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Beyond the Numbers

Dissecting Educational Disparities in 8th Grade Standardized Testing Amidst COVID-19

DATA 73000 Final Project

Nicole Baz, May 2024


The Research Question

How have 8th-grade standardized testing scores evolved across diverse student groups pre- and post-COVID-19, considering various intersectional identifiers, and what factors may explain any observed disparities?

The Audience

This project contributes to a better understanding of the impact of the COVID-19 pandemic on educational outcomes, and can help to address disparities in standardized testing performance. These findings could inform decision-making regarding resource allocation and policy interventions aimed at promoting equity in education. Key audiences include policy makers, educators and parents and students who are feeling the impact on learning.

The Project:

The Data:

The dataset for this analysis was sourced from the Nation’s Report Card, specifically the National Assessment of Educational Progress (NAEP), administered by the National Center for Education Statistics (NCES) and the Institute of Education Sciences (IES). The dataset includes standardized testing scores for 8th-grade mathematics and reading in 2015, 2017, 2019 and 2022, split by the following demographic variables:

  • State
  • Race
  • Gender
  • Lunch program eligibility (proxy for family income)
  • Disability status
  • English language learning (ELL) status.

The data will be extracted in csv format from the NAEP Data Explorer. Data cleaning will involve standardizing variable names, handling missing data, and aggregating scores at the state and national levels. Additionally, a “learning loss” metric will be calculated by subtracting an average of 2015, 207 and 2019 scores from 2022 scores for each demographic group and state to assess the impact of the pandemic on testing outcomes.

The Visualizations:

This extensive visual report includes the following visualizations:

  1. An interactive map that shows student scores from 2015 to 2022.
  2. A bar chart that shows state-level scores compared to the national average across both math and reading and all included test years.
  3. Line charts to show trends in scores a different way, also with the option to compare a state’s average trend to trends by demographic group.
  4. Two bubble charts, one for reading and one for math, to see all “learning loss” data simultaneously. These charts allow you to see which subgroups were most impacted. 

The visualizations can be found here or embedded below:

Design Rationale

My designs aimed to create a clear narrative view of this issue by starting “big picture” and drilling down into jurisdictions, demographics and variance from typical testing.

COVER PAGE:

As this data set is quite specific, I felt it best to begin with some context. The cover page introduces the rationale behind the project to prepare viewers for the subsequent dashboards.

MAP:

As these scores were pulled from “The Nation’s Report Card” (which uses an internal scoring system), I included information about that scale on the dashboard to make it clear what was being shown.

The decision to view scores on a map seemed like an intuitive starting place. I used the average scale score of all students in a given state as a metric to determine the fill of the states. The goal of this was to show where scores were highest and lowest across 2015 to 2022, and interestingly, revealed the major drop off in scores in 2022. This set the stage for future visualizations by priming viewers for this drop-off.

Please note: I excluded Puerto Rico from the Map visualization because it was an outlier in the data and detracted from the chloropleth effect by creating a biased color scheme.

BAR CHART:

Next, I wanted to present the scores in a ranked format so it was easier to see who was highest and lowest in any given year and subject. I colored the bars in the chart by percentile bands of 25%, and added in the “average” reference line to see how far above/below average a state was. This “average” line represented the national average scale score for all students tested.

LINE GRAPHS:

The line graphs were designed to highlight trends and also gives viewers their first opportunity to explore demographic group differences. While still focused on average scale score, by viewing these data in a linear format, viewers get the benefit of trendlines to begin to parse the impact of pandemic learning conditions on performance.

These charts also highlight score and trend differences within groups, and also highlight missing data. While NAEP randomly selects schools to participate in these assessments, there are a handful demographic groups are unrepresented in the data.

BUBBLE CHARTS:

Finally, I wanted to provide viewers with the most detailed view possible – everything at once. To do this, I created a metrics I called “learning loss” which took the average of the 3 pre-COVID test events (2015, 2017 and 2019) and subtracted that from the 2022 score for that group/subgroup. If the result was negative, it meant that that group scored lower in 2022 than “typical.” If it was positive, it meant they grew from their “typical” performance. These learning loss points were plotted on a bubble chart, with a reference line of 0.

This allows viewers to see:

  • The spread of scores across demographics (i.e. Were all demographics groups in a state testing at about the same score range, or is there a wide spread of scores?)
  • The groups who significantly grew in average scale score in 2022.
  • The groups who significantly dropped in average scale score in 2022.

