Hypothesis 3

 

Identify counties in West Virginia in 2025 that exhibit pronounced local variations in this Math/ELA discrepancy, such as McDowell and Logan, where the math challenge is severe, and others like Barbour, where math performance is relatively stronger.

 Here's my plan to tackle that topic. Let me know if you need to make changes.

West Virginia Math/ELA Discrepancy Analysis

Research Websites

(1) Search for the most recent student assessment reports (2024-2025 or the latest available) from the West Virginia Department of Education. (2) From these reports, extract the county-by-county proficiency rates for both Mathematics and English Language Arts (ELA). (3) For each county, calculate the statistical discrepancy between their Math and ELA proficiency rates. (4) Analyze the specific Math/ELA discrepancy for McDowell, Logan, and Barbour counties to establish a quantitative baseline for what constitutes a 'severe math challenge' versus 'relatively stronger math performance'. (5) Identify other West Virginia counties where the math proficiency rate is significantly lower than the ELA rate, comparable to the baseline set by McDowell and Logan. (6) Conversely, identify counties where the gap between math and ELA performance is minimal or where math scores are comparatively high, similar to the profile of Barbour County. (7) For the counties identified with the most pronounced discrepancies, investigate potential contributing socio-economic or educational factors by searching for relevant local reports or news articles. (8) Synthesize the findings to present a categorized list of counties based on the nature and magnitude of their Math/ELA performance gap.

 

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Understanding the Research Goal

I am beginning an investigation into West Virginia counties to identify those exhibiting notable local variations in the discrepancy between Math and English Language Arts (ELA) proficiency rates. The core interest is finding counties where the challenge in Math is particularly severe compared to ELA, using counties like McDowell and Logan as examples of high discrepancy and Barbour as an example of lower discrepancy. Although the target year is 2025, I will prioritize finding the most recent available assessment data, likely from the 2023-2024 or 2022-2023 school years, as 2025 data may not yet be public.

Initial Data Strategy

My initial strategy involves finding the latest student assessment data published by the West Virginia Department of Education. Once located, I will extract county-level proficiency rates for both Math and ELA. Calculating the difference between these two rates for every county will allow me to quantify the Math/ELA discrepancy. I will use the specific examples provided (McDowell, Logan, and Barbour) to establish a clear baseline for what constitutes a 'severe math challenge' versus 'relatively stronger math performance' among the counties.

Next Steps

My immediate next action is to search for the official West Virginia Department of Education assessment results to gather the necessary county-level proficiency data.

 

Finding 2024-2025 Assessment Data

I successfully searched for and found multiple news releases and official documents detailing the West Virginia student assessment results for the 2024-2025 school year, which is the precise year requested. Statewide, I observed that ELA proficiency reached 48% and Mathematics proficiency reached 38%. The math proficiency rate continues to lag behind ELA by 10 percentage points statewide, confirming the overall trend of a Math/ELA discrepancy.

Identifying Data Sources for County Comparison

A crucial finding is the mention of the 'ZoomWV dashboard' and the 'Balanced Scorecard' as official sources for viewing the detailed 2025 assessment results, including information at the county and school levels. These sources are the most promising avenues for obtaining the specific county-by-county data needed to analyze the pronounced local variations.

Strategy to Obtain Detailed County Data

My immediate plan is to access the ZoomWV dashboard, using the URL provided in the search results, to find and extract the county-level Math and ELA proficiency rates for the 2024-2025 school year. If the dashboard is not easily parsable for aggregate data, I will then focus on searching for direct data reports or files related to the Balanced Scorecard to identify counties like McDowell, Logan, and Barbour.

 

Statewide Improvement and Data Access

From my initial search on the West Virginia Department of Education's 2024-2025 assessment results, I confirmed that 48 out of 55 counties improved in Math and 52 counties improved in ELA. The overall statewide proficiency stands at 48% for ELA and 38% for Math, highlighting a continued 10-point gap in proficiency. I've pinpointed the 'ZoomWV dashboard' and the 'Balanced Scorecard' as the primary official mechanisms for viewing this data at the county level.

Strategy Refinement for County Data

My next step is to obtain the granular, county-level Math and ELA proficiency rates for the 2024-2025 school year. Since direct browsing of the official data dashboard didn't yield the aggregate data I need, I am shifting my focus to finding the comprehensive "Balanced Scorecard" data. This scorecard is a key part of the state's accountability system and is very likely to contain the required county-by-county breakdown.

Next Research Action

I will perform a targeted search for the West Virginia Balanced Scorecard data, specifically looking for reports or files that present the county-level assessment results. I will also look for any downloadable PDF reports of the 2024-2025 assessment data that would bypass the interactive dashboard to secure the proficiency rates for all 55 counties.

 

Preliminary Discrepancy Analysis

I've made a significant breakthrough by accessing and analyzing preliminary data for the key counties of interest—McDowell, Logan, and Barbour—using the Education Recovery Scorecard. This data, which measures student performance in 'grade level equivalents' relative to the 2019 national average, confirms the premise of the investigation.

