A SUMMARY OF:
THREE DATA-DRIVEN INSTRUCTION BOOKS
SUBMITTED BY – PHILIP CICERO, ED.D
ADJUNCT PROFESSOR AND NYC/2 LONG ISLAND REGION FACILITATOR
1. Using Data to Improve Learning for All – A Collaborative Inquiry Approach. Edited by
Nancy Love (2009). Thousand Oaks, California: Corwin Press.
The book – an outgrowth of The Data Coach’s Guide to Improving Learning for all
Students: Unleashing the Power of Collaborative Inquiry (book #3 summarized and reviewed
at the end and also edited by Nancy Love) - offers a comprehensive and detailed approach on
how to use school and student data to enhance instruction and improve student achievement.
Forming data teams, preferably at the building levels, team members – administrators and
teachers from various grade levels and/or content areas - are guided in their work by data
coaches. The strategy here is collaborative inquiry where team members share their expertise
with each other in addressing and solving previously agreed to academic concerns. These
concerns can include, but not limited to, reading achievement levels, math achievement levels
and improving graduation rates. Multiple sources of data are used to generate these concerns, and
to further identify the problems and their causes. Solutions are then driven not only the by the
appropriate data, but also by what the discovered research suggests with regard to evidence
based practices and interventions. Solutions are then tried and monitored for their efficacy. This
systematic approach – explained in great detail - is defined as the Using Data Process of
Collaborative Inquiry.
While all of the chapters present a step by step approach to using data as a means to improving
student achievement, the chapter on cultural proficiency seems to be the engine that drives the
train to improvement. Here, data team members must be in touch with their own values and
beliefs and avoid concluding that students from certain races and socioeconomic backgrounds
are incapable of doing better. “Blaming the student” syndrome must be avoided and redirected to
investigating changes in curriculum and instruction as ways to help all students learn and
improve. Erroneous assumptions about students or groups of students will result in little action
about using the data to reach higher goals.
For most school districts and/or school buildings, the challenge of Using the Data Process will be
in it practical implementation. In other words how will school officials find the time for data
teams to meet. Some suggestions are noted throughout the book including pulling teachers out of
their classrooms to meet during the school day, meeting after school, holding meetings during
the summer months, using faculty meetings for these meetings or modifying daily or yearly
schedules and calendars respectively to accommodate data team meetings. Clearly, districts and
schools will have to find the one that best works for them. An additional concern is introducing
to staff yet another educational initiative. Today, building administrators and teachers are already
challenged by Common Core Standards, new assessments and teacher evaluations. Linking data
teams to other improvement committees already in place should be considered.
Integral to the success of data teams – as with other initiatives - is professional development.
Data teams and their coaches must be sufficiently trained in this approach before embarking on
the journey. And the professional development must not be a one-time strategy. Effective
professional development must be ongoing and continuously address the concerns of the data
team members. Buildings and districts interested in establishing data teams must be ready to
make a long term commitment to it.
The final two chapters present the practical and real life experiences of two districts employing
the Using the Data Process. The first district located in Tennessee made exceptional
advancements in a number of academic areas Using the Data Process. Gains were measured by
both absolute improvement (NCE gains) and by improvement determined by using the
Tennessee’s Value Added Assessment System (TVAAS). Attention was also specifically given
to improving the achievement levels of students with disabilities.
The second example included an elementary school located in Nevada. Here, the data team was
more focused with improving math achievement as its goal. The uniqueness of the chapter deals
with how the data team originally misdiagnosed the cause of the problem, but then used the Data
Process to correct itself and propose the alternative and ultimately correct solution.
Finally, the information described in the book tends to focus on bringing and teaching the Data
Process to administrators and faculty in K-12 school districts. But the responsibility of teaching
and learning how to use data goes beyond those school officials. School districts and teacher
education programs from colleges and universities now have the unique opportunity to forge
important partnerships in bringing this instructional approach to their respective institutions.
2. Data Wise. A Step by Step Guide to Using Assessment Results to Improve Teaching and
Learning. Edited by Katherine Parker Boudett, Elizabeth A. City & Richard J. Murnane (2006).
Cambridge, Massachusetts: Harvard Education Press
The book presents eight and distinct steps for using student assessment data to improve
instruction. The organization of the book includes a chapter for each of the eight steps described
in the data process. Data teams, comprised of school building staff members, are generally
responsible for collecting, organizing and presenting data to the faculty. Acting as data
facilitators, data teams seek input from the faculty - by grade level or content area – in
determining where instructional improvement will be needed. The emphasis on collaboration and
inquiry guides the data process approach.
To bring the chapters to life and make each step of the data process relevant, two schools are
used as examples throughout the book. One school is a K-8 building looking to improve a
specific reading skill; the second school is a 9-12 high school using the data process to improve
the problem solving skills of its math students. The individual chapters then present how each
school, and more specifically the faculty and staff from those schools, works its way through the
eight step process in identifying the instructional problem and ultimately finding a solution for it.
