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Name of Teacher:
School:
HCPSS Student Learning Objective
Secondary Science – Middle School Data Collection and Analysis
Component
Student Learning
Objective (SLO)
Description
100% of students will demonstrate growth in the ability to collect and
analyze scientific data as measured by growth on the HCPSS Secondary
Science Data Collection and Analysis Holistic Rubric.
Population
Learning Content
Instructional Interval
Eighth grade science students
HCPSS Middle School Science Essential Curriculum
School year 2014-2015 (One year)
Evidence of Growth
Multiple data points over time, including at least the following:
1. First quarter administration of baseline discipline appropriate
scientific data collection and analysis assignments
2. Fourth quarter administration of end-of-year discipline
appropriate scientific data collection and analysis assignments
Baseline
Baseline scores on the data collection and analysis assignments are on
the attached roster. Summary of scores:
 (Insert number) students earned a score of 1 on the baseline
assignment.
 (Insert number) students earned a score of 2 on the baseline
assignment.
 (Insert number) students earned a score of 3 on the baseline
assignment.
 (Insert number) students earned a score of 4 on the baseline
assignment.
For students to develop science literacy, they must deepen their
understanding and ability to apply the Practices of Science and
Engineering and the Skills and Processes of Science. Actively engaging in
these scientific practices allows students to apply crosscutting concepts
of science and deepen their understandings of core ideas in the field of
science.
Rationale for Student
Learning Objective
This Student Learning Objective focuses specifically on how students
collect and analyze scientific data. This skill is essential for students to
meet the expectations of the HCPSS Essential Curriculum and the
Common Core State Standards.
The Framework for K-12 Science Education:
Practice 3 – Planning and Carrying Out Investigations
Practice 4 – Analyzing and Interpreting Data
Practice 5 – Using Mathematics and Computational Thinking
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
Target

Students who earned a 1 or 2 will earn at least a 3 on a similar
assignment.
Students who earned 3 or 4 will earn a 3 or 4 on a similar
assignment based on more complex performance tasks.
*Please note: Students identified by IEP teams as having significant
cognitive disabilities will have individual targets.
Criteria for
Effectiveness
Strategies
Full Attainment of
Target
More than 90% of
students meet
agreed upon
learning targets.




Partial Attainment of
Target
Between 75% and 90%
of students meet
agreed upon learning
targets.
Insufficient
Attainment of Target
Less than 75% of
students meet agreed
upon learning targets.
Extensive opportunities for students to engage in data collection
and analysis in science through student performance tasks,
student labs, and analysis of data collected from reputable
sources.
Provide opportunities for formative checks on student learning
through performance tasks, student labs, or analysis of data
collected from reputable sources.
Students will review their own performance data and develop
individual targets for improvement in identified areas.
Students collect work samples throughout the school year for a
portfolio that will demonstrate their mastery of Scientific
Practices (Skills and Processes).
This SLO is a sample. Targets need to be adjusted based on your students’ data. Student growth
should be achieved for all students
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Middle School - HCPSS Secondary Science Data Collection & Analysis Holistic Rubric
Supports Practices 3, 4, & 5 from The Framework for K-12 Science Education and HCPSS Essential Curriculum in Science
Data Planning
Data Collection
Data
Presentation
Data Collection
Fully Developed - 4
Adequately Developed – 3
Partially Developed - 2
Undeveloped - 1
Thorough planning and designing of
an investigation to collect a data set at
an appropriate level of precision.
Adequate planning and designing of
an investigation to collect a data set
at a mostly appropriate level of
precision.
Limited planning and designing of
an investigation to collect a data set
that lacks precision.
Inadequate planning and designing
of an investigation to collect a data
set, or an ineffective level of
precision.
Data collection reflects appropriate
selection and strategic/effective use
of tools.
Data collection reflects appropriate
selection and mostly accurate use of
tools.
Data collection reflects somewhat
appropriate selection and/or
inconsistent use of tools.
Data collection reflects
inappropriate selection and
ineffective use of tools.
Quantity of data elements/trials and
units is reasonable.
Quantity of data elements/trials, and
units has some inconsistency.
Quantity of data elements/trials,
and units has many
inconsistencies.
Quantity of data elements/trials,
and units is limited or incomplete.
Exemplary presentation of data that
effectively uses spreadsheets,
databases, tables, charts, or computerbased visualization tools.