Confirming Local Variations

My analysis of the grade level data confirms the desired local variations. For McDowell and Logan counties, Math performance lags behind Reading performance by approximately 0.3 grade levels, establishing them as examples of a severe math challenge. Crucially, I found that Barbour County exhibits the opposite trend: Math performance is nearly 0.7 grade levels *higher* than Reading performance, confirming its role as an example of relative math strength. This preliminary analysis is a strong foundational insight, even though I still need the official proficiency rates.

Strategy for Statewide Data Acquisition

Despite identifying the official sources like the ZoomWV dashboard and the Balanced Scorecard, I still lack the county-by-county proficiency *percentages* for all 55 counties for the 2024-2025 school year. My next steps are highly focused on obtaining a complete, downloadable data file from the state Department of Education. I will be using specialized searches to locate a data download or spreadsheet that bypasses the need to interact with online dashboards, allowing me to complete the comprehensive analysis.

 

Finding a Comprehensive County Status Report

Although directly obtaining the county-by-county proficiency percentages for the 2024-2025 school year remains challenging, I have identified a critical official document: the '2025 County Approval Status and Accreditation Report.' This report provides a powerful qualitative assessment by classifying counties with official accountability ratings such as 'Meet requirements,' 'On Watch,' 'Support,' or 'Intensive Support' for key indicators like 'Math Achievement' and 'ELA Progress.' This offers a new path to categorize and identify the required discrepancies.

Strategy Shift to Qualitative Discrepancy

Since the detailed numerical proficiency data for all 55 counties is aggregated behind a dashboard, I am adapting my approach to leverage this qualitative status report. I plan to analyze the difference between a county's status in ELA (where performance is generally stronger) and its status in Math (where the challenge is generally greater). For instance, a county that 'Meets requirements' in ELA but is in 'Support' for Math Achievement is a strong indicator of the pronounced local variation I am looking for.

Next Steps: Deep Dive into County Status and Context

My immediate plan is to browse the official report to extract the specific accountability status for all counties in Math and ELA. This will allow me to categorize the counties into groups like 'Severe Math Challenge' (e.g., McDowell and Logan analogs) and 'Relatively Stronger Math Performance' (e.g., Barbour analogs). Following this, I will conduct targeted searches for socio-economic and educational context for the counties showing the most extreme discrepancies to enrich the final analysis.

 

Categorizing Counties by Math Challenge

I successfully used the official '2025 County Approval Status and Accreditation Report' to categorize West Virginia's counties based on their math and ELA accountability statuses. This process allowed me to generate two key groups for focused analysis: counties with a 'Severe Math Challenge' and those with 'Relatively Stronger Math Performance.' This classification directly addresses the core research requirement to identify counties with pronounced local variations in the Math/ELA discrepancy, beyond just the statewide average.

Focus on Socio-Economic Context

To understand the root causes of these variations, I am now pivoting the research toward a comparative socio-economic analysis. My plan includes a deeper investigation into the socio-economic challenges present in counties with a 'Severe Math Challenge,' such as McDowell and Logan. This will involve analyzing factors like poverty, infrastructure, and other local educational barriers to build a compelling contextual narrative.

Gathering Comparative Data

To provide a complete picture, I will also conduct targeted searches for socio-economic factors in the 'Relatively Stronger Math Performance' counties, such as Barbour. By comparing the challenging conditions in the low-performing counties against the mitigating factors in the high-performing counties, I anticipate synthesizing a robust explanation for the observed differences in Math/ELA proficiency.

 

Creating the initial report outline...

The initial information sweep has returned a significant volume of data. I am now synthesizing these preliminary findings to construct a logical report outline. This structured framework will organize the key arguments, establish a clear narrative flow, and map the primary data points to their respective sections.

Comprehensive Data Synthesis Achieved

Through multiple phases of research, I've gathered and synthesized all the necessary components to address the core request. Although I could not extract a single spreadsheet with 2025 county-by-county numerical proficiency rates, I have successfully utilized the '2025 County Approval Status and Accreditation Report' for robust categorization and the 'Education Recovery Scorecard' for quantitative benchmarks on the challenge counties (McDowell and Logan) and the stronger-performing county (Barbour). This combination of qualitative accountability status and quantitative grade-level deficit data is sufficient for a comprehensive and insightful analysis.

Identifying Root Causes of Discrepancy

My targeted searches on socio-economic conditions have yielded rich contextual information. I now have strong evidence to link the pronounced Math/ELA discrepancies in the most challenged counties to a complex interplay of factors, including high rates of poverty, teacher shortages, and funding issues often tied to declining enrollment and the legacy of industrial decline. This socio-economic context will form the basis of the explanatory part of my final report.

Final Report Preparation

I am confident that I have reached a point of saturation, meaning further searches are unlikely to yield the specific, numerical county-level data. The assembled information—including the categorization of all 55 counties and the detailed benchmark analysis—provides a complete answer. My next and final action is to structure and generate a comprehensive research report that clearly outlines the local variations in Math/ELA discrepancy, supported by both the assessment status data and the socio-economic factors I have uncovered.

 

Expanding the report outline...

With the high-level outline complete, I am now populating each section with specific supporting evidence, statistics, and qualitative insights from the research. This phase involves a deeper synthesis of the data and helps to identify any critical information gaps that may require a second, more targeted search.

 

 

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