This was done for both Reading and Math, but as there was a lot of data included, I chose to annotate the most extreme plot points to point viewers to them.

Next Steps

Moving forward, I would like to include more subjects and grades. This project focused on one grade level and two subjects, which is a limited scope given the breadth of available data from NAEP. Further, incorporating other national assessments to attempt to corroborate these findings would be a meaningful step. This could also address the currently limited “post-COVID” testing data available through NAEP.

I also think it would be interesting to track a cohort over time. For example, in 4 years, how does this cohort of 8th graders do on their 12th grade tests compared to previous years? Are they still feeling the effects of pandemic learning, or have they gained back momentum? While “learning loss” is a hot topic now, interest in it is already lessening as we all move on from the unprecedented challenges of COVID-19. However, I believe that the generation of students who lost 1-2 years of in-school instruction will face new hurdles as we move forward.

In closing, this project was a good step in exploring these relationships, but is far from comprehensive. Further research must be done to continue digging into the intersectional effects of the pandemic on learning, and policies focused on creating and sustaining an equitable learning environment for those most impacted is essential.

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NYC Noise Complaints & Punitive Action in 2023

DATA 73000 Project #1: 311 Data

Nicole Baz, Spring 2024

Guiding Research Questions:
  • How many loud music/party noise complaints resulted in punitive action from NYPD in 2023?
  • Are there any relationships between location and time that result in more arrests?
Key Audience:

NYC Residents and community members can benefit from this project as it reveals arrest patterns by the NYPD in response to loud music/party complaints made by other New Yorkers.

Complaints Summary

These visualizations show “Loud Music/Party” noise complaints by month and by borough.

With these visualizations you can:

  • See a zoomed out look at all NYC party noise complaints by month
  • Use the pie chart to look at the borough breakdown by month to see where the complaints are being made from.

WHERE are arrests happening?

While borough is a useful way to group complaints, this visualization offers exact complaint locations. There is a helper table to view percentage breakdowns by borough and police response type.

With these visualizations you can:

  • Use the Police Response filter to toggle between the different response options (No Action Taken, Action Taken, or Arrest/Summons).
  • See complaints grouped by location type to highlight any patterns.
  • Filter between NYPD response type to see any patterns in location that arise.
  • Drill down into a specific borough using the Borough filter

WHEN are arrests happening

This line chart shows time of complaint (by hour and by borough), and accounts for all complaints in 2023.

With this visualization you can:

  • See patterns in time of day for party complaints
  • Drill down into NYPD response to see how patterns change between hour and borough.

Data & Design Rationale

Data

The data for this project was sourced from the NYC 311 Open Data platform, specifically the 311 Service Requests from 2010 to Present data explorer. Using the Query tool, I exported service requests from 12am on 1/1/23 to 12am on 1/1/24 that used the descriptor “Loud Music/Party.” This amounted to 353,547 cases.

To support the included visualizations, I had to create the following calculated fields:

  • Functional Time – This string variable allowed me to get the time of the complaint grouped into hours (i.e. 1pm) rather than down to the second.
  • Police Response – I coded the following “Resolution Descriptions” from the 311 Data to streamline the visualizations.

Design Rationale

I chose to start the journey of exploration of this data zoomed out – with basic summative charts. The bar chart serves as a quick way to viewers to glean total noise complaints throughout the year by month. It seemed a natural next step to see this information by borough as well. Viewers have the option to view the whole year by borough or drill down into a specific month of interest.

Next, I wanted to explore specific locations of noise complaints via a map visualization. Given that the data provided a Location Type variable, I chose to split out the complaints by location type to both see patterns in the data but to also make the map more readable (as mentioned, there were > 300,000 data points). This chart is most useful when viewers are able to view just the Arrest/Summons cases. In support of this, I included a summary table to help viewers get a sense of percentage of Arrest cases vs typical cases (no action taken or action taken).

Finally, I chose to drill down into the Arrest/Summons cases by time of day and borough. The line chart compares borough to borough over a 24 hour period, and aggregates all complaints made throughout 2023. This final visualization fills in the missing piece of the puzzle, the “when.”

You can view all vizzes together here.

Next Steps

Moving forward, I would continue to use visualizations to help identify patterns in arrest/summons cases. Further exploration could be done by using cross streets, police precincts and other variables to identify patterns of arrests in even more specific areas of NYC. It would also be interesting to fold in external socio-economic data to see if arrest trends correlate with other variables such as median income.

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