DATA COACHING: ESTABLISHING THE RIGHT CONDITIONS Learning Lab 3: Access and Data Quality
DATA COACHING: ESTABLISHING THE RIGHT CONDITIONS
Learning Lab 3: Access and Data Quality
© 2015, Battelle for Kids. All Rights Reserved
© 2015, Battelle for Kids. All Rights Reserved
© 2015, Battelle for Kids. All Rights Reserved
© 2015, Battelle for Kids. All Rights Reserved
Data Coaching Learning Lab Components
An Overview of
Data Coaching
Establishing the
Right Conditions:
Culture and
Structure
Establishing the
Right Conditions:
Access and
Data Quality
The Data
Coaching
Process
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Data literate educators…
Are comfortable discussing data
in a safe environment…
can freely access multiple sources of data…
have regular opportunities to discuss and
act upon data to make informed decisions…
and use only high-quality data.
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Learning Lab 3Establishing the Right Conditions:
Access and Data Quality
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Understand the importance of access to high-quality data
Understand factors that impact data quality
Reflect on establishing conditions for data coaching teams
Learning Targets for Learning Lab 3: Data Access and Data Quality
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Access refers to the availability of multiple
sources of data applicable to educators.
CONDITIONS THAT PROMOTE ACCESS TO DATA
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Analyze data across schools, grade-levels, and subjects to
identify patterns and opportunities for collaborative inquiry
Examine data from multiple measures as well as longitudinal data to
identify trends and contributing factors as well as to measure impact
Data Access: Allows Educators to Embrace Measurement and Foster a Growth Mindset
Identify strengths and opportunities for improvement
for students as well as for educators’ professional practices
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Data Access and Confidentiality: Questions to Consider
Who currently
has access to
data?
What is the
process to
grant access?
Who needs
access to
data?
Is the data
appropriate to
share?
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Balancing Access and Data Confidentiality
What Level of Data Access is Necessary?
Identifiable DataDe-Identified
DataAggregate Data
Data that contains
information that
can be used to
identify individual
students
Student-level data
that has had
identifying
information
removed
Group-level data
that does not
contain any
identifying
information
Attendance 2nd Quarter
Daniel missed 14
days this quarter.
Erica missed two
days this quarter.
Attendance 2nd Quarter
Student 1-----14
Student 2------2
Student 3------0
Student 4------5
Student 5------0
Attendance 2nd Quarter
Avg. daily attendance
rate: 92%
Students absent >2
days: 12%
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Data Access Scenario
The social studies department has decided that to support
the school-wide goal of incorporating more reading into all
content areas, they will focus on incorporating more
non-fiction reading in their classes.
Since they do not know the reading levels of students, they
ask their colleagues that are English/reading teachers to
share students’ grades with them.
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Data quality references using data generated
from high-quality measures in the inquiry
process.
CONDITIONS THAT PROMOTE DATA QUALITY
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Assessment Literacy and Data Quality
High-Quality
Assessments
High-Quality
Data
Assessment Literacy: Understanding the characteristics
of high-quality assessments and developing the capacity
to design and select assessments that will yield data to
make valid, reliable inferences.
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High-Quality Assessments
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High-Quality Assessments
Validity: Does the assessment
measure what it intends to measure (alignment)?
Reliability: Does the assessment
provide trustworthy results?
Bias: Does the assessment
offend or unfairly penalize students?
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High-Quality Assessments
High-Quality Data
Valid Conclusions
An assessment is not
valid or invalid
High-quality data is the
result of sufficient alignment to
appropriate standards
Conclusions/decisions made
based on the data are valid or
invalid based on the quality of
the data
Validity
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Alignment: The Factor That Impacts Validity
Assessments and data used must be closely aligned to articulated standards taught in the classroom.
What do I want my students to be able to do?
Content
Skills
What do I want my students to know?
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Standards
AssessmentInstruction
Data-
Informed
Decisions
Are the expectations and cognitive
demands consistent for the standards,
the instruction, and the assessment?
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Reliability refers to the extent to which a measure consistently produces data that is trustworthy.
Reliability can be impacted by:
Administration Protocols
Data Collection Protocols
Environmental Factors
Assessment Design Factors
Reliability
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Reliability and Validity
A measure can be reliable, but not have validity
A measure must be reliable to have validity
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Assessment items and assessments that are biased lead to misinterpretations of the data and misinformed instructional decisions.
Bias causes confusion between whatis being tested and who is being tested.
Bias
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High-Quality Data: Integrity and Reliability
Is the data
collected in a
reliable
manner?
Is the data free
from errors and
omissions?
Is the data
securely
maintained?
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Data Quality Scenario
The district has implemented a strict schedule of short
cycle assessments every nine weeks. This quarter, the
students missed 13 days of instruction due to
inclement weather.
The district wants assessments to be given next week
even though much of the content for the quarter has
not been taught.
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Reflection Survey
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© 2015, Battelle for Kids. All Rights Reserved
Next Steps
Complete the activities in the Facilitation Guide. Data Access Inventory
Checklist for Assessment Quality
Access and Data Quality Scenarios
Conditions for Success Scenario
Begin Learning Lab 4: The Data Coaching Process
To learn more about Assessment Literacy and Data Quality, complete the Assessment Literacy Learning Labs available on the Battelle for Kids Student Progress Portal.
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