4 Culture Creating Conditions for Success AN OPENING STORY Iri our first year of implementation of data-driven instruction, we knew that one teacher in particular was going to be very resistant. As one of the most veteran teachers on the staff and well respected by her peers, she also wielded great influence on others. Although we had invited her to join a leadership team to launch the initiative, she was still unprepared for the poor results her students received on their first interim assessment. As we followed the protocols established in Chapter Two and Chapter Three, her students' performance notably improved, but she remained very unhappy and completely unconvinced that data-driven practices had anything to do with these improvements.· She regularly sent us signals of her displeasure with this initiative and felt it was stifling her teaching. At the end of the year, students gained thirty points in proficiency from the previous year's cohort, despite the fact that this cohort had been even lower­ skilled when they started the year! Despite all the signs of her accomplishments, the teacher was still un:willing to acknowledge any impact of data-driven practices and continued to advocate for removing these systems. Two years later, however, we had a faculty meeting and were discussing whether we should shorten our analysis protocol and action plan to make it easier for teachers to complete. In the middle of the meeting, this same teacher raised her hand and said, "This is a critical reason why o}r students learn so effectively; we shouldn't shorten it at all." . It took two full years' for the teacher to buy in to data-driven instruction, but in the meantime, her students still made dramatic gains in achievement. When implemented well, data-driven instruction drives achievement from the beginning-a critical factor that distinguishes it from many other initiatives that require teacher buy-in before they have any chance of success. DEVELOPING CULTURE If you feed "culture of high expectations" to an Internet search engine, you will find hundreds of articles devoted to the topic. More concretely, .studies of high­ achieving schools often talk about the influence of "culture" or "shared vision" in their success.1 The question to ask, however, is not whether high-achieving schools h~ve a strong culture of high expectations-they universally do-but what were the drivers that created such a culture in each school? In traveling around the country, I have yet to meet any teachers or school leaders who did .not believe they had high expectations for student learning. The difference, then, is not in what is said but what is practiced. How can a school demystify the process of improving expectations and. operationalize it with concrete actions that have proven to yield results? Just as standards are meaningless until you define how to assess them, working to build a data-driven culture is fruitless until you define the concrete drivers that guarantee it. Building Buy-In Initial faculty buy-in is not a prerequisite for starting to implement data-driven instruction. (Which is just as well; it's easy to argue that any initiative that 106 Driven by Data I requires complete buy-in prior to implementation is likely to fail.) The best initiatives in schools-and elsewhere-do not require buy-in, they create it. In fact, the Camden County, Georgia, School District published a very persuasivM article about the phases of data-driven instruction. It illustrated how teachers in their district moved from Phase 1 to Phase 5: • Phase 1: Confusion and overload-"This is too much!" • Phase 2: Feeling inadequate and distrustful-"How can two questions on a test possibly establish mastery of an objective? These questions are terrible!" • Phase 3: Challenging the test-"That is a poor question. Answer 'b' is a trick answer." • NP hase 4: Examining the results objectively and looking for causes­"Which students need extra help and in what topic? Which topics do I need to re-teach in different ways?" • P hase 5: Accepting data as useful information, seeking solutions, and modifying instruction- "Their inability to subtract negative integers affected their ability to solve the algebraic equation. I need to re-visit the concept of negative numbers and how to use them. "2 Rather than hope that teachers enjoy the process from the very beginning, school leaders should anticipate that it will take various phases for everyone to see the value of data-driven instruction. The article from Camden County, Georgia, is one of the few publications to discuss the hurdles and challenges that occur early on in the implementation of data-driven instruction. If you would like to look at an even more concrete example, read the case study included in the CD-ROM about Douglass Street School. While the names were changed to allow for a candid sharing of the details, the case study is a true story and can give more insight into how schools· make dramatic gains in achievement despite initial resistance. Culture 107 Data-Driven Success Story Chicago International Charter School: Winning Converts The Results Illinois !SAT Exam: Percentage of Chicago International Charter, School Students at or Above Proficiency &' - " .·¥19cf 'J;j, " ( ) - - - - -* - - ·,, , , .. · , Eilglishand$ngii%'Arts. - .,. .,,..,, / .,. • .;0 # - ,,, > 1 ;· - . '""' 2 - .+ 7' , : Matll&matii::11 " 3.. -4% =- 2005-06 58.8% 61.5% 2006-07 72.1% 83.1% 4 ,. : ., .   Figure 4.1 Chicago International Charter School Students' Scores on the Illinois ISAT Exam: Percentage· at or Above Proficiency. 