Sufficient presentation of data that
adequately uses spreadsheets,
databases, tables, charts, or
computer-based visualization tools.
Inconsistent presentation of data.
Limited use of spreadsheets,
databases, tables, charts, or
computer-based visualization tools.
Ineffective presentation of data.
Very limited or incomplete use of
spreadsheets, databases, tables,
charts, or computer-based
visualization tools.
Graphical displays clearly show
relationships among variables,
reflecting appropriate selection and
strategic/effective use of tools with
grade-appropriate statistical functions.
Graphical displays mostly show
relationships among variables,
reflecting appropriate selection
and mostly accurate use of tools
with grade-appropriate statistical
functions.
Graphical displays minimally show
relationships among variables,
reflecting somewhat appropriate
selection and/or inconsistent use
of tools. Inconsistently uses gradeappropriate statistical functions.
Graphical representations are
mostly appropriate for the data and
adequate for the intended audience.
Graphical representations are
somewhat appropriate for the
data
Graphical representations are
appropriate for the data, focused and
clear for the intended audience.
Graphical displays do not show
relationships among variables,
reflecting inappropriate selection
and ineffective use of tools. Does
not use grade-appropriate
statistical functions.
Graphical representations are not
appropriate for the data, or are not
present
A Score of “0” indicates a blank or incoherent response
7-16-14
Middle School - HCPSS Secondary Science Data Collection & Analysis Holistic Rubric
Supports Practices 3, 4, & 5 from The Framework for K-12 Science Education and HCPSS Essential Curriculum in Science
Data
Communication
Data
Analysis
Fully Developed - 4
Adequately Developed – 3
Partially Developed - 2
Undeveloped – 1
Thoroughly analyzes data to identify
patterns and effectively support
whether data are consistent with an
initial prediction or scientific
phenomena.
Adequately analyzes data to identify
patterns and somewhat supports
whether data are consistent with an
initial prediction or scientific
phenomena.
Partially analyzes data to identify
patterns and minimally supports
whether data are consistent with an
initial prediction or scientific
phenomena.
Inadequately analyzes data to
identify patterns and inadequately
supports whether data are
consistent with initial prediction or
scientific phenomena.
Clearly identifies and recognizes
patterns in data that suggest
relationships worth investigating
further.
Sufficiently identifies and
recognizes patterns in data that
suggest relationships worth
investigating further.
Minimally identifies and
recognizes patterns in data that
suggest relationships worth
investigating further.
Does not identifies and
recognizes patterns in data that
suggest relationships worth
investigating further.
Appropriate calculation/digital tools
such as graphical displays (maps,
charts, graphs, models and /or tables)
are used effectively and accurately to
analyze data using grade appropriate
statistics (mean, median, mode and
variablilty).
Appropriate calculation/digital tools
such as graphical displays (maps,
charts, graphs, models, and/or
tables) are mostly used with
accuracy to analyze data using
grade appropriate statistics (mean,
median, mode and variability).
Calculation tools such as graphical
displays (maps, charts, graphs,
models, and/or tables) are used
ineffectively or inconsistently to
analyze data. Statistical analysis is
somewhat grade appropriate.
Calculation tools such as graphical
displays (maps, charts, graphs,
models, and/or tables) are used
inaccurately to analyze data.
Statistical analysis is not grade
appropriate.
Skillfully evaluates the strength of
scientific conclusions derived from the
data set(s).
Plausibly evaluates the strength of
scientific conclusions derived from
the data set(s).
Attempts to evaluate the strength
of scientific conclusions derived
from the data set(s).
Little or no attempt to evaluate
the strength of scientific conclusions
derived from the data set(s).
Accurately uses mathematical
arguments to describe and support
conclusions.
Sufficient use of mathematical
arguments to describe and support
conclusions.
Minimal use of mathematical
arguments to describe and support
conclusions.
Makes little use of mathematical
arguments to describe and support
conclusions.
Correlations are identified accurately,
and causation and correlation are
clearly distinguished.
Correlations are identified
appropriately, and causation and
correlation are adequately
distinguished.
Correlations are identified
ineffectively, and causation and
correlation are minimally
distinguished.
Correlations are identified
inaccurately, and causation and
correlation are minimal or
missing.
A Score of “0” indicates a blank or incoherent response