and Achievement

Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117–148.Campbell-Whatley, G. D., & Comer, J. (2000). Self-concept and African-American student achievement: Related issues of ethics, powerand privilege. Teacher Education and Special Education, 23(1), 19–31.

Rivera, K. A. (2004). Academically high achieving African Americans: An exploratory study of a middle class sample (Unpublished doctoral disser-tation). Rutgers, The State University of New Jersey, New Brunswick, NJ.

Social Capital and Achievement

Siisiäinen, M. (July 2000). Two concepts of social capital: Bourdieu vs. Putnam. Paper presented at ISTR Fourth International Conference, TheThird Sector: For What and for Whom?, Trinity College, Dublin, IE.


Standards and Testing

Bok, D. (2003). Closing the nagging gap in minority achievement. The Chronicle of Higher Education, 50(9), B20.Darling-Hammond, L. (2003). Standards and assessments: Where we are and what we need. Teachers College Record. Retrieved fromhttp://www.tcrecord.org.

State Policy Effects on Achievement

Braun, H. J., Chapman, L., & Vezzu, S. (September 19, 2010). The Black-White achievement gap revisited. Education Policy AnalysisArchives, 18(21), 1–99.

Braun, H. I., Wang, A., Jenkins, F., & Weinbaum, E. (March 20, 2006). The Black-White achievement gap: Do state policies matter? Edu-cational Policy Analysis Archives, 14(8), 1–110.

Statistics on the Achievement Gap

Bast, J., & Reitsma, P. (1998). Analyzing the development of individual differences in terms of Matthew Effects in reading: Results froma Dutch longitudinal study. Developmental Psychology, 34(6), 1373–1399.

Clotfelter, C., Ladd, H. F., & Vigdor, J. L., (May 2006). The academic achievement gap in Grades 3 to 8. Retrieved fromhttp://papers.ssrn.com/sol3/papers.cfm?abstract_id=900992.

University of Chicago News Office (April 28, 2005). Economics research shows black-white achievement gap has stopped narrowing. Retrievedfrom http://www-news.uchicago.edu/releases/05/050428.neal.shtml.

Teacher and Principal Quality

Berry, B., TeacherSolutions 2030 Team. (2011). Teaching 2030: What we must do for our students and our public schools—now and in the future.New York, NY: Teachers College Press.

Clotfelter, C., Ladd, H., & Vigdor, J. (2006). Teacher-student matching and the assessment of teacher effectiveness. Journal of HumanResources, 41(4), 778–820.

Darling-Hammond, L. (December 1999). Teacher quality and student achievement: A review of state policy evidence. Center for the Study ofTeaching and Policy, University of Washington.

Darling-Hammond, L., (2000). Reforming teacher preparation and licensing: Debating the evidence. Teachers College Record, 102(1),28–56.

Darling-Hammond, L., Berry, B., & Thoreson, A. (2001). Does teacher certification matter? Evaluating the evidence. Educational Evalua-tion and Policy Analysis, 23(1), 57–77.

Darling-Hammond, L., Wise, A. E., & Pease, S. R. (1983). Teacher evaluation in the organizational context: A review of the literature.Review of Educational Research, 53(3), 285–328.

Fenstermacher, G. D., & Richardson, V. (2005). On making determinations of quality in teaching. Teachers College Record, 107(1),186–213.

Jongewaard, S. M. (2004). Teachers at risk: Preparing effective teachers for 21st century schools. Paper presented at the Oxford RoundTable on At-Risk Students, Oriel College, Oxford University, March 21–26, 2004. Retrieved from www.agi.harvard.edu/Search/download.php?id=118.

Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban schools: A descriptive analysis. Educational Evalua-tion and Policy Analysis, 24(1), 37–62.

McCaffrey, D., Lockwood, J. R., Koretz, D. M., & Hamilton, L. S. (2003). Evaluating value-added models for teacher accountability. Preparedby RAND for the Carnegie Corporation of New York. Retrieved from http://www.rand.org/pubs/monographs/2004/RAND_MG158.pdf.

Peske, H. G., & Haycock, K. (June 15, 2006). Teaching inequality: How poor and minority students are shortchanged on teacher quality.A reportof the Education Trust. Retrieved from http://www.forumforeducation.org/node/59.