90% ----- 86% 83% -- English and Language Arts -- Mathematics 60% 50 % +-------------- 2005-06 2006-07 2007-08 The Story In 2005, the Chicago International Charter School (C.I.C.S.) Bucktown Campus was stagnating. With ineffective leadership and an unmotivated faculty, the school had seen almost no change in test scores since 2000. Turon Ivy set out to change this upon 108 Driven by Data becoming principal at C.I.C.S.-Bucktown. Taking what he learned from the Data-Driven Instruction Comprehensive Leadership Workshop (see Chapter Twelve]. Ivy introduced interim assessments to the school. Yet although the new principal was enthusiastic about data-driven instruction, his teachers were considerably more wary. During the 2005-06 school year, resistance from the faculty was strong, a problem greatly compounded by the lack of communication and transparency that had been practiced by Ivy's predecessor. Rather than abandon the project of data-driven instruction, the leadership at C.I.C.S.-Bucktown put systems in place to win staff over and secure faculty participation. One of the most important parts of this process was running detailed professional development sessions to introduce faculty members to data-driven instruction and to show them its value in improving education. More important, during a time where many faculty members were apprehensive about testing, Ivy presented data-driven analysis not as a way for the administration to catch poor teachers but as an opportunity for the school to succeed as a whole. In the next year, 2006-07, Ivy continued to win staff over, making professional development more systematic than it had been in the past and creating a transparent school calendar to allow for faculty participation and input. As more and more staff bought in, the few holdout teachers eventually left on their own accord to work elsewhere. Ivy continued to visit other high-achieving schools and attend data-driven workshops to bring additional best practices to the school. Additionally, C.I.C.S.-Bucktown began having teachers from different grade levels meet with each other so that teachers of younger students could coordinate their curricula to the demands of later academic years. As a result of this strong emphasis on effective professional development and staff involvement, Ivy was able to bring a formerly dysfunctional school from stagnation to success! Key Drivers from Implementation Rubric • ] Introductory professional development: If great care is not taken when setting up the professional development session that introduces data-driven instruction, the result can be a seemingly insurmountable level of faculty distrust and resistance. Framing assessments as opportunities for the entire school to improve its teaching as a whole is a great strategy for persuading wary staff to give them a try. • ] Build by borrowing: Ivy looked for best practices in other high-achieving schools that ]he could bring to C.I.C.S.-Bucktown, and he also built systems for teachers to learn from each other within the school. • ] Implementation calendar: By developing a transparent implementation calendar, I vy removed the mystery of data-driven instruction and allowed teachers to understand clearly what was occurring each step of the way. Even teachers who were resistant knew what was expected of them and achieved stronger results. Culture 109 Core Idea • Data-driven instruction properly implemented does not require teacher buy­ in - it creates it. Much of what builds an effective data-driven culture is embedded within the drivers of assessment, analysis, and action. This chapter focuses on the remaining explicit structures that build buy-in and guarantee an effective data-driven culture. In my experience, following the drivers identified in this book will lead directly to increased student achievement. IDENTIFYING AND DEVELOPING THE RIGHT LEADERSHIP TEAM At the heart of this work is the identification of the school leadership team. School leaders should identify and cultivate relationships with key faculty leaders, ties that can be thought of as bridges to buy-in. As long as structures exist to ensure the participation of key school leaders, improved results will win over the rest of the faculty in time. In the Harvard Business Review article "Informal Networks: The Company Behind the Chart," David Krackhardt and Jeffrey Hanson argue about the importance of making sure that the leadership team includes. members of two important networks in an organization: the expert network and the trust network.3 The expert network consists of those members with the greatest expertise: in the case of a school, your strongest teachers. These are the people teachers admire for the quality of their teaching. The trust network in a school, by contrast, consists of teachers to whom others turn for personal support or guidance. While riot necessarily the strongest teachers, they are the ones with the greatest influence on their peers in the day-to-day working of the school. Most school leadership· teams already consist of leaders of the expert net­ work. Securing the .input and involvement of leaders of the trust network 11 0 Driven by Data as well will go a long way toward creating a solid culture of data-driven instruction. Involvement now, buy-in later: Once these staff members are identified, every effort should be made to include them in the process of implementing data­ driven instruction. Of course, not every