data coaching: establishing the right...

DATA COACHING: ESTABLISHING THE RIGHT CONDITIONS

Learning Lab 3: Access and Data Quality


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


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.


Learning Lab 3Establishing the Right Conditions:

Access and Data Quality


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


Access refers to the availability of multiple

sources of data applicable to educators.

CONDITIONS THAT PROMOTE ACCESS TO DATA


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


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?


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.


Student 1-----14

Student 2------2

Student 3------0

Student 4------5

Student 5------0


Avg. daily attendance

rate: 92%

Students absent >2

days: 12%


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.


Data quality references using data generated

from high-quality measures in the inquiry

process.

CONDITIONS THAT PROMOTE DATA QUALITY


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.


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?



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


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?


Standards

AssessmentInstruction

Data-

Informed

Decisions

Are the expectations and cognitive

demands consistent for the standards,

the instruction, and the assessment?


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


Reliability and Validity

A measure can be reliable, but not have validity

A measure must be reliable to have validity


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


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?


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.


Reflection Survey


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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