SOLUTION: Georgia Southern University Armstrong Campus The Data Driven Classroom Paper

THE DATADRIVEN
CLASSROOM
How do I use student data tto
improve my
y instruction?
Craig A.
MERTLER
The DaTaDriven
Classroom
How do I use student data to
improve my instruction?
Craig a.
Mertler
Alexandria, VA USA
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PAPERBACK ISBN: 978-1-4166-1975-8 ASCD product #SF114082
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Library of Congress Cataloging-in-Publication Data
Mertler, Craig A.
The data-driven classroom : how do I use student data to improve my
instruction? / Craig A. Mertler.
pages cm. — (ASCD ARIAS)
Includes bibliographical references.
ISBN 978-1-4166-1975-8 (pbk. : alk. paper) 1. Educational tests and
measurements. 2. Effective teaching. I. Title.
LB3051.M4655 2014
371.26—dc23
2014021970
_________________________________________________
21 20 19 18 17 16 15 14
1 2 3 4 5 6 7 8 9 10
The DaTa-Driven
Classroom
How do I use student data to
improve my instruction?
Introduction to Data-Driven Educational
Decision Making……………………………………………………………. 1
Decision Making for Individual Interventions…………….. 8
Decision Making for Group-Level
Instructional Revisions………………………………………………… 14
Action Planning for Future Instructional Cycles……….. 25
Encore………………………………………………………………………….. 33
References……………………………………………………………………. 40
Related Resources………………………………………………………… 41
About the Author………………………………………………………… 42
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The Data-Driven Classroom
1
Introduction to Data-Driven
Educational Decision Making
Teachers have been using data about students to inform their
instructional decision making since the early movement to
formalize education in the United States. Good teachers
tend to use numerous types of data and gather them from
a wide variety of sources. Historically speaking, however,
teachers have typically not incorporated data resulting from
the administration of standardized tests (Mertler & Zachel,
2006).
In recent years—beginning with the adequate yearly
progress requirements of No Child Left Behind (NCLB) and
continuing with Race to the Top (RTTT) and the Common
Core State Standards (CCSS) assessments—using standardized test data has become an accountability requirement.
With each passing year, there seems to be an increasing level
of accountability placed on school districts, administrators,
and teachers. Compliance with the requirements inherent
in NCLB, RTTT, and the CCSS has become a focal point for
schools and districts. For example, most states now annually
rate or “grade” the effectiveness of their respective school
districts on numerous (approximately 25–35) performance
indicators, the vast majority of which are based on student
performance on standardized tests.
2
Craig A. Mer tler
As a result, the notion of data-driven decision making
has steadily gained credence, and it has become crucial for
classroom teachers and building-level administrators to
understand how to make data-driven educational decisions.
Data-driven educational decision making refers to the
process by which educators examine assessment data to
identify student strengths and deficiencies and apply those
findings to their practice. This process of critically examining
curriculum and instructional practices relative to students’
actual performance on standardized tests and other assessments yields data that help teachers make more accurately
informed instructional decisions (Mertler, 2007; Mertler &
Zachel, 2006). Local assessments—including summative
assessments (classroom tests and quizzes, performancebased assessments, portfolios) and formative assessments
(homework, teacher observations, student responses and
reflections)—are also legitimate and viable sources of student data for this process.
The “Old Tools” Versus the “New Tools”
The concept of using assessment information to make
decisions about instructional practices and intervention
strategies is nothing new; educators have been doing it forever. It is an integral part of being an effective educational
professional. In the past, however, the sources of that assessment information were different; instructional decisions
were more often based on what I refer to as the “old tools”
of the professional educator: intuition, teaching philosophy, and personal experience. These are all valid sources of
The Data-Driven Classroom
3
information and, taken together, constitute a sort of holistic
“gut instinct” that has long helped guide educators’ instruction. This gut instinct should not be ignored. However,
it shouldn’t be teachers’ only compass when it comes to
instructional decision making.
The problem with relying solely on the old tools as the
basis for instructional decision making is that they do not
add up to a systematic process (Mertler, 2009). For example,
as educators, we often like to try out different instructional
approaches and see what works. Sounds simple enough, but
the trial-and-error process of choosing a strategy, applying it
in the classroom, and judging how well it worked is different
for every teacher. How do we decide which strategy to try,
and how do we know whether it “worked”? The process is
not very efficient or consistent and can lead to ambiguous
results (and sometimes a good deal of frustration).
Trial and error does have a place in the classroom:
through our various efforts and mistakes, we learn what
not to do, what did not work. Even when our great-looking
ideas fail in practice, we have not failed. In fact, this process
is beneficial to the teaching and learning process. There is
nothing wrong with trying out new ideas in the classroom.
It’s just that this cannot be our only way to develop strong
instructional strategies.