Each chapter is written in great detail. While at times this may serve as a distraction to the
reader, you come to understand the need for such specificity. With an abundance of test and
assessment data available to educators today and the general hesitation of school officials to
embrace it and apply it to improving instruction, the authors make it relatively easy – and leave
no stone unturned - for the reader to make sense of the numerous statistical records readily
available to them.
For example, one basic but important chapter – Building Assessment Literacy – reviews (or
maybe introduces) the common constructs of tests, kinds of tests, and the typical ways scores are
reported. Terms such as sampling principles, measurement error, and reliability are all explained
with easy to understand examples. The reader is also shown the difference and limitations among
norm-referenced, criterion-referenced and standards-referenced tests. Lastly, raw scores, cut
scores, scaled scores, percentile ranks and grade equivalent scores are all explained and made
easy for the reader to understand. The lessons of this chapter become instrumental, if not a
prerequisite, in effectively applying data to the instructional improvement process.
In yet another attempt to present the basics, a chapter on Creating a Data Overview describes the
best, or most efficient, way for visually presenting data to a targeted audience. While it seems at
times to be a lesson in how to use Excel and its accompanying graphics, it nevertheless makes
the point of presenting data in a friendly and concise format. Allowing the audience to become
focused and engaged with the graphically presented data makes it more likely for meaningful
discussions to follow.
While the book addresses many of the peripherals associated with understanding and using data,
its most important chapter gets to the heart of the matter which is instructional improvement.
Chapter 5, or step 5 of the data process – Examining Instruction – explicitly links teaching and
learning together. If learning is going to improve there must be an understanding that teaching
must also improve or at least change in order to accommodate the needs of the various learners.
It is not a matter of blaming the teacher – or the student - but rather this approach emphasizes
that the teacher has an important role to play in the teacher-student partnership. The authors of
this chapter use the phrase “problem of practice.” Here, the teacher analyzes assessment and
instructional data to solve the particular learning problem. The solution may result in how or
what the teacher teaches the student. Once again, teachers are encouraged to collaboratively
share their instructional concerns with each other, identify what the research says about effective
instruction, and observe those effective practices in classrooms and/or on packaged videos.
The remaining steps of the eight step process - Developing a Plan, Planning to Assess Progress
and Acting and Assessing - are considered fairly routine steps to most plans where data driven
solutions are being encouraged. However, the hallmark of this book is that these steps are offered
in great detail with various examples given from the two schools engaged in the process.
A final chapter – Roles of the District Central Office – once again appears to be beyond the
intent of the book. But the chapter makes a clear and obvious argument that if reform initiatives
are to be successful, central office must make the commitment, as well as the resources, for that
to occur.
Lastly, and never to be overlooked, are the practical and logistical concerns associated with
many of these educational initiatives. From deciding how prospective team members will be
selected or chosen to serve on these teams to finding the time for these teams to receive the
required training and the time needed in carrying out their many responsibilities are all real
challenges for the administration. Because much of what is determined will carry with it a
significant financial obligation, the involvement of all stakeholders must be a priority.
3. The Data Coach’s Guide to Improving Learning for all Students: Unleashing the Power
of Collaborative Inquiry. Edited by Nancy Love, Katherine E. Stiles, Susan Mundry & Kathryn
DiRianna (2008). Thousand Oaks, California: Corwin Press.
This book serves as a logical complement to book #1 (Using Data to Improve Instruction) first
presented here and summarized earlier. Nancy Love, the editor of that book, is also one of four
editors for this book - The Data Coach’s Guide to Improving Learning for All Students.
Many of the themes, content and data processes involved in using data to improve instruction in
this book can also be found in the aforementioned book. For example, the familiar principles
found in both books include: Collaborative Inquiry, Getting Organized for Collaborative Inquiry,
Building a Foundation, and Building Data Literacy just to mention a few. However, a significant
difference between the two books is primarily how the eight chapters of this book are organized
around the standing principles. While the content and intent of this book is essentially the same
as in the one reported earlier, this book contains additional logistical information and guidance
for implementation and consideration. The first two chapters present a general overview of the
Using Data Process, and how to prepare data coaches for developing and implementing
collaborative inquiry. However, the differences in organization and depth of detail between the
two books begin to surface in chapters 3-7. Each chapter of those chapters is dedicated to one of
the five components of collaborative inquiry. Unlike Love’s previously noted book, the role of
the data coach is now carefully explained in each chapter. This includes what the data coach
needs to know or to do next in order to facilitate a data driven dialogue within that component. In
addition to the separate attention given to the data coaches and their roles, chapters 3-7 also
include specific steps – or tasks – to follow relative to the component. For example, in chapter
three, there are four sequential tasks to follow and complete in developing the Building the
Foundation component. There are also commensurate activities listed that the data coaches can
utilize as they pursue the definition and understanding of specific component with their data
teams.. Finally, there are other resources and materials which data coaches can use as they
prepare for the various tasks. The availability of these materials can be found in an
accompanying CD-Rom which provides the user with protocols, templates, PowerPoint slides
and related handouts and articles.
Lastly, chapter 8 - Clark County, Nevada: Collaborative Inquiry in Action – gives readers a
practical application of how collaborative inquiry was used to build the bridge from data to
results.