A Continuous Improvement Framework:

Transcription

1 A Continuous Improvement Framework: DATA-DRIVEN DECISION-MAKING IN MATHEMATICS EDUCATION Sharnell S. Jackson President Data-Driven Innovations Consulting, Inc. WHITE PAPER

2 INTRODUCTION Educators face ever-increasing pressure from federal, state, and local accountability policies to improve student achievement. In 2010, the Race to the Top Assessment Program funded two state consortia to develop new systems that measure student skills from third grade through high school against a common set of college- and career-ready standards in mathematics and language arts. The development of these effective measurement systems is resulting in an unprecedented opportunity to initiate major changes in teaching and learning practices, all of which depend on reliable, comprehensible data. There are several components in this more data-driven environment: systematic data use processes, intelligent adaptive learning programs, consistent professional training and development opportunities, and strong leadership. To access what students are learning and how they are progressing, educators can now use a continuous improvement framework for data-driven decision-making to organize people and processes to reach education objectives. 2

3 THE ECONOMIC NEED FOR EFFECTIVE USE OF DATA In today s global economy, a high-quality education is no longer simply a pathway to opportunity, it is a prerequisite to success and economic viability. It is now a national imperative that every student in this country should be able to graduate from high school fully prepared for college or a career. As such, President Obama has advanced reforms centered on four key objectives: Higher standards and better assessments that will prepare students to succeed in college and the workplace. Ambitious efforts to recruit, prepare, develop, and advance effective teachers and principals, especially in the classrooms where they are most needed. Smarter data systems that measure student growth and success, and help educators improve instruction. New attention and a national effort to turn around our lowest-achieving schools. In 2009, John Easton, the director of the U.S. Department of Education s Institute of Education Sciences stated, Data and analyses are powerful tools that must be used to improve schools (Easton, 2009). Encouraged in part by federal legislation and funding, states and school districts are increasingly focused on building longitudinal data systems and using interim assessments to identify gaps in achievement and monitor growth. Additionally, increasingly stringent standards regarding accountability, testing, teacher and principal evaluation policies, and the ability to access student-level data have reinforced the need to leverage data to inform instruction and improve student learning. Data-Driven Decision-Making Benefits In 2010, Arne Duncan, the U.S. Secretary of Education emphasized, Our best teachers today are using real time data in ways that would have been unimaginable just five years ago. They need to know how well their students are performing. They want to know exactly what they need to do to teach and how to teach it. Districts and states across the country are implementing data-driven decision- making processes not only to analyze test scores and student achievement, but also to boost achievement by personalizing learning to unlock potential. 3

4 Framework for Data-Driven Decision-Making If daily use of data-driven decision-making by classroom teachers and administrators is the goal, a data analysis framework that includes protocols, processes, roles, and responsibilities is fundamental. The critical framework contains five elements to facilitate more informed practices, accelerate overall school performance, and improve student achievement. The five elements are leadership, data coaches, data teams, establishing a data culture, and technology use; each of these is explained in detail below. Ultimately though, success depends on leadership that creates an effective vision based on teachers adoption of a systemic data-driven process that includes blended learning tools and strategies to personalize students learning. 1. LEADERSHIP With growing accountability for student performance and new evaluation systems linked to student outcome data, principals and other education leaders are now expected to be datadriven. For example, candidates for principal positions are often asked to review case studies with a fictitious set of data, and then analyze and apply that data to develop a comprehensive school improvement plan. This exercise is meant to demonstrate the candidate s competency with analyzing data and establish his or her problem solving ability. The primary role of school and district leaders in a data-driven instruction framework is to provide a vision: define the purpose, set expectations for data use, and build agreement within the school. This vision paves the way for the creation of a culture that uses data to inform instruction. The resources needed to help establish a data culture include fostering data use, appointing a data coach, setting time aside for collaboration, and modeling effective data use. 4

