Abstract

DRAFT REPORT – DO NOT CITE OR CIRCULATE

The COVID-19 pandemic is having far-reaching effects on all facets of our lives. The impact on education included school closures in Spring 2020 and a 2020-2021 academic school year with mixed remote and in-person instruction that looked and felt quite different from pre-pandemic times. State A, like all states, is currently grappling with evaluating the academic impact the pandemic has had, as well as how to make efficient, targeted use of resources to resources to ameliorate learning loss.

This report investigates the pandemics impact on student achievement (status) and growth. To pursue this investigation, historical A+ State Assessment Program (ASAP) data was used to establish “fair” achievement comparisons and to derive baseline student growth percentiles (i.e., describing 2021 growth relative to 2019 norms). Data in from ASAP was used in these investigations.

1 Introduction

At the end of January 2020, the World Health Organization declared the novel coronavirus (COVID-19) a public health emergency of international concern and the United States Secretary of Health and Human Services declared a public health emergency for the United States. Although many continued to carry out their daily lives without interruption, drastic changes began to occur in State A and around the country.

The COVID-19 pandemic continues to have far-reaching effects on all facets of our lives. The impact on education included school closures in Spring 2020 and a 2020-2021 academic school year with mixed remote and in-person instruction that looked and felt quite different from pre-pandemic times. State A, like all states, is currently grappling with evaluating the academic impact the pandemic has had, as well as how to make efficient, targeted use of resources to resources to ameliorate learning loss.

1.1 Background

Beginning in spring 2020, the COVID-19 pandemic has interrupted normal school activities and has led to unprecedented changes in the ways in which we educate our students. Even now in spring 2021 the majority of students in the United States continue to experience substantial impacts to their education.

This report is part of the due diligence being conducted by the State A Department of Education (SADoE) to quantify the academic impact students in State A have experienced over the year-long course of the pandemic.

Figure 1 illustrates how learning loss might be measured by interim assessments taken at multiple points in an academic year. In this sentence we use the figNum function to get the figure number of the next figure. Since it has not been placed in the report yet, we need to advance the counter by one - figNum(1).

Figure 1.1: Example image placement: learning loss interim assessment illustration

Example image placement: learning loss interim assessment illustration

As Figure 1 shows, interim assessments occur at multiple time points during the year. This sentence shows how the figNum function works in more detail (the figure increment is 0 to get the last figure number.)

Figure 2 illustrates how learning loss might be measured by annual state summative assessments taken at the end of an academic year.

Figure 1.2: Example learning loss illustration with a summative state assessment

Example learning loss illustration with a summative state assessment

The figure counter is now at 2: should be 2.

1.2 Methods

All analyses were conducted using the R Software Environment (R Core Team 2021) in conjunction with the SGP package (Betebenner et al. 2020). Source code associated with the learning loss analyses is available at this link.

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1.2.1 Status and Achievement Comparisons

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1.2.2 Student Growth Percentiles

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1.3 Data

State A has extensive historical state summative assessment data from the A+ State Assessment Program (ASAP) for grades 3 to 8 to evaluate student achievement and growth over the two-year gap in assessment data.

We calculated baseline growth for 2021 (using 2019 as the baseline year with 2016 and 2017

as priors).

2 Student Participation Data

We need to know what the state looked like before and during the pandemic. Here is some helpful data :)

Table 1 provides frequencies and proportions associated with student counts by year, content area, and grade.

Table 2 provides frequencies and proportions student counts by year, content area, and an important demographic variable.

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2.1 Assessment Participation

This section gets at missing data through participation rates. Some data may need to be provided by the state. Another way to get at it would be to check SGP calculation rates. This assumes that students we had in the 2019 data should be present in the 2021 data. It may be the best we can do…

Table 1 provides frequencies and proportions associated with student SGP counts by year, content area, and grade. An important aspect of Table 1 is that there are no 4th grade ELA and mathematics SGPs because no prior score exists with which to calculate growth since there is no exam administered two grades prior. The absence of SGPs for an entire grade level has a significant impact on comparisons done at the elementary school level.

Table 1: Baseline SGP counts and percentages for 2019 and 2021 by content area and grade
  Baseline
Content Area Grade Year Total Students   Count Percent
ELA 5 2019 7,979   6,962 87.3
5 2021 7,894   6,732 85.3
6 2019 7,663   6,724 87.7
6 2021 8,197   7,153 87.3
7 2019 6,900   6,014 87.2
7 2021 7,744   6,774 87.5
8 2019 6,723   5,810 86.4
8 2021 7,195   6,502 90.4
Mathematics 5 2019 7,985   6,962 87.2
5 2021 7,880   6,725 85.3
6 2019 7,663   6,727 87.8
6 2021 8,201   7,149 87.2
7 2019 6,894   6,010 87.2
7 2021 7,738   6,779 87.6
8 2019 6,715   5,803 86.4
8 2021 7,192   6,499 90.4

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3 Academic Impact of the Pandemic

Figure 3 shows how to place multiple images or plots in a single figure frame. Note that you should use fig.show = 'hold' and NOT fig.align='center' in the knitr chunk. It also illustrates different learning loss trajectories and recovery patterns that the state might encounter in student growth over an extended timeline. This emphasizes the importance of being able to measure and monitor student growth this year and going forward in 2022.

Figure 3.1: Example learning loss recovery trajectories.

Example learning loss recovery trajectories.Example learning loss recovery trajectories.

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3.1 Impact on achievement

Here is an example of how to include tables for counter/caption similar to figures in pagedown template. Adam has been unable to put this into a function because the include_graphics function does not play well with environments other than the top level. It also can NOT be done with knitr::knit_child. However, this solution seems pretty clean and lets us render the table the same way in bookdown and pagedown with a single process. Its a little convoluted with the requirement of the invisible gif and the “figtab” class requirement, but I’m happy with where it is at.

Figure 3.2: Historical percent proficient trends by content area and grade

Historical percent proficient trends by content area and grade
Table 2: Historical percent proficient trends by content area and grade
Content Area Grade 2018 2019 2021
ELA 3 67.0 66.6 59.6
4 66.6 66.5 60.7
5 66.7 66.2 64.7
6 68.1 66.2 62.7
7 65.6 65.2 62.2
8 67.1 67.2 64.0
Mathematics 3 65.8 64.5 51.1
4 62.1 62.6 49.6
5 59.3 57.3 46.3
6 51.3 53.4 42.8
7 51.6 52.9 40.3
8 55.5 55.3 47.6

3.2 Impact on academic growth

4 Summary

The analyses conducted in this report investigate the academic impact (learning loss) students in State A experienced from 2019 to 2021. The report attempts to help answer these questions:

These findings will allow the State A Department of Education to allocate relief and remediation resources in a more targeted way, as well as inform stakeholders about the impacts of the COVID-19 pandemic on student educational outcomes.

References

Betebenner, Damian W., Adam VanIwaarden, Ben Domingue, and Yi Shang. 2020. SGP: Student Growth Percentiles & Percentile Growth Trajectories. sgp.io.
R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.