The Data Coach's Guide to Improving Learning for All Students. Katherine E. Stiles

The Data Coach's Guide to Improving Learning for All Students - Katherine E. Stiles


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“raising the achievement of all students while narrowing the gap between the highest- and lowest-performing students and eliminating the racial predictability and disproportionality of which student groups occupy the highest and lowest achievement categories” (p. 46). Equity does not mean that all students receive an equal level of resources and support, but that those of the greatest need receive the level of support they need to succeed.

      A collaborative community committed to equity requires a high level of trust. In high-functioning cultures, educators trust each other enough to discuss “undiscussables” such as race, reveal their own practice and mistakes, root for one another, and face together the brutal facts that data often reveal (Barth, 2006). For all of these reasons, districts that make the most of their investment into data management systems place an equal or greater priority in strengthening school cultures and the ability to respond to the data.

      Assumption 5:

       Using data itself does not improve teaching. Improved teaching comes about when teachers implement sound teaching practices grounded in cultural proficiency—understanding and respect for their students’ cultures—and a thorough understanding of the subject matter and how to teach it, including understanding student thinking and ways of making content accessible to all students.

      It is easy to get swept away in the data-driven mania provoked by federal and state education accountability policies, where data can sometimes seem to be an end in themselves. But test results, lists of “failing” schools, bar graphs, tables, proficiency levels, even student work do nothing by themselves to improve teaching unless they spark powerful dialogue and changes in practice. For example, it doesn’t take hours of data analysis to discover that students struggle with solving nonroutine mathematics problems or reading informational text. But talking about and learning more and more about what to do about those problems does take time and is where teams gain momentum for instructional improvement.

      Questions like the following merit as much time in Data Team meetings as does the actual data analysis:

       Who among us is having success and what are they doing?

       What does research say about how students learn this content or what typical misconceptions they struggle with?

       What have other schools done to solve this problem?

       What would a culturally proficient approach to this content look like? What content knowledge and pedagogical content knowledge will strengthen our ability to teach this content? What does the research base on effective teaching tell us?

       What kind of professional development will help us learn these skills and knowledge?

      The data are just the tip of the iceberg, alerting us to problem areas and reminding us that what lies beneath is what counts—the curriculum, instruction, assessment, and professional development practices that will improve student learning. Data use is not a substitute for the hard work of improving instruction. Throughout the Using Data Process, Data Teams are guided to draw on and add to the rich knowledge base about teaching and learning.

       Leaders matter. Therefore significant change in organizations begins with significant changes to what leaders think (depth of understanding and beliefs), say (the speech forms we use and the content of our speech), and do (a continuous flow of powerful actions within a culture of interpersonal accountability).

      —Sparks, 2005, p. xii

      Assumption 6:

       Every member of a collaborative school community can act as a leader, dramatically impacting the quality of relationships, the school culture, and student learning.

      The Using Data Process supports and promotes distributed leadership, where all staff members take full responsibility and do their parts to get the job—academic success for all students—done. Marzano, Waters, and McNulty (2005) identified 21 leadership behaviors correlated with student academic achievement. Virtually all of these 21 responsibilities, which include celebrating accomplishments, challenging the status quo, fostering shared beliefs and community, staying focused on goals, communicating ideas and beliefs, actively engaging others in decision making and instructional improvement, and fostering strong relationships, are functions of Data Coaches and Data Team members as well as of school and district administrators. In particular, data use is no longer a specialty of the assessment or central office or the principal. Everyone in the school understands and uses data in ways that contribute to instructional improvement.

      Becoming a Data Coach and building Data Teams is all about developing the ability to think, speak, and act differently—to act as courageous leaders. Educators we work with often ask us, “How do we deal with resignation in our schools?” or “How do we get more people to believe that all students can learn?” One answer is to be full of possibility yourself, to frequently, succinctly, and clearly articulate what you believe, and to consistently act on those beliefs. We have seen Data Teams shift their direction completely when one team member took a clear stand against tracking students and provided evidence of its damaging effects. In Johnson County, Tennessee, the former superintendent, Mrs. Minnie Miller, and other district leaders consistently communicated two messages: “the little engine that could” and “what is is.” Virtually everyone in the district knew what they meant: “All kids can, do, and will learn,” and “stop focusing on what we have no control over.” When asked to what they attributed their dramatic success—virtually closing the gap between students with exceptional needs and general education students in one year, in all grade levels and every content area—teachers and principals consistently reported that they were inspired by those two messages.

      A Word About Our Language

       The historic legacy of education in America is rooted in acts of separation and inequality, and these attributes currently operate in the lexicon of the education profession.

      —CampbellJones and CampbellJones, 2002, p. 135

      In this book we avoid the terms “minority,” “economically disadvantaged,” and “culturally disadvantaged” because they reflect and reinforce stereotypes of those who are not White and middle class as “different” or “less than” (CampbellJones & CampbellJones, 2002; Lindsey, Nuri Robins, & Terrell, 2003). Even a seemingly benign term like “subgroup” connotes “less than” or “under.” The term “achievement gap” communicates that African American, Latino/a, or Native American students do not achieve as well as White or Asian students, but ignores the legacy of racist practices that underlie this outcome. The achievement gap might better be called a “testing gap” or a “racial teaching gap.” The very fact that we struggled as authors to find language that is both respectful and acknowledges the reality of race testifies to the grip that the language of oppression has on our vocabulary.

      In this book, we use “groups” or “student populations” instead of “subgroups” to refer to the disaggregation of the total student population into groups based on race/ethnicity, language, economic status, gender, mobility (students moving from school to school), and educational status (students with exceptional needs and the general education population). We use the terms African American, Native American, Latino/a or Hispanic, and Asian, which, though imprecise, are used commonly in the equity literature and are generally, although not universally, preferred by members of those groups (Lindsey, Nuri Robins, & Terrell, 2003). (For more on identifying student groups, see Chapter 3, Task 2.) Although we are ambivalent about the term achievement gap for the reasons stated earlier, we use it to name the current reality with the caveat that it is important to examine what is in the gap—a long history of institutional racism in society as a whole, low expectations, overt and covert discrimination, and biased testing, to name just a few causes of the gap.

      How to Use This Book

      Organization


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