5 Leaders who are uncomfortable or incapable of modeling data use because of a lack of knowledge and skills or lack of interest or motivation should recognize their limitations and employ a model of distributed leadership (e.g., by appointing a data coach) to empower school staff members to effectively use data. 2. DATA COACHES The data coach, also referred to as a facilitator or mentor, is perhaps the most important person within a school to make data use a reality. A data coach is the knowledgeable go to person who takes responsibility for integrating data and modeling school data use. About two-thirds of schools currently have a data coach whose responsibilities may include creating a data team, facilitating data use, helping to collect, analyze, and interpret data, and providing training to other school staff members to build a team model of professional development (Lachat & Smith, 2005; Love, Stiles, Mundry, & DiRanna, 2008). The data coach may be a lead teacher, a content specialist, or an administrator. The data coach may even be a retired educator, rehired to work with existing staff. Whatever the data coach s background, in addition to data knowledge, the individual must also be someone who works easily with others and can help translate numbers into actionable pedagogical knowledge. Researchers have identified five specific ways data coaches engage and lead (Love et al., 2008): Build a foundation by understanding school needs and how data can be applied to those needs. 1. Building a foundation by understanding school needs and how data can be applied to those needs. 2. Identify the problems or student learning issues to which data can be applied. 3. Verify the possible causes of the problems. 4. Facilitate the development of possible solutions. 5. Implement the solutions and monitor outcomes. 3. DATA TEAMS Even the best data coach can t do it alone. Using data in the classroom is essential, but equally important is allotting time for teachers to learn from each other. Collaboration is a vital component in the implementation of data-driven practices, such as discussing pressing problems around student learning, or working together to find possible instructional strategies to remediate student-learning concerns. Surprisingly, while 90 percent of schools report that educators feel comfortable collaborating in this way, only 59 percent actually understand how to work with colleagues in practice (Datnow & Park, 2010; Hamilton et al., 2009; Long, Rivas, Light, & Mandinach, 2008; Wayman, 2005). The best way to successfully collaborate is to identify a team, set expectations, identify structured collaborative meeting times, and adopt a systematic process for data analysis to 5

6 improve teachers ability to meet students individual learning needs. Team identification. A school s data team is comprised of individuals who are tasked with collaborating to collect, analyze, and interpret data. Data teams take various forms: they can be horizontal collaborations that team at a grade level or a course level, or they can be vertical teams that span grade levels. They can be content-based, grade-based, course-based, or take other logical teaming forms centered on established goals. Data teams can also function across schools within a district. Set expectations. Beyond data collection and analysis activities, data collaboratives are responsible for developing common formative and summative assessments, building datasupported action plans, and assisting with communicating results to staff and stakeholders. They help supply the administration with quality data needed for communication with parents and the community, and they respond to data requests from fellow staff members. Meeting times and support. To be most productive, data teams should spend at least 45 minutes per week together, and it is imperative that this designated time is supported and protected by the school administration. Also, before the work even commences, there should be an agreement that no topics are out of bounds that there should be no limit to the discussion that is so necessary to achieving continuous improvement. Educators need to know that it is both appropriate and safe for them to share information without the possibility of negative consequences. Data analysis meeting process and protocol. For more effective collaborative inquiry and action planning, data team sessions can be facilitated with a seven-stage data analysis meeting protocol process. 6

7 PROCESS 1. FOCUSED QUESTIONS 2. INTERPRET DATA AND IDENTIFY GAPS 3. ANALYZE ROOT CAUSES FOR GAPS DATA ANALYSIS MEETING PROTOCOL How are your students performing by subject level? What are the trends in student performance over time? How are subgroups performing over time? What are your strengths and weaknesses in teaching and learning? Can you evaluate the effectiveness of the curriculum, teaching, student learning, professional development, programs, and instructional strategies? Analyze mastery differences between student groups and individual students Analyze commonly missed items Analyze common wrong answer choices on assessments Examine student work to reveal discrepancies in expectations and content coverage Does the problem reside in the: content or subject matter to be learned? teachers and teaching processes used? learners and the learning processes used? context or setting in which the learning was to occur? 4. RULES FOR ROOT CAUSE ANALYSIS PROCESS Data team members can suggest many different hypotheses Hypotheses will be considered if they can be corroborated with data Data can be previously acquired or recently collected Theories-of-cause remain in potentially valid category only while data supports the hypothesis 5. ROOT CAUSE ANALYSIS PROCESS 6. DEVELOPING A TESTABLE HYPOTHESES 7. DETERMINE EFFECT OF INSTRUCTIONAL INTERVENTION Examine the most recent assessment Pay specific attention to variability in student scores Brainstorm explanations for the performance of the lowest-achieving onethird Describe data that can be used to determine whether a hypothesis is true Brainstorm why the valid hypotheses are true Interpret data and develop a hypothesis about how to improve student learning Identify a promising intervention or instructional modification Ensure that the effect can be measured Compare multiple sources of data across classrooms Determine supporting data to verify evidence of the hypothesis Identify comparison data to determine instructional effectiveness of the intervention Gather classroom-level data to quickly evaluate student performance after the intervention Consider the extent to which student learning did or did not improve in response to the intervention 7