school leader will instantly embrace data-driven instruction, and some will initially dislike it. By keeping such faculty leaders involved in the process, however, the principal will be able to minimize resistance and at least ensure participation on the part of the most influential teachers. This is extremely significant, because as long as leaders are involved and willing to stay with the plan, then buy-in will inevitably follow. THE CALENDAR A story that sticks (author unknown): during one lecture, a time management expert set out a large glass container and a box of fist-sized rocks. After carefully placing rocks in the glass container, he came to a point where no more would fit. He then turned to the audience and asked: "Is it full?" "Yes," came the reply. He then produced a box of smaller pebbles and managed to fit a few into the container. "Is it full?" he asked again. "Yes, it is now," was the answer. From a small bucket he began to pour gravel into the spaces between the rocks and pebbles, every now and then shaking the container until no more would go in. "Is it full?" "Probably not!" the audience replied. Out came some fine sand, and he began to pour. With just a few gentle shakes, he was able to bring the contents of the container to the very brim. "Is it full?" "No!" Next came a pitcher of water and this he allowed to drip slowly into the container until, in time, the pitcher was empty. "So," he asked, "what have you learned today?" "Well," someone responded, "the lesson is that there is always room for more." Culture 111 "Nope. The lesson is that if you don't put the big rocks in first, they won't fit." The lesson of the story is clear: if certain key fundamentals are not secured first, then nothing else will be possible. Although this principle applies to many /facets of life, it is especially apparent in data-driven instruction when it comes to creating a culture in which assessment, analysis, and action can thrive. The "jar" in this arena is the school calendar. The "big rocks" are interim assessments, analysis, and action. Without the "big rocks" firmly in place within this calendar, it is almost impossible to create a truly excellent data-driven school. Schools live and die by their calendars: whatever makes it onto the schoolwide calendar trumps other activities that come later. Given that data-driven instruction is based upon timely and regular analysis, assessment, and. action, placing these events on the school calendar first is essential for student achievement. Without being embedded in the structure of the calendar and school schedule, analysis and action are likely to be ignored, overlooked, or delayed, causing the project to fail. There are too many moving pieces in a school year to expect effective data, driven instruction to "just happen"; schools must con,sciously craft a calendar that lays the foundation for genuine progress. Core Idea • School calendars drive priorities: Make sure to schedule assessments, scoring, analysis, and professional development before placing any other events on the school calendar. Here are the keys for developing an effective data-driven school calendar: • YMake time for data: The first critical feature of the calendar is that it blocks off time for interim assessments to be administered, scored, and analyzed. All too often, schools will maketime to test but leave no time to grade exams, a situation that gives teachers and school leaders an excuse to postpone analysis until it is useless. • YNote end-goal tests when placing interim assessments: Beyond fixing the time for interim assessments, the schoolwide calendar must also take into account the state and national tests taken by students during the year. Given that interim assessments are most effective in six- to eight-week 112 Driven by Data periods, plan the timing of the interim assessments working backward from the summative state and national tests, and then working forward for the rest of the school year after these assessments. (For example, if your state test is in February, plan for an interim assessment cycle the leads up to the February state test, and then after February you can start working toward the standards of the following year, aj.lowing you to have a full calendar year of interim assessments). • Mark professional development: As a further important feature, plan for professional development days before and after each round of interim assessments to allow for implementing each step of the data-driven process. This will also allow the school to provide content-focused professional development in response to the learning needs identified on the assessment. • kLeave room for re-teaching: Finally, and perhaps most important, an, effective calendar is one that builds in time for the re-teaching necessitated · by the assessment analysis. North Star Academy, for example, formally allots a week following assessments to re-teaching and reviewing earlier standards. Of course, this is not to say that this entire week is spent in review; in most cases, teachers integrate and spiral re-teaching while presenting new material. Nevertheless, the very existence of this re-teach week sends a powerful signal that assessment results will guide curriculum and that data results are to be taken seriously. Exhibit 4.1 is an example of a yearlong assessment calendar. As can be seen from Exhibit 4.1, an effective calendar need not be overly complex or difficult to create, but it must include the basic elements outlined here if it is to be successful. A second question often asked is how to structure the week itself when assessments occur .and then analysis meetings and re-teaching. Chapter Two (Analysis) highlighted a one-week schedule used by Greater Newark Academy, and that can serve as a model. Build by Borrowing In building a data-driven culture, few skills are as vital as the ability to identify and adapt best practices from other successful schools. All the highest-achieving Culture 113 Exhibit 4.1 Assessment Calendar. 