Creating Data-Driven Reteaching Groups
Data-Driven Instruction
Teaching something does not guarantee that students learn the material. As student-learning data become available, datadriven educators reflect on those results and consider, if, when, and how they should adjust instruction.
Key Method
When considering reteaching, one must determine when, how, and with whom reteaching should occur. A key decision is
whether reteaching should occur as a whole group, in small groups, or individually. This step in data-driven instruction
comes before determining content will actually be retaught. Using analyzed data, the instructor contemplates student
performance, decides whether standards should be retaught, and if so, proposes whether the reteaching should happen with
the whole group, with small-groups of students, or with individuals.
Method Components
There are five primary components to creating data-driven reteaching groups:
1.
Conduct a high-level analysis of the student performance data.
At a minimum, the educator calculates the average student performance on each standard. Additionally, he or she should
consider any limitations to the validity of the inference they make about average student achievement, given the data.
2.
Based on that analysis, identify any standards that should likely be retaught with the whole group.
The educator uses the data to provide rationale for which standards, if any, should be retaught to the whole class.
3.
Based on the data, identify small groups for reteaching.
The educator uses the data to provide rationale for which standards should be retaught to small groups of students and
decide who should be in each group and on which standards they should focus. It is important to describe the process
used to arrive at the conclusions.
4.
Based on the data, identify students who should be retaught individually.
The educator uses the data to provide rationale for selecting particular students for whom individualized reteaching is
likely to be most effective given the time-intensive nature of this intervention.
5.
Identify other data that could improve reteaching decisions.
Data-literate educators see assessment data as only part of a spectrum of data that can be used to make instructional
decisions. Other data could include attendance data, data on student motivation, behavioral data, etc.
Supporting Research

Mason, D., and Good, T. 1993. “Effects of Two-Group and Whole-Class Teaching on Regrouped Elementary
Students’ Mathematical Achievement.” American Educational Research Journal, 30, 328–360.
Resources

Bambrick-Santoyo, Paul. 2010. Driven by Data: A Practical Guide to Improve Instruction. Jossey-Bass, San
Francisco.
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
Bambrick-Santoyo, Paul. 2012. Leverage Leadership: A Practical Guide to Building Exceptional Schools. JosseyBass, San Francisco.
Good, T., and Brophy, J. 2008. Looking in Classrooms, 10th Edition. Pearson, New York.
Submission Guidelines & Evaluation Criteria
To earn the micro-credential for Creating Data-Driven Reteaching Groups, use the dataset at http://bit.ly/1J8JsIX to complete
the steps outlined below. Please assume that each of the items from the associated assessment is similar in structure, content,
and rigor. This assumption allows you to complete the activity without having a test in hand or being a subject-specific expert
in the underlying content. It should be emphasized that before actually reteaching, one would want to question the initial
classroom instruction and think about what could be done differently in the reteaching to better help learners.
Each component of your artifact submission will be assessed based on the rubrics that follow each question. You must earn a
(3) Proficient or (4) Exemplary score on each portion of the submission in order to earn the micro-credential.
Part 1. High-level analysis
1. How did the class as a whole perform on each of the seven math standards?
Standard
Number of Items on Assessment
Average Student Performance
3.OA.A.1
3.OA.A.2
3.OA.A.3
3.OA.A.4
3.OA.B.5
3.OA.B.6
3.OA.C.7
2.
Given the data, on which standards should one be least confident in the inference about average student
performance? Why?
The educator
identifies average
student
performance by
standard and
assesses the
limitations of that
measure.
Attempting
(1)
Foundational
(2)
The educator
incorrectly calculates
average student
performance by
standard or does not
assess limitations of
the measure given
the data.
The educator correctly
calculates average
student performance
by standard but does
not accurately assess
limitations of that
measure given the
data.
Proficient
(3)
The educator
correctly calculates
average student
performance by
standard and
accurately assess
limitations of that
measure given the
data.
Exemplary
(4)
The educator correctly
calculates average student
performance by standard
and assesses limitations of
that measure given the data
in a manner that
demonstrates a deeper
appreciation for the
strengths and limitations of
the measure.
Part 2. Grouping questions
1. Given the data, which standards, if any, should be retaught to the whole group? Why?
Attempting
(1)
The educator
Either the educator fails to
Foundational
(2)
The educator provides
Proficient
(3)
The educator
Exemplary
(4)
The educator provides
2
identifies
which
standards
should be
retaught to the
whole group.
2.
provide rationale for which
standards should be
retaught to the whole group
or the rationale is largely
unsubstantiated by the data.
rationale for which
standards should be
retaught to the whole
group, but there are
questions about some of
the conclusions based on
the data.
provides compelling
rationale based in
the data for which
standards should be
retaught to the
whole group.
compelling rationale
based in the data for
which standards should
be retaught to the whole
group and identifies
nuances in approach
given the data.
Given the data, which standards, if any, should be retaught in small groups? Please describe who would be in each
group and which standards one would prioritize when reteaching.
The educator
identifies
small groups
of students
who should
be retaught
standards
together.
Attempting
(1)
Foundational
(2)
Proficient
(3)
Exemplary
(4)
The educator sections
students into small
groups to reteach
particular standards, but
there are many
questions about the
logic of the grouping
choices based on the
data or there is little or
no explanation of the
process for identifying
the groups.
The educator sections
students into small
groups to reteach
particular standards
and explains the
process for doing so,
but there is some
question about the
logic of the grouping
choices given the data.
The educator
compellingly
leverages data to
section students into
logical small groups
to reteach particular
standards and
explains the process
for doing so.
The educator not only
compellingly leverages data to
section students into logical
small groups to reteach
particular standards and clearly
explains the process for doing
so, but is nuanced in those
decisions, governed by a larger
set of rules for this work, and
operates in a sophisticated way
that will likely optimize time
spent in small groups.
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3.
Given the data, which students should be retaught individually? Why? On which standards should they focus?
Attempting
(1)
The educator
identifies
students in
need of
individual
reteaching.
4.
The educator
identifies students in
need of
individualized
reteaching, but the
choices are largely
unsupported by the
data.
Foundational
(2)
Proficient
(3)
The educator leverages
data to identify students
in need of individualized
reteaching, but there is
some question about the
logic of the choices
based on the data.
The educator
compellingly
leverages data to
identify students in
need of
individualized
reteaching.
Exemplary
(4)
The educator not only
compellingly leverages data to
identify students in need of
individualized reteaching, but
does so in a strategic way that
balances the benefit of
individualized instruction with
the reality of how much time
personalized instruction takes.
What other data, beyond what was provided, would be helpful in making the ultimate decision about how to group
students to reteach these standards?
Attempting
(1)
The educator
identifies other
data sources that
could improve
reteaching
decisions.
The educator
suggests other data,
but it is unclear how
these data would
improve reteaching
decisions.
Foundational
(2)
The educator suggests
other data, but it is less
clear how these data
would improve
reteaching decisions.
Proficient
(3)
The educator
suggests how
other data could
improve
reteaching
decisions.
Exemplary
(4)
The educator not only suggests
other data that could improve
reteaching decisions, but also
makes explicit predictions
about how these additional data
might influence reteaching
decisions.
4