8 4. ESTABLISHING A DATA CULTURE Creating a school data culture requires the consideration of many aspects, such as concerns surrounding time, skills, collaboration, and the provision for formal structures that support enculturation of data. Love and colleagues (2008) outlined seven steps needed for developing a data culture: 1. Enculturate the notion of continuous improvement 2. Build support from stakeholders 3. Strengthen collaboration 4. Empower a data coach 5. Organize a data team 6. Create time for collaboration 7. Provide timely access to data As data use becomes enculturated into a school, two issues dominate. The first concerns the kinds of data that educators want to use and have available. The second is the skill set that is required as part of the inquiry cycle of data use. Using multiple data sources is a fundamental principle of accurate measurement. For example, consider the case of a student who takes a standardized test while either ill or tired. The resulting score may not reflect a true assessment of the student s understanding of the material. But including other sources of data when evaluating that student will enable the teacher to gain a more comprehensive picture of what that student knows or does not know. Another use for multiple data sources is to assist in resolving data conflicts. Teachers are often confronted with data sources that tell different stories; perhaps the state summative test and more local assessments do not agree. Taken separately, no simple measure gives an adequate picture of the student. The triangulation, or bringing together of these multiple sources of data, is an effective data use strategy. 8

9 ANNUALLY SEMI-ANNUALLY SUMMATIVE STATE AND DISTRICT ASSESSMENTS OF LEARNING DATA ABOUT PEOPLE, PRACTICES, PERCEPTIONS Aggregated, disaggregated, strand, item, and student work Demographic, enrollment, attendance, behavior, surveys, interviews, observations, curriculum maps, parent meetings, phone calls, and cumulative folders QUARTERLY MONTHLY WEEKLY BENCHMARK SCHOOL-WIDE COMMON ASSESSMENTS FORMATIVE SCHOOL-WIDE COMMON ASSESSMENTS FORMATIVE CLASSROOM ASSESSMENTS FOR LEARNING End of unit, common grade and subject-level tests reported at the item level analysis Math problems, writing samples, science journals and lab work, class project blogs and wikis, and other student work Student self assessments, descriptive feedback, selected response, written response, personal communications, performance assessments, class work, homework, running records, and other student work Data sources. Assessments formative, benchmark, and summative scheduled assessments provide valuable, measurable data. However, data other than assessments can also help form a more comprehensive picture of a student s performance. For example, a data warehouse that holds a variety of demographic information can help more fully inform decision-making. Data might include attendance, absence, behavioral transgressions, health-related data, and familial circumstances. Knowing if the student has extended absences, suspensions, illnesses, or problems at home can provide valuable contextual information for a teacher. Timeliness and utility. The most critical factor in data utility is timeliness; the feedback loop must be close between the time an assessment is administered and when data is delivered back to inform teacher instruction. For example, state summative tests often produce data after the learning has occurred. Typically, testing is completed in the spring and the data reports are delivered in the fall for the new school year. This lag diminishes the utility of the data. This substantial delay in the feedback loop 9