4 Weeks (5/28-6/221 Unit 6 and Final Perfor­ mance Task Preparation YEAR-END (6/W-6/291 Final Performance Taosks Oral presentations and large math projects schools highlighted in this book are masters of "building by borrowing." They visited schools that were achieving better results than their own and borrowed any and every tool that could increase their own results. Leaders should strive to create an ethos in which teachers and school leaders perpetually seek out the best ideas beyond their building. During their initial roll-out of data-driven instruction, leaders should make an effort to visit effective schools and see data in action. Such visits will surely provide important insights into the mechanics of data-driven instruction, but they also provide something more important: hope. By seeing data-driven instruction succeed with their own eyes, school leaders and teachers will gain the confidence to articulate a compelling and coherent vision of what data-driven excellence looks like and what it will take to truly succeed. 114 Driven by Data One individual has taken this concept to another level. Doug Lemov, a fellow managing director at Un,common Schools and manager of True North Rochester Prep (see success story), has devoted the past few years to 1finding the most accomplished urban school teachers in the country-"Master Teachers." He has videotaped them in action and identified the shared strategies that they all use to be so successful. He compiled these experiences into Teach Like a Champion, which includes a framework, actual video clips, and resources to be used in training teachers. Lemov is proving that teachers don't have to be born great; they can also be developed into high-achieving teachers. It is also much easier to believe in success when you can see examples of success with students like your own. This happens naturally in the assessment cycle when teachers see their own students improve on subsequent assessments. In these video clips, Lemov makes it possible for school leaders and teachers to "build by borrowing" without ever leaving their own schools! Getting to Why As you lead your school to build a culture of data-driven instruction, the most frequent and important question you will face is also among the simplest: why? Very often, people will ask why such dramatic changes are being made and, more fundamentally, why data-driven instruction matters at all. Implementing the coWe principles of effective professional development and building by borrowing will answer these questions effectively for most school staff members. However, other staff members will have lingering questions, and they will need a brief, personal "sales pitch." Indeed, if you cannot coherently defend data-driven instruction in a minute or less, then faculty, students, and community members will be much less likely to accept it. Culture Data-Driven Success Story Samuel J. Green Middle School: New Orleans Rebirth The Results Louisiana Eighth-Grade State Exam: Percentage and of Students at or Above Proficiency Figure 4.2 Louisiana State Assessment: Percentage of Samuel J. Green Students at or Above Proficiency in Eighth-Grade Math. 80% /-------------·---·--·-··-·-··-·--···- · -·-..··· 73% --------------- -- S.J. Green 40% ............. - - - New Orleans - - - Louisiana ""'-....-------- -' 20% 2004-05 2005-06 2006-07 2007-08 2008-09 The Story In 2005, Samuel J. Green Middle School had a ten-year history of low performance. The school was surrounded by barbed wire fences and concrete, and dilapidated portable classrooms filled what should have been the playground. The state of Louisiana turned Green into a charter school and handed it over to a local nonprofit organization in an attempt to turn the school around. One week later, the levees broke and the floodwaters of Hurri­ cane Katrina inundated the city. The Green cFmpus collected a few feet of water but was quickly drained and repaired. In January 2006, Samuel J. Green Charter School reopened for former Green students and new students returning to the city after Hurricane Katrina. 116 Driven by Data As co-founder of Firstline Schools and the leader of the school turnaround, Tony Recasner found himself without a principal-the one who started the year never returned post-Katrina-and so he started to bring order and address the urgent learning needs of students who had missed months of school and whose families were displaced across the country. Two years later, Recasner was rejoined by his fellow co-founder Jay Altman, and they set two primary goals for Green: implement an effective data­ driven instructional model and ensure a calm, orderly environment where teaching and Learning could thrive. Leaders were trained to work effectively with teachers in individual and group data meetings. Teachers received extensive professional development during summer staff orientation, particularly in how to use assessment_ data to drive instruction. They launched formal interim assessments, getting feedback from teachers