4-15-13
Name of Teacher: ______________________________________ School: ______________________________________________
HCPSS Student Learning Objective
Secondary Science- Data Collection & Analysis
Component
Student Learning
Objective (SLO)
Population
Learning Content
Instructional Interval
Evidence of Growth
Baseline
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 & Analysis Holistic Rubric.
Eighth grade students in Science
HCPSS Middle School Science Essential Curriculum
School year 2013-14 (one year)
1. First quarter administration of baseline scientific data
collection & analysis assignments
2. Fourth quarter administration of scientific data collection &
analysis assignments.
Baseline scores on the data collection & analysis assignments are on
the attached roster.
Summary of scores:
o (insert number) students earned a score of 1 on the
baseline assignment
o (insert number) students earned a score of 2 on the
baseline assignment
o (insert number) students earned a score of 3 on the
baseline assignment
o (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 cross-cutting
concepts of Science and deepen their understandings of core ideas in
the field of Science.
Rationale for
Students 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 & carrying out investigations
Practice 4- Analyzing & Interpreting Data
Practice 5- Using Mathematics and Computational thinking.
This SLO is a sample. Targets need to be adjusted based on your students’ data. Student growth should be
achieved for all students.
4-15-13
Target
Students who earned 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
Full Attainment
of Target
More than 90% of
students meet
agreed upon
learning targets.


Strategies


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 & 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 & Processes)
This SLO is a sample. Targets need to be adjusted based on your students’ data. Student growth should be
achieved for all students.
HCPSS Secondary Science Data Collection & Analysis Holistic Rubric
Fully Developed
4
Adequately Developed
3
Partially Developed
2
Undeveloped
1
Data Collection
Thorough and independent planning and
designing of an investigation to collect a
data set at an appropriate level of
precision. Data collection reflects
appropriate selection and
strategic/effective use of tools. Quantity
of data elements/trials, and units is
reasonable.
Adequate planning and designing of an
investigation to collect a data set at a
mostly appropriate level of precision.
Data collection reflects appropriate
selection and mostly accurate use of tools.
Quantity of data elements/trials, and units
has some inconsistency.
Limited planning and designing of an
investigation to collect a data set that lacks
precision. Data collection reflects
somewhat appropriate selection and/or
inconsistent use of tools. Quantity of data
elements/trials, and units has many
inconsistencies.
Inadequate planning and designing of an
investigation to collect a data set, or an
ineffective level of precision. Data
collection reflects inappropriate selection
and ineffective use of tools. Quantity of
data elements/trials, and units is limited or
incomplete.
Data
Presentation
Exemplary presentation of data that is
logical and clear and uses spreadsheets,
databases, tables, charts, or computerbased visualization tools effectively.
Graphical displays clearly show
relationships among variables.
Graphical representations are appropriate
for the data, focused & clear for the
intended audience.
Sufficient presentation of data that
adequately uses spreadsheets, databases,
tables, charts, or computer-based
visualization tools . Graphical displays
mostly show relationships among
variables.
Graphical representations are somewhat
appropriate for the data and adequate for
the intended audience.
Inconsistent presentation of data. Limited
use of spreadsheets, databases, tables,
charts, or computer-based visualization
tools. Graphical displays minimally show
relationships among variables.
Graphical representations are not
appropriate for the data, are unclear, or
inconsistent.
Ineffective presentation of data. Very
limited or incomplete use of spreadsheets,
databases, tables, charts, or computerbased visualization tools. Graphical
displays do not show relationships among
variables.
Graphical representations are not
appropriate for the data, or are not
present.
Data
Analysis
Systematically analyzes data to identify
patterns and effectively supporst whether
data are consistent with an initial
hypothesis or accepted theory.
Clearly identifies and recognizes patterns
in data that suggest relationships worth
investigating further.
Appropriate calculation tools such as
spreadsheets are used effectively and
accurately to analyze data using grade
appropriate statistical functions.
Adequately analyzes data to identify
patterns an somewhat supports whether
data are consistent with an initial
hypothesis or accepted theory.
Sufficiently identifies and recognizes
patterns in data that suggest relationships
worth investigating further.
Appropriate calculation tools such as
spreadsheets are mostly used with
accuracy to analyze data using grade
appropriate statistical functions.
Attempts to analyze data to identify
patterns and minimally supports whether
data are consistent with an initial
hypothesis or accepted theory.
Minimally recognizes patterns in data that
suggest relationships worth investigating
further.
Calculation tools such as spreadsheets are
used ineffectively or inconsistently to
analyze data. Statistical analysis is
somewhat grade appropriate.
Inadequately analyzes data to identify
patterns and inadequately supports
whether data are consistent with an initial
hypothesis or accepted theory.
Does not recognize patterns in data that
suggest relationships worth investigating
further.
Calculation tools such as spreadsheets are
used inaccurately to analyze data.
Statistical analysis is not grade
appropriate.
Data
Communication
Supports Practices 3, 4, & 5 from The Framework for K-12 Science Education and HCPSS Essential Curriculum in Science
Skillfully evaluates the strength of
scientific conclusions derived from the data
set(s).
Accurately uses mathematical arguments
to skillfully describe and support
conclusions.
Correlations are identified accurately, and
causation and correlation are clearly
distinguished.
Plausibly evaluates the strength of
scientific conclusions derived from the data
set(s).
Sufficient use of mathematical arguments
to describe and support conclusions.
Correlations are identified appropriately,
and causation and correlation are
adequately distinguished.
Attempts to evaluate the strength of
scientific conclusions derived from the data
set(s).
Minimal use of mathematical arguments to
describe and support conclusions.
Correlations are identified ineffectively,
and causation and correlation are
minimally distinguished.
Little or no attempt to evaluate the
strength of scientific conclusions derived
from the data set(s).
Makes little use of mathematical
arguments to describe and support
conclusions.
Correlations are identified inaccurately,
and causation and correlation are minimal
or missing.
A Score of “0” = indicates a blank or incoherent response
HCPSS- Office of Secondary Science
April 4, 2013