10 makes the data less informative and useful to the teacher. In contrast, there are diagnostic data that can be delivered to the teacher immediately so that instructional strategies can be modified on the spot. The U.S. Department of Education is currently working toward the development of new assessments that will ultimately replace the traditional test models. These new tests are being designed to assess complex skills and deliver timely results that can be used more effectively by administrators and teachers. INQUIRY CYCLE As part of their process of collaborative inquiry, data teams should implement a cycle of inquiry that becomes a consistent component of school procedures. This seven-step inquiry cycle is a means to test assumptions and construct an understanding of student learning gaps: Enhancing the knowledge and skills of teachers and administrators requires continuous collaborative professional learning opportunities, with easy access to intelligent adaptive online learning systems, assessment systems, data systems, and technological tools. These technologies can be used to inform instruction, access online assessment systems with progress monitoring tools, and use emerging mobile technologies to personalize students differentiated instructional needs. Teachers need to acquire the data literacy skills and ability to translate acquired data into actionable instructional knowledge, which is the foundation of data enculturation. 10

11 THE ENCULTURATION PROCESS Enculturation is based on the notion that inquiry is an essential component of professional practices, with a goal of a sharing a mutual vision and understanding of objectives and strategies. The creation of a data culture means that educators will assume internal responsibility for data use, and employ collaborative inquiry for continuous improvement. Requirements. The enculturation process includes the need for explicit norms and expectations, with measurable objectives in which there is an assumption that decisions will be made from the use of data. Further, school staff must develop an understanding of how data can be used to inform practice. Through iterative inquiry cycles, educators must use data-informed questions to stimulate the process of continuous improvement it is about matching the right solutions to the right questions regarding student learning. Another part of the enculturation process is the sharing of data and strategies between teachers and even between schools within a developed culture of trust and security. Six-step process. Researchers have outlined a six-step process for enculturating data within a school (Love et al., 2008; Datnow, Park, & Wohlstetter, 2007): 1. Schools must lay a foundation for data-driven decision-making 2. There must be an emphasis on continuous improvement 3. Schools must incorporate the use of an information management system 4. They must select the right data 5. They need to build capacity 6. The school needs to analyze and act on data to improve performance Collaboration is vital. Collaboration and having the time to collaborate is of ultimate importance, and teachers need time to come together around data. According to teachers (Mandinach & Gummer, 2010), structured collaborative time is the greatest challenge they face when adopting data-driven instruction. How can data inquiry be integrated into a schedule that is already full and often restricts required meetings beyond typical school hours? There are several issues. School calendars are structured with little flexibility to make changes. There is a perception that asking educators to use data will take more time. Data-driven decisionmaking is often seen as a trade-off between data use and instructional planning, when in reality it is fundamental to an informed systematic planning process. 11

12 In practice, the collaboration must be mandated by school administrators, with regular, planned time set aside for data inquiry. These structured opportunities provide important points to reflect and discuss data, generate potential instructional strategies, share successes and failures, and receive constructive feedback from colleagues. Visits and classroom observations are additional opportunities to learn from colleagues. Other support structures. As noted earlier in this paper, data team meetings are the most prevalent collaborative opportunities. Other strategies include discussing data in full faculty meetings and convening data retreats. These should be formal, but can also be informal and impromptu. Some schools have weekly, structured meetings in which topics around data are discussed, as well as daily planning meetings within grades and courses to support collaboration and to achieve enculturation of data use. Schools and districts need supportive structures and data analysis protocol training to help their staff acquire data literacy skills. Data coaches and data teams who receive early training can use a train-the-trainer model to then help colleagues to use data. For teachers, principals, technology specialists, and other staff, training should be differentiated; and ongoing and continuous professional development is preferred. These opportunities need to be immediate and explicitly relevant to practice. Other support structures for data enculturation can also be considered, such as modifying future hiring criteria to include data expertise. Criteria characteristic for technology acquisition should be accessible, comprehensive, flexible, have a reasonable feedback loop to monitor progress data aligned to classroom needs, and a link to standards-based instruction. There should also be incentives for educators to use data. Stipends are always good, but there are other possibilities, such as release time for a course. Schools need to be creative in these times of economic constraints to empower and motivate teachers. 12