to increase investment and the quality of the tests. They also implemented qata Days, where teachers analyzed students· performance on the assessments. Teachers used tracking sheets to monitor students· performance between interim assessments and adjust their teaching strategies in the moment to meet student learning needs: The gains from 2005 to 2009 are an inspiring story of rebirth after the hurricane. "We've improved in a lot of areas since taking over the school in 2005," Altman reflects, "but the biggest driver of our success in the past year has been implementing interim assessment and using the data in a systematic way." Expect to see Green hitting 90 percent proficient in the near future! Key Drivers from the Implementation Rubric • • Build by borrowing: Altman traveled the country to pull the best practices from high-achieving urban schools and apply them to Green's improvement strategies. Ongoing assessment: Tracking sheets enabled teachers to have precise in-the­ moment measures of student learning. How, then, to justify data? Although no one answer will settle this question for all who ask, there are several important basics to keep in mind. First, as suggested earlier, keep your responses short and direct. Beyond this, it is important to connect with the questioner on a personal level; in this regard, stories and analogies are extremely effective. Examples and stories need not come from education; indeed, they can be drawn from entertainment, family life, literature, Culture 117 and even sports. Consider the following argument, originally created by Darlene Merry: TEACHER: Listen; this data-driven education thing seems interesting and all l;>ut . . . why are we doing it? PRINCIPAL: Do you watch basketball? TEACHER: Sure. PRINCIPAL: During a recent high school basketball playoff game, the score­ board completely malfunctioned midway through the game. So the refs kept the score and time on the sidelines. As it came close to the end of the game, the visiting team was down by two points, but they did not realize it nor how much time was left. The clock ran out before they took the final shot. TEACHER: That's not right! PRINCIPAL: Of course not If the scoreboard had been working, the entire end of the game could have been different. So you'd agree that a working scoreboard is critical for sporting events, correct? TEACHER: Of course. PRINCIPAL: At the end of the day, data-driven instruction is like fixing the broken scoreboard. Relying on state tests is like covering up the score­ board at the beginning of the game and then uncovering it at the end of the game to see if you won. At that point, there's nothing you can do to change the outcome! We use interim assessments to keep the scoreboard uncovered, so we can make the necessary adjustments to be able to win the game. Of com:se, you needn't use this story; indeed, this particular anecdote will only work for someone who is comfortable with a sports metaphor. But others can be drawn from almost any area oflife-baking a souffle with no timer and no thermostat, driving with nothing but a speeding ticket to tell you you're going too fast, shopping with no idea how much money you have or when you'll get more. Regardless of what story you use, creating a short, clear, and accessible explanation for the pursuit of data-driven instruction provides a powerful tool for creating a culture of excellence. 118 Driven by Data Data-Driven Culture: Five Core Drivers • Highly active leadership team: Facilitate teacher-leader data analysis meetings after each interim assessment and maintain focus on the process throughout the year. • Introductory professional development: Introduce teachers and leaders to data­ driven instruction effectively-so they understand how interim assessments define rigor and experience the process of adapting instruction based on what students did or did not learn. • Implementation calendar: Begin the school year with a detailed calendar that includes time for assessment creation or adaptation, implementation, analysis, planning meetings, and re-teaching (flexible enough to accommodate district changes and mandates). • Ongoing professional development: Align the professional development calendar with the data-driven instructional plan: include modeling assessment analysis and action planning and make it flexible enough to adapt to student learning needs. • Build by borrowing: Identify and implement best practices from high-achieving teachers and schools: visit schools and classrooms, share resources, and disseminate good strategies. THE LARGEST ROCK OF ALL: EFFECTIVE PROFESSIONAL DEVELOPMENT FOR LEADERS AND TEACHERS After establishing a calendar, the single most important element of building a data-driven culture is effective training for both teachers and leaders. Unless school leaders and teachers are given the opportunity to experience the success of data-driven instruction-and concrete strategies to implement-it is impossible to implement the changes it requires. Unfortunately, much of the existing professional development in the field of data-driven instruction does meet this framework. Part Two attempts to address this critical need in two ways: Chapter Six, "Leading Professional Development," directly lays the framework for designing effective learning opportunities for teachers and school leaders. Each professional development activity offered in this book follows the model presented in Chapter Six. Culture 119 Chapters Seven through Eleven include explicit