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Introduction

It is 3:15 p.m., and several members of the fourth-grade team at Gardenview Elementary are late, as usual, for the scheduled 3:00 p.m. meeting. They eventually straggle in, some with the materials for exploration, some without. Those without their class rubrics need to go back to their classrooms to retrieve them. After a few greetings and a few grumbles, the conversation gets started.

This week’s facilitator suggests the members look at the student results for word choice based on the rubric they constructed for expository writing.

“My class was all over the place in this skill set, how about yours?”

“My kids didn’t do very well. I think we should create word walls in every classroom to build vocabulary.”

“Before we do that, I think we should create some common vocabulary lesson plans.”

“Yeah, but we should include word walls in them.”

“And then we could give another assignment to see if the results are the same.”

“Why do we need to teach exactly the same way? I’d like to do more integrated vocabulary building, and we’re not all teaching the same social studies or science units.”

The facilitator struggles for the group’s attention and says, “Wait, before we start fixing, we should look at all the rubrics.”

But at that point, the clock strikes 4:00, and the meeting adjourns.

This group, like many struggling groups, is limited by its lack of structure, shared goals, and skill with collaborative analysis of data. Such teams flounder because they try to operate without protocols and because they lack the communication skills for managing sensitive conversations about student learning and current teaching practices. Often they are trapped by a narrow definition of data as test, state, or provincial scores, and as a result, the types of data they examine constrain rich, collaborative conversations and important discoveries about student growth. These data are too far from the local classroom and individual learners to stimulate powerful conversations about practice. Unfortunately, the pressure to produce growth—growth as measured by these scores in particular—drives the team to limit its collaborative conversations to these high-stakes data sources. Pressured groups then focus on targeted interventions and test-taking skills to move a few students from one level of proficiency to the next, not on developing deep changes that produce rich learning for all.

The Promises and Problems of Collaborative Cultures

As in the opening scenario, school teams confront three common dilemmas in their work with data. These dilemmas present technical, personal, and social challenges for individual group members and for the group as a whole: (1) committee without community, (2) time without tools, and (3) data without deliberation.

Committee Without Community

Being in the room doesn’t mean individuals necessarily identify as members of the group or think of themselves as interlocking parts of the whole. Professional identity as a solo practitioner conflicts with a sense of collective responsibility for student learning and a commitment to collaborative exploration of data, options, and actions. Student results as a shared responsibility and instructional repertoire as a common toolkit are radical notions for teachers who view their primary workplace as the classroom and not the school.

Group members avoid tough-to-talk-about topics when they lack the relational skills to manage the mental and emotional demands of improving student learning. Moving from my students and my work to our students and our work requires clear purpose, safe structures, and compelling data that present vivid images of the effects of teachers’ work. This shift from individual perspective to collective perspective is the heart of collaborative inquiry as teacher teams search for the patterns and practices that produce learning success for all students.

Moving from my students and my work to our students and our work requires clear purpose, safe structures, and compelling data that present vivid images of the effects of teachers’ work.

Time Without Tools

Structural change is not cultural change. Simply altering the schedule to provide time to meet does not create conditions for learning or increase enthusiasm for the demands of collaborative engagement. Protected time without productive use builds resentment when group members feel that they are being kept from their real work back in the classroom.

Front-loaded training is a necessary but insufficient resource for developing fluency and confidence with the skills of collaborative inquiry. To institutionalize patterns of thoughtful practice requires the group’s ongoing attention to goal setting, self-assessment, collective assessment, reflection, and redirection.

Data Without Deliberation

Data-rich environments in and of themselves do not produce robust improvements in instructional practice and student learning. Milbrey McLaughlin (2011) suggests:

A significant obstacle to the collaborative, ongoing, and frank discussions about data and student progress found in strong teacher learning communities lies in teachers’ general lack of knowledge about how to understand the data available to them, how to develop assessments of student progress specific to their classrooms, and how to link data to action. (p. 67)

Collaborative inquiry is complex and often stretches the capacities of many groups. When group members do not embrace a spirit of inquiry, habits of judgment and critique constrain both group growth and effective problem solving. As a result of these limitations, groups tend to simplify problems and apply narrow solutions, rather than embrace the messiness of tenacious issues.

Collaborative inquiry is a value as much as it is a skill set. Its true value emerges from the daily disciplines of practice, persistence, and attention to process. Skilled data use influences group development, and simultaneously, group development influences skilled data use. Patient and thoughtful groups learn to trust the process, their data, and one another.

High-performing teams systematically collect and use data to drive cycles of problem solving, planning, action, and reflection to both improve their own collaborative practices and improve instruction that makes a difference in student learning. Conversely, when teachers work in isolation without the grounding that data or collegial perspectives provide, they tend to rely on habit and make decisions based on anecdotal evidence and intuition. Some of the literature in the field of group development (see, for example, DuFour, DuFour, Eaker, & Many, 2010) makes distinctions between the terms groups and teams and collegial and collaborative. In this book, we use these terms interchangeably to refer to professional communities that share common goals and view each other as resources for exploring practice and improving learning, using data to inform their conversations and decisions.