13 5. TECHNOLOGY USE Technologies to support data-driven decision-making hold great promise for increasing the efficiency of education agencies at all levels by enhancing the effectiveness of teaching and learning activities, accelerating student achievement, and improving administrative, programmatic, and organizational performance. They have the potential to engage students and capitalize on learning styles. Educators need comprehensive data coaching and professional learning opportunities to build their capacity to use data and access the technology systems that support data-driven decision-making. These systems are vital to the successful implementation of data-driven practices, which can identify and promote how technology supports educational practices through a clear vision for communication and collaboration with district instructional leadership (Mandinach & Jackson, 2011). Having a technology system to support data-driven decision-making is one of the five recommendations in the IES Practice Guide: Using Student Achievement Data to Support Instructional Decision Making. An increasing number of student information systems, assessment systems, instructional management systems, data warehouses, and other emerging technologies are being used as efficient means by which educators can access, collaborate, communicate, and use student data for continuous school improvement. Four of the technologies that most directly relate to data-driven mathematics instruction are progress monitoring tools, data dashboards, online and blended learning environments, and mobile learning and assessment. PROGRESS MONITORING TOOLS The use of handheld tools and mobile computing devices in schools began several years ago when federal requirements around the Reading First program mandated that teachers use diagnostic assessments to monitor the progress of students in kindergarten through third grade. To meet the requirements, companies developed technology applications to run on handheld and mobile devices that delivered literacy and mathematics assessments which teachers could use to administer the assessments, record the answers, and get immediate feedback about a student s performance all to enable further analysis and reporting. The teacher could then see the trajectory of an individual student s learning and ability over time, noting if the student was performing above or below expectations, and had the ability to monitor full class performance, while a curriculum supervisor or principal could then compare performance across multiple classes. The strength of mobile computing devices is the capability to deliver immediate diagnostic results so teachers can make real-time instructional modifications to personalize student learning. This places assessment squarely in the middle of instruction, where it s potential can count the most in increasing student achievement. DATA DASHBOARDS Dashboards are interactive collections of charts, gauges, reports, and other visual indicators that educators have selected to monitor. Unlike a scorecard that offers a snapshot of data at a particular moment in time, dashboards are more effective at viewing data in a specific period of time and over time. They are used to monitor daily operations and performance progress because the data and information are delivered in real time. 13

14 With districts placing even greater importance on whether schools meet state and national standards, data dashboards or performance management dashboards can provide an overall view of a school for administrators, and principals can focus on those power standards that are critical metrics by which schools are measured on the state report card. The dashboard interface allows administrators to identify and focus on standards that are highly valued, or where a school has experienced low success. The communication of the vision and targeted objectives assists teachers in adopting a data-driven process for identifying standards that need to be incorporated into instructional plans based on the highlighted dashboard data. Thus the dashboard facilitates educational and technology best practices through communication, collaboration, and continuous progress monitoring with instructional leadership. In the classroom, dashboards give teachers access to historical, real-time, and predictive assessment data on all students, to help manage academic performance and anticipate problems that could arise throughout the year. The process of data collection is automated, so teachers don t have to spend hours sifting through reports; the technology allows them to eliminate the labor-intensive process of poring over grid paper, spreadsheets, charts, data walls, and pivot tables which have historically required enormous amounts of instructional and personal time. The ability to combine multiple measures of assessment data to compare and identify each student s learning problems saves time as well. Teachers can access summarized, useful information via a single dashboard on their computers when it is most convenient. Because they spend less time accessing and analyzing data, they gain more time to focus on meeting the individual math learning needs of each student. By receiving information on a regular basis, teachers can reflect on their instructional practices, adjust their instruction, and address the actual identified needs of their students. ONLINE AND BLENDED LEARNING ENVIRONMENTS Online learning management systems record all teacher and student interactions. These streams of student data provide feedback loops, in which data can be used to drive instruction, and to meet and accelerate individual student learning needs. Online and blended learning environments are being used for a variety of purposes. Teachers use data from the interactions with blended learning environments to drive instruction and monitor student learning progression. They can assess the understanding of learning objectives with embedded assessments, create and facilitate group discussions, develop 14