As noted in Alan Gaynor’s Reflection (p. 31), there is an instruc-tive, and not entirely unexpected, symmetry between his struc-tural analysis of school improvement mechanisms and our ownempirical investigation of those same mechanisms in the Schoolson the Move (SOM) Award Winners. Both papers are informed bya robust literature examining structural factors that contribute toeffective schooling. What is most notable, perhaps, in comparingour two papers is that despite differences in method our collectiveevidence points to the potential benefit of looking beyond teacherquality exclusively, to policies that empower school leaders andundergird effective school-wide practices. Gaynor conducted a structural analysis of the factors that con-

tribute to school success for students who enter at low readinesslevels. Specifically, his model focuses on school-based support forstudents who enter with lower levels of English language develop-ment, academic motivation, and self-discipline. Conducting a com-puter simulation of how “essential elements of a school” impactstudents’ achievement trajectories, Gaynor finds that, in addition tohigh teacher quality, school leadership and community and parentinterest have the greatest impact on student performance.Importantly, schools able to effectively combine all the above

factors are predicted to see the greatest growth “to the point ofclosing the gap with grade level standards” (p. 18). These data leadGaynor to the conclusion that school leadership may be the singlemost important variable, as effective leaders are able to recruit andsupport high-quality teachers and are instrumental in generatingparent/community involvement. At this point, a brief digression is warranted in reflecting on

Gaynor’s paper. Research that leverages simulated data is commonin many academic disciplines that promote sophisticated statisticalanalysis, but remains rare in educational research. This may resultfrom the fact that education research is an inter-disciplinary pur-suit with direct applications to present policy and practice deci-sions. Researchers who write for multiple audiences (e.g.,educators, policymakers, legislators, other researchers) may beleery of applying special knowledge. Regardless, finding ways to

anticipate and assess learning outcomes that cannot be easily stud-ied in controlled settings is needed. Simulated data can be an art-ful substitute for empirical evidence when lacking data, allowingresearchers and decision-makers to engage policy issues that mightotherwise be neglected. We would encourage Gaynor to considera future article that more fully explores and explains computer-based simulated models to lay audiences and considers their appli-cation as policymaking instruments.Returning to our reflection on the two articles, Gaynor’s find-

ings are vibrantly illustrated in the SOM Award Winning schoolsanalyzed in our study. Leadership at SOM schools was particularlyeffective in reaching out to students’ families/communities andsupporting teacher quality. At the Sarah Greenwood K–8 School,for example, a Student Support Team composed of teachers,administrators, nurses, and counselors played an important role inproviding whole-child supports to students who entered withlower levels of readiness. Additionally, data-driven instructionalteams, viewed across all SOM Award Winners, demonstrated thateffective teaching does not occur in the vacuum of a single class-room. Instead, quality teachers at SOM schools work with admin-istrators in collaborative data teams to make the instructional orcurricular adjustments necessary to reach high-needs learners. Too often, decision-makers, guided exclusively by research stat-

ing that teacher quality is the single most important factor affectingstudent performance, focus on piecemeal approaches to improvingteaching. Our papers join a growing literature that raises questionsabout this research and its related policy prescriptions.

Peter Piazza is a doctoral student in Curriculum and Instruction at the LynchSchool of Education, Boston College. Mr. Piazza can be reached [email protected].

Dr. Chad d’Entremont is the Executive Director of the Rennie Center forEducation Research & Policy in Cambridge, MA. Dr. d’Entremont can bereached at [email protected].

A Reflection on the Case Study and the Structural Analysis

peter piazza and chad d’entremont, rennie center for education research and policy

29

I was pleased to be offered the opportunity to read an advancecopy of “Charting the Course: Four Years of the Thomas W.Payzant School on the Move Prize” by Chad d’Entremont, Jill Nor-ton, Michael Bennett, and Peter Piazza, and to be invited to reflectin print on the relationship between this article and my paper,“Different Students: How Typical Schools Are Built to Fail andNeed to Change: A Structural Analysis,” both published in thisissue of the Journal of Education.Clearly the two articles are related in that they both address

school improvement, student performance, and the nature ofschools that are exemplary in these regards. Despite these similar-ities in focus, there are significant differences between them inpurpose and approach. Given the notably similar major conclusionthat can be drawn from the outcomes of these two efforts, it isuseful to begin by describing these differences.

THE CASE STUDY

The paper by d’Entremont et al. focuses on four urban schools: oneelementary, one K–8, and two high schools that were the recipientsof the Thomas W. Payzant School on the Move Prize. This honor is“awarded annually to a Boston Public School that has made signifi-cant progress in improving student achievement” (p. 3). While providing descriptive data on the four prizewinning

schools they discuss (Tables 1–4), the article is essentially, as theauthors note, “a collective case study” (p. 4). It presents relativelyrich and detailed qualitative information on what has made theseschools high-performing, information that was acquired in tradi-tionally qualitative ways:

• examination of “previous case studies of individual SOM[School on the Move] Prize winners” (p. 4),

• school observations, and• “new interviews with school leaders, staff, and students” (p. 4).

Overall, the presentation is descriptive and, perhaps most sig-nificantly, comparative across the four schools. An instructive setof additional findings is an analysis of “core challenges,” which willresonate with readers who are educators in urban schools. Theinsights gained from this focused work will be particularly inform-ative in the ongoing discussions of providing high quality educa-tion in urban schools across the K–12 spectrum.

