Leveraging Assessment Derived Data to
Inform COVID-19 School Support Strategies
Nathan Dadey & Damian Betebenner
The National Center for the Improvement of Educational Assessment
February 25, 2021
ASR SCASS
Draft Paper Available
This work is licensed under a
Creative Commons Attribution 4.0
International License.
Background: Providing a Shared Understanding
• The pandemic has complicated state assessment in unforeseen
ways.
• As states move forward with testing, being clear about the
purposes of analyses will be more important than ever
▪ Time is short and the context is fraught
▪ Assessment is often confounded with accountability
▪ Planning of school supports should go hand in hand with plans for
analysis
Planning should start now, if it hasn’t already!
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Background, Continued
We can’t wait until the assessment data comes in to plan.
• Plans for school support should be ongoing
• Analyses of assessment should be targeted to provide needed
information for those plans, while working within a number
of constraints, including:
▪ Organizing and providing school support Timeline for suppor
Is it Fall 2021?
▪ Turn around time for score reporting and analyses
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Strands of Analysis
Strand Description Focus Responsible Party
Operational Typical yearly procedures and analyses Validity of test scores Assessment vendor(s)
meant to support the technical quality • Comparability We hope this
of test scores and intended • Interpretation doesn’t blow up
interpretation(s) and uses of the
assessment results.
Investigative Prioritized analysis aimed at Effect on learning and Assessment vendor(s) and
understanding the effects of the performance SEAs (with negotiated, clear
pandemic on learning and • Overall effects lines of responsibility)
performance. • Differential effects
Note the interactive nature between the strands of analysis:
• Findings from preliminary discovery analyses can inform operational analyses.
• Valid test scores from operational analyses can support the findings of in-depth discovery analyses.
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Key Questions of Interest
• Who participated in the test in 2021?
• What do the test scores mean in 2021?
• Are the individual and aggregate scores in 2021 comparable to those in
previous years?
• What are appropriate (or inappropriate) uses of test scores in 2021?
• Which schools and districts have the largest decreases in performance?
▪ What characteristics explain, or at least associate with, these decreases?
• What subgroups display the largest decreases?
• Do students in remote learning conditions perform differently than those in
in-person learning conditions?
• Etc.
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Operational Analyses
• Sound investigative analyses are predicated upon assessment data
of high technical quality.
▪ Operational analyses tend to proceed investigative analyses
▪ Sound investigative analyses can occur with compromised assessment data,
but such cases are rare (e.g., getting to the right conclusion with the wrong
data)
• Concerns about validity of the data derived from assessments
abound, e.g.,
▪ Comparability of remote versus in-person testing
▪ Impact of low participation rates (e.g., potential impacts on item
parameters under post equating, possible impacts on normative statistics)
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Investigative analyses
• Assuming assessment data of
good technical quality, what
investigative analyses should be
pursued?
• USED’s recent relaxation of
accountability provisions makes
investigative analyses even more
critical.
• Typical analyses that support
accountability are not the only, or
even the major, stories we want
to investigate this year.
www.nciea.org
Investigative analyses
• Support planning/intervention is rooted in our ideas, or
hypothesis, about what we think is happening to students
▪ Based on our current, incomplete picture of student experiences
• These hypotheses should be tested and the results used to
inform and revise our support plans
▪ Support plans should be ready to run with the results of planned
analyses (and in worse case scenarios, run without the data
anyway)
▪ Support plans should be amenable to change as new data/
evidence becomes available.
www.nciea.org
Support
Planning
Planning & Developing:
Operational • Key questions
Implement-
Analyses ation
• Benchmarks based
on Simulations using
Historical Data
Investigative
• Code based on
Analyses simulated data
Feb March April May June July August Sep
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Support
Implement-
Planning ation
Planning & Developing:
Operational • Key questions
Implement-
Analyses ation
• Benchmarks based
on Simulations using
Historical Data Implement-
Investigative
• Code based on ation
Analyses simulated data
Feb March April May June July August Sep
www.nciea.org
Some Overarching Questions
• How did overall performance change relative to prior years?