15 group projects, make regular adjustments to course resources based on student learning data, and respond to students individual learning needs and questions all through the virtual environment. A good example is DreamBox Learning s Intelligent Adaptive Learning which delivers millions of individualized learning paths, to tailor every math lesson to meet each student s unique needs. The DreamBox adaptive online learning environment ensures that students are always working in their optimal learning zone. It recognizes more than just their yes or no answers with every mouse click it evaluates student strategies and immediately adjusts the lesson. DreamBox can adapt the level of difficulty, scaffolding, sequencing, the number of hints, the pacing all in real time allowing students at all levels to progress at a pace that suits them. MOBILE LEARNING AND ASSESSMENT A major problem facing K 12 education in this country is student disengagement. As a result, a growing number of students are dropping out of school before graduation due to lack of rigor, relevance, engagement, and differentiated instruction. One way to become relevant is to adopt the way students communicate via social networking. Mobile computing devices such as smartphones, electronic tablets, ipads, and netbooks have become pervasive throughout society, and provide a familiar and meaningful opportunity to communicate and collaborate inside and outside of school. Mobile devices connect teachers and students alike to the Internet with substantial computing power, anytime and anywhere. These technologies can be used in schools to provide easy access to the technology solutions that students constantly use, and to promote learning and enhance engagement. Ironically, some school districts have prohibited the use of phones and other communication devices while on school premises. The mobile devices, however, do have the potential to equalize the digital divide because many students, regardless of economic status, have access to smartphones. While some forbid the devices, other school districts are allowing students to bring your own device (BYOD) to use in school for assignments and project-based work to level the playing field in using technology in school. Schools must find ways to use data to drive instruction by delivering content and assessing student learning in more systematic ways to capitalize on the affordances and strengths of mobile computing devices and smartphones to address the engagement. These devices can help students work at their own pace using a medium that is familiar and comfortable to extend, engage, expand, and unlock potential learning opportunities. 15

16 CHALLENGES The establishment of data-driven practices involves some common challenges (Armstrong & Anthes, 2001; Mandinach, 2009a, 2009b; Wayman, Snodgrass Rangel, Jimerson, & Cho, 2010; Heritage, 2007) including: Lack of pedagogical data literacy: inability to translate data into actionable instructional knowledge Resistance: educators may find data-driven decision-making too hard and need to be convinced to expend the time needed to make it work Unprepared leadership: principals are many times not prepared to help in the data-driven process Disconnection: between teachers and principal expectations Time constraints: lack of time or structured time to collaborate Lack of human capacity: around data-driven decision-making A need for more instructionally valid data sources: primarily common school-wide formative assessments that are better aligned to teaching and learning needs To make the shift to a data-driven culture also requires fundamental infrastructure that includes the technological tools to support data-driven decision-making, as well as data literacy among the staff to include support structures, systematic processes, and resources that facilitate to develop, implement, and sustain a data culture. THE ULTIMATE GOAL: MEETING PERSONALIZED LEARNING NEEDS To meet the national 21st century goals, education must be evidence-based and data-driven. Educators need to be armed with data to inform their practice as a regular component of their work. Then there will be true enculturation, based on the ability to build a culture that values data-driven instructional practices. It would be a culture that recognizes that, with time and experience, data-driven instruction and inquiry becomes an embedded part of practice, not an add-on that takes time away from actual teaching. It becomes an integrated process where multiple measures of data inform instruction, monitors progress, and personalizes student learning. 16

17 Multiple Measures of Data to Drive Personalized Learning Needs Technology provides invaluable resources and tools for educators at all levels of the educational system. It is now contingent upon educators to harness these tools and the data they provide to inform their daily instructional practices, and to increase academic rigor and growth, enabling students of all abilities to enjoy learning, unlock their potential, and achieve proficiency. Instead of teaching to the average student, teachers need to be able to use rich, interactive adaptive learning systems with sophisticated analytics, aligned to common core standards, comparing multiple measures of assessments to make informed decisions, assigning personalized content with instantaneous feedback, identifying instructional interventions, and utilizing emerging technologies to accelerate personalized learning for all students. 17