Although the power of data-driven collaboration is well researched (see for example, Louis & Marks, 1998; McLaughlin & Talbert, 2001) it is often difficult to establish as a norm in schools. As DuFour et al. (2010) remind us, “A collaborative culture does not simply emerge in a school or district: leaders cultivate collaborative cultures when they develop the capacity of their staffs to work as members of high-performing teams” (p. 153).

What You’ll Find in This Book

Got Data? Now What? Creating and Leading Cultures of Inquiry is a practical and accessible resource for confronting these dilemmas. It provides the strategies and tools for deep and deliberate work with data that turn struggling committees into powerful communities of learners. It is intended for group leaders—including instructional coaches, department chairs, team leaders, building and district administrators, and group members—who want meaningful and time-efficient work sessions that produce greater learning for all.

Got Data? Now What? draws from our work with professional learning communities, data teams, and grade-level, department, and administrative meetings. This book shares the lessons we’ve learned and presents practical, time-efficient methods for effectively completing tasks while developing productive collaborative relationships.

This book is based on the following five assumptions about group leadership.

1. Assessment and feedback drive group growth: Group development is an active ongoing process, not a result.

2. Group development and task accomplishment intertwine: Groups need purposeful structures and practical tools to learn with and from their data and one another.

3. When groups change the way they talk, they change the way they work: Thoughtful, systematic data-driven exploration of the results of instructional practice produces learning gains for both students and teachers.

4. Comfort with discomfort is necessary for collaborative learning: Willingness to navigate the emotional challenges of work with data is a key factor for group success.

5. Patterns become habits, habits become norms, and norms shape behavior: The real goal is to positively influence the culture of the organization. High-performing groups are vehicles for producing high-performing cultures, not an end in themselves.

We present a three-phase learning cycle—the collaborative learning cycle—which is a framework for using data to energize collaborative practices that improve student learning. Each chapter offers concepts, tools, tips, exercises, and a data story that illuminates the central focus. Each chapter also offers an Exercise Your Learning section with opportunities for application of the information in the chapter and an Extend Your Learning section with additional resources for further exploration. Visit go.solution-tree.com/teams to download the reproducibles and access the links in this book.

Chapter 1 presents the traits of high-performing data cultures and ways to purposefully develop and sustain learner-centered practices in schools. We offer an inventory for turning these standards of excellence into data for feedback and self-correction to produce ongoing improvements in group performance. The data story illustrates an elementary group applying data about its processes and interactions to refine and improve collaborative skills.

Chapter 2 presents a three-phase, inquiry-driven model for guiding productive group work with data—the collaborative learning cycle. Examples of purpose, process, potential, and pitfalls elaborate each phase of the model. We offer applications and tips for success, and we emphasize the importance of structuring group work and the liabilities that occur when scaffolds and skills are missing. The data story illustrates the collaborative learning cycle in action as a middle school team works with data from a benchmark expository writing assessment.

Chapter 3 presents ways to frame issues for investigation. These fundamental choices direct a group’s attention and data pursuits. We describe how expert groups use structured inquiry to identify gaps and successes and to clarify root causes before generating solutions. The data story illustrates a high school language arts team grappling with student performance gaps in reading comprehension of informational texts.

Chapter 4 presents fundamental definitions and descriptions of data types and uses with tips and cautions for choosing and using effective data displays. We offer approaches for data gathering including data that are presently available or archival and data that might need to be collected via constructed tools such as surveys or interviews. The data story illustrates an elementary math coach helping a vertical team consider possible causes for gaps in student problem-solving skills and identify formative assessment data to explore the issue.

Chapter 5 presents the group-member knowledge, skills, and dispositions that drive high performance. We describe stages of group development including predictable challenges, developmental indicators, and requirements for transitioning from one stage to the next. The data story illustrates a middle school team working with a group-development inventory to assess its growth as a team.

Chapter 6 presents distinctions between three essential modes of discourse in data-based conversations: (1) dialogue, (2) discussion, and (3) decision making. We describe common constraints to productive discourse and identify problematic and productive elements in six decision-making methods. The data story illustrates a middle school team applying effective discourse patterns within the collaborative learning cycle to improve a new behavior management program.

Chapter 7 presents approaches for turning decisions into productive plans for action driven by clear and measurable goals. We offer ten tips for avoiding common planning problems and addressing barriers to effectiveness. The data story illustrates a high school science team moving from making a decision to crafting an action plan for improving student inquiry skills across the science curriculum.

The Road to Learning

School improvement is not easy and quick. Data-driven change requires the commitment and perseverance of individual practitioners sustained by the focused efforts of the whole school community. Collaborative inquiry requires the vulnerability to learn in public, be patient with process, and suspend self-interest to serve a larger purpose. Groups that embrace these challenges, invest energy in their own development, and put data in the center of their conversations produce significant learning gains for themselves and their students.

School improvement is not easy and quick. Data-driven change requires the commitment and perseverance of individual practitioners sustained by the focused efforts of the whole school community.

We invite you to use this book as one vehicle on your road to learning. To accelerate your progress, use the exercises in each chapter individually or as a group study. Exploring the web resources will open further avenues for investigation. While at times the road ahead might be steep or bumpy, we believe the journey will both exhilarate and surprise you.