Assessment #5: Impact
Data Analysis
and Visual
Representation
of Data
UCA-CF 4
INTASC 6, 9
TESS 1f, 3d, 4a, 4b, 4e, 4f
CAEP 1.2, 1.5, 4.1
Data Trends and
Analysis of
Student Learning



UCA-CF 4
INTASC 6, 9
TESS 1f, 3d, 4a, 4b, 4e, 4f
CAEP 1.2, 1.4, 1.5, 2.3, 4.1

Analysis of
Student Learning
Among
Subpopulations
UCA-CF 4
INTASC 6, 9

Data spreadsheet and report
narrative include inaccurate
descriptive identifiers and
statistics representing student
performance including overall
sample size, sample sizes of
important subgroups, and
group means.
Charts/tables were presented
for 1 or fewer of the 3 required
areas: (1) comparing pre/post
data, (2) representing whole
class, and (3) comparing
student subpopulations

Candidate provides narrative
analysis of student learning but
does not include evidence from
data collected (e.g. sample
sizes, means, etc.) and instead
relies on generalizations, bias,
or stereotypical thinking in
drawing conclusions.
Candidate does not align data
findings to national or state
standards, learning objectives,
teaching strategies, or
assessments (e.g., how were
they designed and why were
they designed that way) or
alignment is incomplete (less
than 75% alignment).
Candidate provides narrative
analysis of student learning for
only 1 identified subpopulation
or does not include evidence
from data collected (e.g.
sample sizes, means, etc.) and
instead relies on




Data spreadsheet and report
narrative include accurate
descriptive identifiers and
statistics representing student
performance including overall
sample size, sample sizes of
important subgroups, and
group means.
Charts/tables were presented
for at least 2 of the 3 required
areas: (1) comparing pre/post
data, (2) representing whole
class, and (3) comparing
student subpopulations

Candidate provides narrative
analysis of student learning
that includes 3 data points (e.g.
sample sizes, means, etc.) and
but relies on generalizations,
bias, or stereotypical thinking
in drawing conclusions.
Candidate aligns data findings
to national or state standards,
learning objectives, teaching
strategies, and assessments
(e.g., how were they designed
and why were they designed
that way) (75% alignment).

Candidate provides narrative
analysis of student learning for
2 identified subpopulations
that includes 3 data points (e.g.
sample sizes, means, etc.) and
but relies on generalizations,
bias, or stereotypical thinking



Data spreadsheet and report
narrative include accurate
descriptive identifiers and
statistics representing student
performance including overall
sample size, sample sizes of
important subgroups, and
group means as well as
additional relevant numbers
(i.e., median, mode, or range).
Charts/tables were presented
for all 3 of required areas: (1)
comparing pre/post data, (2)
representing whole class, and
(3) comparing student
subpopulations

Candidate provides narrative
analysis of student learning
that includes 4-5 data points
(e.g. sample sizes, means, etc.)
and provides two studentspecific examples from the
data to support conclusions
Candidate aligns data findings
to national or state standards,
learning objectives, teaching
strategies, and assessments
(e.g., how were they designed
and why were they designed
that way) (90% alignment).

Candidate provides narrative
analysis of student learning for
2 identified subpopulations
that includes 4-5 data points
(e.g. sample sizes, means, etc.)
and provides two studentspecific examples from the



Data spreadsheet and report
narrative include accurate
descriptive identifiers and
statistics representing student
performance including overall
sample size, sample sizes of
important subgroups, and
group means as well as
additional relevant numbers
(i.e., median, mode, or range).
Provided data also includes (1)
standard deviation or variance
or (2) kurtosis or skewness.
Charts/tables were presented
for all 3 of the required areas:
(1) comparing pre/post data,
(2) representing whole class,
and (3) comparing student
subpopulations
Candidate provides narrative
analysis of student learning
that includes more than 5 data
points (e.g. sample sizes,
means, etc.) and provides
more than two student-specific
examples from the data to
support conclusions
Candidate fully aligns data
findings to national or state
standards, learning objectives,
teaching strategies, and
assessments (e.g., how were
they designed and why were
they designed that way).
Candidate provides narrative
analysis of student learning for
2 identified subpopulations
that includes more than 5 data
points (e.g. sample sizes,
means, etc.) and provides
more than two student-specific
1
TESS 1f, 3d, 4a, 4b, 4e, 4f
CAEP 1.1a, 1.2, 1.5, 4.1

Interpretation
and Conclusions
UCA-CF 4
INTASC 6, 9
TESS 1f, 3d, 4a, 4b, 4e, 4f
CAEP 1.2, 1.5, 4.1



Samples of
Student Work
and Feedback
UCA-CF 4
INTASC 6, 9
TESS 1f, 3d, 4a, 4b, 4e, 4f
CAEP 1.2, 4.1

generalizations, bias, or
stereotypical thinking in
drawing conclusions.
Candidate does not align data
findings for subpopulation(s) to
overall data trends or to
learning objectives, teaching
strategies, or assessments
(e.g., how were they designed
and why were they designed
that way) or alignment is
incomplete (less than 75%
alignment).