18 SHARNELL S. JACKSON PRESIDENT DATA-DRIVEN INNOVATIONS CONSULTING, INC. Jackson is an educational leader with over 34 years of exceptional results in data-driven decision-making and educational innovation as a classroom teacher, building administrator, district administrator, and consultant. As former Chief elearning Officer of Chicago Public Schools, Jackson and her team implemented innovative instructional management, assessment, digital media, and blended on-line learning systems to accelerate student motivation, proficiency, and progress. A noted author and consultant, she is an adviser to the U.S. Department of Educational Regional Educational Laboratories, state departments, national superintendent, administrator, and teacher associations, school districts, foundations, and corporations. Her vision and mission is to help teachers and administrators adopt a systematic process for using data to improve their ability to meet student learning needs and help them develop the grit and focus to solve difficult problems on their own through transformative innovation technology systems that personalize student learning. References Blackboard K 12. (2010). Learning in the 21st century: Taking it mobile! Washington, DC. Retrieved from Bruff, D. (2009). Teaching with classroom response systems: Creating active learning environments. San Francisco, CA: Jossey-Bass. Halverson, R., & Thomas, C. N. (2007). The roles and practices of student services staff as data-driven instructional leaders. In M. Mangin & S. Stoelinga (Eds.), Instructional teachers leadership roles: Using research to inform and reform (pp ). New York, NY: Teachers College Press. Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers ability to use data to inform instruction: Challenges and supports. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. Bibliography Armstrong, J., & Anthes, K. (2001). How data can help: Putting information to work to raise student achievement. American School Board Journal, 188(1), Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high-performing school systems use data to improve instruction for elementary students. Los Angeles, CA: University of Southern California, Center on Educational Governance. Datnow, A., & Park, V. (2010, May). Practice meets theory of action: Teachers experiences with data use. Paper presented at the annual meeting of the American Educational Research Association, Denver, CO. 18

19 Duncan, A. (2010, November 16). Secretary Arne Duncan s remarks to National Council for Accreditation of Teacher Education. Retrieved from Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE ). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from guides/ Heritage, M. (2007, June). Formative assessment in the classroom. Presentation to the EED Winter Conference, Informing Instruction, Improving Achievement, Anchorage, AK. Lachat, M. A., & Smith, S. (2005). Practices that support data use in urban high schools. Journal of Education for Students Placed at Risk, 10(3), Long, L., Rivas, L., Light, D., & Mandinach, E. B. (2008). The evolution of a homegrown data warehouse: TUSDStats. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning (pp ). New York, NY: Teachers College Press. Love N., Stiles, K. E., Mundry, S., & DiRanna, K. (2008). A data coach s guide to improving learning for all students: Unleashing the power of collaborative inquiry. Thousand Oaks, CA: Corwin. Mandinach, E. B. (2009a, October). Data use: What we know about school-level use. Presentation at the Special Education Data Accountability Center Retreat, 2009, Rockville, MD. Mandinach, E. B. (2009b, October). How LEAs use data to inform practice: The opportunities for and challenges to use in schools and districts. Presentation at the National Evaluation Institute Research and Evaluation that Inform Leadership for Results Conference, Louisville, KY. Mandinach, E. B., & Gummer, E. (2010). An examination of what schools of education are doing to improve human capacity regarding data. Proposal to the Spencer Foundation Data Initiative. Washington, DC, and Portland, OR: WestEd and Education Northwest. Mandinach, E. B., & Jackson, S. S., (2011). Transforming Teaching and Learning Through Data-Driven Decision Making. Thousand Oaks, CA: Corwin. Wayman, J. C. (2005). Involving teachers in data-driven decision-making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Student Placed at Risk, 112(4), Wayman, J. C., Snodgrass Rangel, V. W., Jimerson, J. B., & Cho, V. (2010). Improving data use in NISD: Becoming a data-informed district. Austin, TX: University of Texas. 19

20 LEARNING DreamBox Learning was founded in 2006 in Bellevue, Washington, and is transforming the way students learn mathematics through its groundbreaking combination of Intelligent Adaptive Learning, rigorous elementary mathematics curriculum, and motivating learning environment. DreamBox Learning Math is designed to teach and reinforce key mathematical concepts through effective, individualized instruction in an engaging and fun manner, and is aligned with the Common Core State Standards. The platform has won more than 20 top education and technology industry awards and is in use in all 50 states. Learn more about DreamBox Learning at dreambox.com. For more information, contact Client Care at , or visit dreambox.com. 20