professional development activ­ ities for each core principle of data-driven instruction that then can guide leaders and teachers in learning how to implement data -:driven instruction effectively. Each of these activities has been thoroughly tested in the field, having been used with thousands of educators nationwide. With a well-trained leadership in place, seemingly insurmountable obstacles can be overcome; without them, even ideal conditions cannot guarantee success. APPLICATION: FIRST STEPS FOR TEACHERS AND LEADERS So what is the most effective way to build a data-driven culture as a classroom teacher, school leader, or multicampus or district office leader? What follows are the first steps that could be taken to put this into action. Level 1-Teachers As a teacher, you have the most influence over the data-driven culture in your own classroom. If your school doesn't have one, set up your own assessment calendar. Visit the classes of the highest-achieving teachers you can find (within your school and in neighboring schools) to identify best practices that could increase your repertoire and make you a stronger teacher. But more than anything, focus on the key steps listed for assessment, analysis, and action (described in Chapters One through Three). Level 2-School-Based Leaders The core drivers listed in this chapter are the basic road map for your work as school leader. Listed here are just some final tips during the implementation of each of these drivers: • Professional development for leaders: It is imperative to train every leader in your building who will lead ana!ysis meetings with teachers (for example, a coach, department chair, grade-level chair, or assistant principal). Plan for a leadership retreat, or gather for a few afternoons over the summer. Take 120 Driven by Data advantage of the professional development activities listed in Part Two, with a particular focus on analysis and action. • Professional development for teachers: In the best timing, you will launch the [ school year with an introductory training on the core concepts of data­ driven instruction. Ideally you can cover the introduction, assessment, [ analysis, and action. If you have to limit your focus given time constraints, [ be sure not to skip analysis and the role playing of analysis meetings (these [ are as valuable for teachers to witness as they are for school leaders). [ • Keep the interim assessment cycle free of other commitments: Make sure the calendar during interim assessment week and the following week are free of other events and teacher duties. One concrete piece of advice: keep the report card dates far enough away so that teachers don't have to turn in grades anytime near when they turn in action plans from their assessment. This might initially seem counterintuitive as many school leaders think it ideal for the interim assessment to fall at the end of the quarter. However, that timing is unnecessary given that the assessments are cumulative and continue to measure the standards each progressive round. In tum, your teachers will thank you for not creating an unbearable week! • Ongoing professional development: The best agenda for professional development after each round of interim assessments is the results meeting protocol (see Chapter Three and the "Data-Driven Implementation Rubric" section of the Appendix). In addition, one of the most fruitful topics to address after the first round of interim assessment implemen­ tation can be "checking for understanding": how a teacher can effectively use in-the-moment assessments to check student learning on a daily basis. Culture 121 Data-Driven Success Story Lanier and Riverview Middle Schools: Building by Borrowing Together The Results Tennessee State Assessment- Percentage of Students at or Above Proficiency Figure 4.3 Tennessee State Assessment: Percentage of Lanier and Riverview Students at or Above Proficiency. Math Language Arts Riverview Middle School 1;J 1st Year as Principal The Story Math Language Arts Lanier Middle School II 2006-07 When Tiffany Hardrick began her principalship at Lanier Middle School in Tennessee (99 percent African American students with 90 percent free and reduced lunchl. she walked into a school that already had the beginnings of a data-driven culture. The previous principal had looked at data, but the analysis had been on a global scale. Hardrick 122 Driven by Data immediately led the teachers to look at student-level and question-level analysis. She launched an opening professional development session using the data-driven workshop materials provided in this book. The teachers analyzed student data from the preceding year, looking at individual student performance and determining the key first steps for that school year. They created small groups within each classroom based on student needs and their Tennessee Value Added Scores (TVAS). When each round of Renaissance interim assessments took place, the teachers dove into the data and created detailed re-teach plans according to the results. In this process, Hardrick reached out to fellow principal Keith Sanders of Riverview Middle School. who was a graduate of the same principal training program at New Leaders for New Schools (NLNSl. They both had attended the Data-Driven Instruction Comprehensive Leadership Workshop (Chapter Twelve) and were eager to put those steps into action. The two of them connected with Mark Murphy, the head of assessment for NLNS. They shared data across their schools, identifying best