Candidate does not correctly
identify trends in data or
simply reiterates data findings
(e.g., comprehension level
rephrasing of numerical data).
Candidate does not draw
conclusions to make case that
that data findings are
meaningful representation of
student learning or conclusion
is not supported by evidence.
Candidate does not identify
factors (environmental and
pedagogical) that may have
contributed to the data
findings and does not provide
specific strategies for
improving results

Candidate provides 0-2
graded/scored samples of
student work or narrative does
not describe provided student
work samples in relation to
overall data set (e.g., does not
explain why a work sample is
“low”, “mid”, or “high”)



in drawing conclusions.
Candidate aligns data findings
for subpopulations to overall
data trends and to learning
objectives, teaching strategies,
and assessments (e.g., how
were they designed and why
were they designed that way)
(75% alignment).

Candidate correctly identifies
trends in data
Candidate draws conclusions
to make case that data findings
are meaningful representation
of student learning and
supports conclusions with
evidence but relies on
generalizations, bias, or
stereotypical thinking or
includes fewer than 2
examples including reference
to data points and student
work samples).
Candidate identifies one factor
(environmental and
pedagogical) that may have
contributed to the data
findings or does not provide
specific strategies for guiding
instruction to meet learner
needs

Candidate provides
graded/scored samples of
student work at all 3
performance levels but
narrative does not describes
student work samples in
relation to overall data set
(e.g., explains why a work



data to support conclusions
Candidate aligns data findings
for subpopulations to overall
data trends and to learning
objectives, teaching strategies,
and assessments (e.g., how
were they designed and why
were they designed that way)
(90% alignment).

Candidate correctly identifies
trends in data
Candidate draws conclusions
to make case that data findings
are meaningful representation
of student learning and
supports conclusions with
specific evidence (e.g., 2-3
examples including reference
to data points and student
work samples).
Candidate identifies 2-3 factors
(environmental and
pedagogical) that may have
contributed to the data
findings and provides 2-3
specific strategies for guiding
instruction to meet learner
needs (e.g., new methods or
strategies, lesson
development, re-teaching)

Candidate provides
graded/scored samples of
student work at all 3
performance levels and
narrative describes student
work samples in relation to
overall data set (e.g.., explains
why a work sample is “low”,



examples from the data to
support conclusions
Candidate fully aligns data
findings for subpopulations to
overall data trends and to
learning objectives, teaching
strategies, and assessments
(e.g., how were they designed
and why were they designed
that way) and includes
discussion of how lessons were
differentiated for
subpopulations based on
formative assessments.
Candidate correctly identifies
trends in data
Candidate draws conclusions
to make case that data findings
are meaningful representation
of student learning and
supports conclusions with
specific evidence (e.g., 4-5
examples including reference
to data points and student
work samples).
Candidate articulates 2-3
factors (environmental and
pedagogical) that may have
contributed to the data
findings and provides 2-3
specific strategies for guiding
instruction to meet learner
needs (e.g., new methods or
strategies, lesson
development, re-teaching) to
include differentiation for
specific students
Candidate provides
graded/scored samples of
student work at all 3
performance levels and
narrative describes student
work samples in relation to
overall data set (e.g., explains
why a work sample is “low”,
2

Teacher Efficacy
UCA-CF 4
INTASC 6, 9
TESS 1f, 3d, 4a, 4b, 4e, 4f
CAEP 1.2, 4.1

Student work samples do not
include feedback given to
students or feedback is does
not reinforce learner’s
strengths (e.g., may be rote
responses like “good job”).
Candidate does not provide
meaningful discussion of
instructional strengths or areas
for instructional improvement
based on data findings and
student samples


sample is “low”, “mid”, or
“high”)
Student work samples include
visible feedback that reinforces
the learner’s strengths but
without substantial detail (e.g.,
may be stated as “I like how
you answered this question”)
or only 1-2 work samples
include detailed feedback.
Candidate provides discussion
of goals for instructional
improvement including 1
strength and 1 area for
improvement based on data
findings and students samples


“mid”, or “high”)
All student work samples
include visible feedback that
gives 1-2 details to reinforce
the learner’s strengths and 1-2
details identifying next steps
for growth.
Candidate provides discussion
of goals for instructional
improvement including 2-3
strengths and 2-3 areas for
improvement based on specific
data findings and student
samples


“mid”, or “high”)
All student work samples
include visible feedback that
gives more than 2 details to
reinforce the learner’s
strengths and more than 2
details identifying next steps
for growth.
Candidate provides discussion
of goals for instructional
improvement including 4-5
strengths and 4-5 areas for
improvement based on specific
data findings and student
samples
3