• Who has the lowest performance?
• How does performance compare across key groups?
www.nciea.org
Using Investigative Analyses to Support Planning
• Hypothesis: The academic impact (sometimes referred to as
learning loss) of the pandemic is being unevenly felt by
students.
• If true, targeted intervention is more prudent than blanket
intervention.
• For states considering targeted assistance:
▪ Who needs help? Grades, schools, districts, demographic subgroups,
COVID related subgroups, English Language Learners.
▪ What do they need help in? Mathematics, ELA, Science, non-tested
subjects?
▪ How much help do they need? Is recovery needed large or small?
www.nciea.org
Many Factors to Consider
• Unit. At what level will the analyses be focused?
▪ Individual students
▪ Classrooms, schools, districts, or the state overall
▪ Specific subgroups (e.g., race/ethnicity, economically disadvantaged,
students with disabilities, English language learners)
▪ Specific grades (and grade-groupings, like early elementary) or content areas
▪ Learning conditions (e.g. remote, in-person, hybrid)
• Measure. What outcome measure will be examined?
▪ Achievement (e.g., level scale scores or proficiency levels), often in terms of
cross-sectional comparisons
▪ Growth (e.g., score differences on a vertical scale, student growth
percentiles), often in terms of longitudinal comparisons
▪ Gaps between groups defined in terms of achievement or growth
www.nciea.org
Prioritizing Analyses
• Internal vs. External
▪ Are the results are intended for use outside of department, and if
so, what specific stakeholders are meant to be addressed?
• Availability
▪ How easily accessible will the data be?
▪ Like the distinction of internal vs. external, level of access involves
stakeholders and refers to the specific ways in which the results will
be provided and communicated (e.g., posted on the department
website, communicated through a press release, as part of a
webinar).
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Prioritizing Analyses
• Reporting Format
▪ How will the results be provided (e.g., a simple spreadsheet, posted as part
of the state’s report card, as an interactive web-based visualization)?
• Timing
▪ Is there a specific window of time by which the results are needed? For
example, is there a window of time an analysis needs to be completed by
to support an intervention?
▪ Results will likely come in waves from different data sources, how will this
impact timing.
• Responsible Party
▪ Who will be conducting the analyses and what is their capacity to
implement the analyses? Will the analysis be seen as credible to key
stakeholders when coming from that responsible party?
www.nciea.org
Additional Considerations
Contextual data. How can contextual data, such OTL and
learning and testing environment data, be collected in 2021 to
help contextualize findings?
▪ See, for example, Marion (2020)
▪ Consider asking experts, such as the TAC to review your survey or
data collection instruments.
Interaction effects. How can the various factors influencing
2021 test score be disentangled?
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Example Investigative analyses for 2021
Participation
• Participation rates on assessments are likely to be
substantially less than in normal years.
• Understanding who and who did not participate in state
assessments will be critical in interpreting overall results.
▪ By demographic subgroups (e.g., ses, ethnicity)
▪ By mode of instruction (remote/hybrid/in person learner)
▪ By grade, school, district
www.nciea.org
Example Investigative analyses for 2021:
Using Growth to Assesses Impact
• Student academic growth analyses can be used to assess the
academic impact of the pandemic.
• Baseline skip-year growth norms (2017 to 2019) can be used
with 2019 to 2021 data to assess how much “slower”
students learned during the pandemic relative to pre-
pandemic times.
• Differential impact on groups can be investigated by
aggregating up SGPs at the group level.
• With accurate mode of instruction data (remote/hybrid/in
person), impact of mode of instruction can be assessed.
www.nciea.org
About this copy
Leveraging Assessment Derived Data to Inform COVID-19 School Support Strategies — CCSSO SCASS, February 25, 2021.
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