I firmly believe that teaching can be an art form: there
are some skills that just cannot be taught. I am sure that
if you think back to your own education, you can recall a
teacher who just “got” you. When you walked out of that
teacher’s classroom, you felt inspired. Conversely, we’ve all
4
Craig A. Mer tler
had teachers who were on the opposite end of that “effectiveness spectrum”—who just did not get it, who were not
artists in their classrooms. Even young students are able to
sense that.
The concept of teaching as an art form is an important
and integral part of the educational process, and I don’t
intend to diminish it. Rather, what I want to do is expand
on it by integrating some additional ideas and strategies
that build on this notion of good classroom teaching. The
old tools do not seem to be enough anymore (LaFee, 2002);
we must balance them with the “new tools” of the professional educator. These new tools, which consist mainly of
standardized test and other assessment results, provide
an additional source of information upon which teachers
can base curricular and instructional decisions. This datadriven component facilitates a more scientific and systematic
approach to the decision-making process. If we think of the
old tools as the “art” of teaching, then the new tools are the
“science” of teaching.
I do not think that the art of teaching and the science
of teaching are mutually exclusive. Ideally, educators would
practice both. In this publication, however, I focus on the
data-driven science of teaching.
A Systematic Approach
Taking the data-driven approach to instructional decision making requires us to consider alternative instructional and assessment strategies in a systematic way. When
we teach our students the scientific method, they learn to
The Data-Driven Classroom
5
generate ideas, develop hypotheses, design a scientific investigation, collect data, analyze those data, draw conclusions,
and then start the cycle all over again by developing new
hypotheses. Likewise, educational practitioners can use the
scientific method to explore and weigh our own options
related to teaching and learning. This process is still trial and
error, but the “trial” piece becomes a lot more systematic and
incorporates a good deal of professional reflection (Mertler,
2009). And, like the scientific method, the decision-making
process I describe in the following sections is cyclical: the
data teachers gather through the process are continually
used to inform subsequent instruction. The process doesn’t
just end with the teacher either deciding the strategy is a
winner or shrugging and moving on to a new strategy that
he or she hopes will work better.
A major reason teachers don’t rely more on assessment
data to make instructional decisions is the sheer volume of
information provided on standardized test reports. One
teacher comment I often hear is, “There is so much information here that I don’t even know where to start!” One way to
make the process less overwhelming is to focus your attention on a few key pieces of information from test reports and
other assessment results and essentially ignore other data,
which are often duplicative.
Another anecdotal comment I often hear from teachers provides a reason why many educators resist relying on
assessment data—that is, the belief that using test results to
guide classroom decision making reduces the educational
process to a businesslike transaction. It’s true that in business
6
Craig A. Mer tler
settings, data are absolutely essential. Information about
customers, inventory, and sales, for example, are crucial
in determining a business’s success or failure. In contrast,
in education we tend to focus more on the “human” side
of things. Rightfully so, of course: kids are living, breathing
entities, whereas data are abstract. For many educators, this
truly makes data a four-letter word (LaFee, 2002). Yet we
don’t need to view data as antithetical to the educational
process; there’s room for the data side and the human side.
The idea of data-driven decision making is not new, but
incorporating data into instruction does take some practice
on the part of the classroom teacher. The following section
should shed some light on this practice and help make it a
less intimidating process.
Understanding How to Look at the Data:
Advice and Caveats
Educators can effectively use student assessment data to
guide the development of either individualized intervention
strategies or large-group instructional revisions. Regardless
of the purpose and goals of the decision-making process, it is
important to heed some cautionary advice before examining
the results of standardized tests.
Generally speaking, standardized achievement tests are
intended to survey basic knowledge or skills across a broad
domain of content (Chase, 1999). A standardized test may
contain as many as seven or eight subtests in subjects such
as mathematics, reading, science, and social studies. Each
subtest is then further broken down to assess specific skills
The Data-Driven Classroom
7
or knowledge within its content area. For example, the reading subtest may include subsections for vocabulary, reading
comprehension, synonyms, antonyms, word analogies, and
word origins. One of these particular subsections may contain only five or six actual test items. Therefore, it is essential
to interpret student performance on any given subtest or
subsection with a great deal of care.
Specifically, educators must be aware of the potential for
careless errors or lucky guesses to skew a student’s score in
a particular area, especially if the scores are reported as percentile ranks or if the number of items answered correctly is
used to classify student performance according to such labels
as “below average,” “average,” and “above average.” This caveat
also applies to local classroom assessments, including larger
unit tests, final exams, or comprehensive projects. Whatever
the type of assessment, it is important to avoid over-interpretation—that is, making sweeping, important decisions
about students or instruction on the basis of limited sets of
data (Russell & Airasian, 2012). Although over-interpreting
results does not guarantee erroneous decision making, it is
certainly more likely to result in flawed, inaccurate, or lessvalid instructional decisions.