practices and areas in need of improvement. Hardrick brought her instructional experience in math and science, and Sanders provided leadership in English and language arts, as well as social studies. By relying on each other's expertise, theywere able to provide better feedback and support to their teachers. They even brought their teachers together for data analysis work! One of the most important steps for them was to have the teachers all predict the performance of their own students on each question a few days prior to the actual interim assessment. They then compared predicted performance with actual performance, which allowed teachers to see the disconnect between their perception of student understanding and the reality. When building re-teaching plans; they led teachers to design mini-lessons: ten minutes at the start of every class that would hit one standard with some small check for understanding. Each week, the teachers would assess whether they needed to revisit the same standard or could move on to another one. Each conversation was personalized by focusing on the specific students who were still struggling. Not only did both schools go on to make gains in 2006-07, but Sanders and Hardrick took those lessons with them as they responded to the call to launch a school in New Orleans in the aftermath of Hurricane Katrina. Miller McCoy Academy will surely benefit from theirJeadership. Key Drivers from Implementation Rubric • †Build by borrowing: There is no better example of this driver than two principals collaborating across their schools to drive achievement and share best practices. • Introductory professional development: Hardrick and Sanders started each school year with a thorough, engaging introduction to data-driven instruction and the skills of data analysis. Culture 123 f Use the Data-Driven Implementation Rubric: In the Appendix is a rubric you can use to evaluate your overall progress in implementing data-driven instruction. After the first cycle of interim assessments and then midyear, evaluate your school using this rubric. Identify the areas of weakness in your data-driven approach and develop a corresponding action plan for the leadership team. This is a great exercise for schools to do even after implementing data-driven instruction for many years: it keeps you fresh and focused on areas of improvement. Level 3-District-Level or Multicampus Leaders If districts have established effective interim assessments and analysis structures, you have done the most important things to set up principals to lead the core elements of action effectively. Your ongoing work here is to block and tackle: keep everything else away from school leaders so they can focus. on these elements. Here are some ofthe most important ways to do that: • l Professional development for leaders: It is imperative to train every principal and school leader in each of your schools. Depending on the size of your district and organization, you can train all principals and then have them train their second-tier leaders (coaches, assistant principals, and so on), or you can set up districtwide training for all school leaders. Plan for a leadership retreat, or gather a few afternoons over the summer. Use the professional development activities listed in Part Two, with a particular focus on analysis and action. If a principal is not fully trained in data-driven instruction, the initiative is likely to fail at that school. • l Make a districtwide calendar that prioritizes interim assessments first, everything else second: Just as the big rocks analogy suggests, make sure the interim assessment cycle drives the rest of the district calendar and meets the criteria established in each chapter. Keep all other events.and requests away from leaders during those critical times. • l Use the Data-Driven Implementation Rubric: As mentioned for Level 2, in the Appendix is a rubric you can use to evaluate each school's overall progress in implementing data-driven instruction. After the first cycle of interim assessments and then midyear, have school leaders evaluate their 124 Driven by Data school using this rubric and develop a corresponding action plan for the leadership team. Collect the evaluations from all the schools and look for common trends across your district as well as differences from school to school. Are your assessments not seen as aligned by your principals ( despite all your best efforts to do so at the district level)? Are schools struggling to lead analysis meetings? This evaluation can give you insight into additional professional development school leaders need and help you create a road map for districtwide improvement. Culture 125 Chapter Four: Reflection and Planning Ta.ke this opportunity to reflect upon culture at your own school or district. Answer the following questions: • After reading this chapter. what are the key action steps around culture that you are going to implement in your school(and that.you can realistically do)? • Who are the key people in your school with whom you need to communicate this plan and have on board? • How are you going to get them on board? What are you going to do when someone says no? (What's Plan B?) • Set the key dates for each action step, write them here, and then put them in your personal agenda and calendar to hold yourself accountable for implementing these steps. Reflection and Planning page copyright© 2010 by Paul Bambrick-Santoyo 126 Driven by Data
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