THE STRUCTURAL ANALYSIS

My approach, to essentially the same research topic, while rootedin, and expressive of, fundamentally qualitative understandings,was theoretical, rather than descriptive in nature. I wrote the fol-lowing to describe my work:

Computer simulation analysis was used to illustrate the the-sis that the typical American public school is structured in away that reinforces the entry characteristics of its students sothat by the time they graduate—if they graduate—aftertwelve years, students who enter the school in kindergartenor first grade with high “readiness” perform academicallybetter-than-average, while students who enter the schoolwith low “readiness” perform worse than average, thus cre-ating the well-known and widely discussed “achievementgap.” (p. 13)

Instead of presenting lists and tables, I presented the theoreti-cal structure of this “typical” school in the form of what are called“causal loop” or “causal influence” diagrams. Depending on theirstructure, these sub-components of a system can reinforce orresist changes in the overall behavior of the system.Reinforcing causal loops are referred to as “positive feedback”

loops; stabilizing causal loops are referred to as “balancing” or“negative feedback” loops. An initial change in one of the variablesin a positive feedback loop causes changes in other variables in theloop that circle back over time to reinforce the initial change (upor down) contributing, for example, to inflation or depression. Onthe other hand, initial changes (up or down) in one of the variablesin a negative feedback loop cause counterbalancing changes in othervariables in the loop that, overall, tend to help stabilize the largersystem over time.It is hypothesized in my paper that the typical school is domi-

nated by positive feedback loops that reinforce the initial charac-teristics of the students when they first enter the school. Thetheory, central to the paper, is that, in the typical school, initial lev-els of school readiness—high, average, and low—are maintained,and widened, over the course of the students’ school careers toproduce what is commonly called “the achievement gap.” The theory is presented in the form of diagrams and equations

that represent mathematically the proposed causal structure alongwith reasonable parameters (“effect sizes”) based on a review ofthe literature. This model “runs” in a way that produces trend val-ues for all the variables so as to illustrate both the dynamics of a“typical” school and the effects of changes in such variables as

A Reflection on the Structural Analysis and the Case Study

alan kibbe gaynor, boston university

31

teacher quality, school and community leadership, curriculum,instruction, student motivation and resilience, etc.

THE IMPLICATIONS

While the two articles thus take these very different approaches tothe dynamics of school improvement, it is interesting that, funda-mentally, they support one another in their conclusions. For exam-ple, d’Entremont et al. draw the following conclusions from theircase study about the factors that are central for effective schooling:

1. Shared leadership and meaningful collaboration;2. Data-driven decision making;3. High expectations for academic success enabled by intense and student-centered academic and social/emotional supports(p. 11);

4. Emphasis on teacher quality, teacher evaluation, and appropri-ate professional development and support (p. 11);

5. Strong commitment to families and community-building, andoverall;

6. Strong leadership from principals [that] ensures that the focuson high expectations is centered as much on the staff as it is onthe students (p. 11).

As I point out in the conclusions to my paper, the implicationsto be drawn from my work are similar:

Given the structure of the model as formulated, highteacher quality, school leadership, and community and par-ent interest in schools all have quite significant effects on theacademic performance of initially low-achieving students,to the point of closing the gap with grade level standards.The combination of pairs of these variables have evengreater effects. The greatest effects are achieved by the com-bination of all three variables. (p. 18)

Thus, in the end, while the two articles are rooted in differentmethodologies and purposes, one descriptive and empirical, the

other mathematical and theoretical, the variables I included in mymodel and the general conclusion I drew in the end seem deeplyconsistent with, and confirmed by, the work of d’Entremont et al.This mutuality is evident in my final discussion of the implicationsof the theoretical analysis of the achievement-gap problem inschools:

While teacher quality in the model has slightly greatereffects than school leadership, it seems important to keep inmind that changes in the overall level of teacher qualityprobably cannot be achieved in the real world withoutstrong school leadership—through the effects of leadershipon the recruitment and selection of teachers and on profes-sional development, instructional supervision, and the rigorof the content presented, especially to low-achieving stu-dents. In the same way, community and parent interest inschools is probably essentially what in the world of researchis called a fixed effect, amenable to deliberate strategic ini-tiatives only in the long term—by improving the schoolincrementally over time, which, again in my view, seemscrucially dependent on strong school leadership.With this in mind, it seems that in the real world the most

important variable amenable to purposeful policy action isschool leadership. The implications for the selection, recruit-ment, and preparation of a pool of both high quality teach-ers and strong school leaders seem evident. (p. 18)

So it appears that the conclusions drawn in both articles empha-size the crucial importance of school leadership for success inschool improvement, the factor that powerfully influences all theothers.

Alan Kibbe Gaynor is an associate professor at Boston University. ProfessorGaynor can be reached at [email protected].


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