Prior to making any significant instructional or curricular decisions, it is therefore crucial to examine not only
the raw scores, percentile ranks, and the like but also the
total number of items on a given test, subtest, or subsection (Mertler, 2003, 2007). In addition, educators should
consult and factor in multiple sources and types of student
data to get a more complete view of student progress or
8
Craig A. Mer tler
achievement. These additional sources of data may be formal
(e.g., chapter tests, class projects, or performance assessments) or informal (e.g., class discussions, homework assignments, or formative assessments). Looking at a broader array
of data can help teachers avoid putting too much weight on
a single measure of student performance and, therefore,
reduce the risk of making inaccurate and invalid decisions
about student learning and teaching effectiveness.
In the following sections, we will look at the two main
ways classroom teachers can use student assessment results
as part of the data-driven decision-making process: (1)
developing specific intervention strategies for individual
students, and (2) revising instruction for entire classes or
courses (Mertler, 2002, 2003).
Decision Making for Individual
Interventions
Figure 1 depicts the process for examining assessment
results to make instructional decisions and develop intervention strategies for individual students (Mertler, 2007).
The steps shown in Figure 1 constitute a “universal”
process for using standardized test data and other assessment results to guide intervention decisions. The process is
universal in that it can be applied to any situation, regardless of grade level, subject area, type of instruction, or types
of skills being taught. Seen separately, these steps are not
The Data-Driven Classroom
Figure 1: Universal Process for Identifying Areas for
Individual Intervention
Standardized Test Scores/Assessment Results
1. Identify any content, skill, or subtest areas where the
student performed below average.
2. Rank the content or skill areas in order of poorest
performance.
3. From this list, select 1–2 content areas to serve as the
focus of the intervention.
4. Identify new or different methods of instruction,
reinforcement, assessment, and so on to meet the needs
of the individual student.
Development of Intervention
9
10
Craig A. Mer tler
particularly complex, but taken together they represent an
ongoing, systematic process that enables educators to
• Take a large amount of assessment data.
• Narrow the focus for potential interventions on
the subtests, content areas, or skills where student
performance was weakest.
• Further pare down that list by focusing on the one or
two most critical content or skill areas.
• Develop an intervention strategy for addressing the
particular weakness(es) by identifying different modes
of instruction, reinforcement or practice, or methods
of assessing student learning and mastery.
Typically, a teacher might know which students in his
or her class are struggling (by means of assessment performance or simple observations) but likely would not know
the specific areas or skills where interventions should be
targeted. The process begins with the teacher examining test
reports or other obtained assessment results and identifying any content, skill, or subtest areas where a given student
performed poorly or below average. If the teacher identifies
more than one problem area, he or she should rank those
areas in order of perceived severity of deficiency. The one
or two highest-priority areas should then be selected as the
focus of the intervention. Finally, the teacher should identify, develop, and implement new or different methods of
instruction, reinforcement, or assessment to meet the needs
of the individual student (Mertler, 2007).
An example of student-level decision making follows.
The Data-Driven Classroom
11
Example #1: Student-Level Decision
Making
On the whole, Mrs. Garcia’s 1st grade class had been
doing quite well in all tested areas of reading (phonemic
awareness, alphabetic principle, vocabulary, and fluency
and comprehension) on both the benchmark assessments
and the monitoring assessments of the district-adopted
standardized reading test. One student, however, was struggling with certain aspects of the assessments. Jacob seemed
to be strong in phonemic awareness, having surpassed the
target in the October benchmark assessment, and he met the
target score for alphabetic principle in December. However,
he was having trouble in the area of oral reading fluency. He
scored slightly below the target in October and did not show
any improvement in the December assessment, although
the target goal had increased. Essentially, the assessments
indicated that Jacob was regressing in oral reading fluency.
Mrs. Garcia knew that Jacob needed some individualized support to progress toward the grade-appropriate goals.
She decided to implement weekly oral reading fluency interventions. The purpose of these intervention activities was
to provide continual reinforcement of fluent reading skills.
In addition, she decided to check Jacob’s ability to recognize
sight words to see if he might also need remediation in that
area. If this was the case, Mrs. Garcia knew that helping him
improve his sight word recognition would also increase his
oral fluency.
12
Craig A. Mer tler
How to Know Whether Your Interventions
Have Been Effective
As you may have guessed, the key to determining the
effectiveness of your intervention strategies is to collect
more data. In essence, determining intervention effectiveness is a continuation of the process depicted in Figure 1.
This is a cyclical process: teachers collect and analyze data
to inform their initial intervention attempts; collect data
after implementation to determine the effectiveness of the
intervention; and then collect even more data to continually
assess the intervention’s overall effectiveness in terms of
student learning. In between data co …
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