Command prompts to text variance in student test scores.

 Create 50 command prompts to test variance in student test scores. Include subject variance, grade level variance , and test item variance. Include variance between districts and individual schools.

 

50 Command Prompts for Analyzing Student Test Score Variance

Subject Variance

1. "Calculate the variance in student test scores for Mathematics compared to Language Arts for the entire 8th-grade cohort."

2. "Generate a report showing the standard deviation of scores in Science versus Social Studies across all high school grades."

3. "Identify which subject area shows the highest coefficient of variation in test scores for the fall semester."

4. "Compare the variance in test scores between STEM subjects and Humanities subjects for the graduating class."

5. "Analyze if the variance in Reading scores correlates with the variance in Writing scores for individual students."

6. "Determine if the spread of scores is wider in AP Calculus compared to General Mathematics."

7. "Plot the distribution of scores for Physics and Chemistry to visually compare their variance."

8. "Calculate the variance in year-over-year growth scores for History compared to Geography."

9. "Assess whether subject-specific variance remains consistent across different demographic groups."

10. "Compare the variance of final exam scores in Foreign Language classes against the variance in English Literature."

Grade Level Variance

11. "Compare the variance in standardized test scores between 3rd grade and 5th grade students."

12. "Analyze the trend of score variance from Kindergarten through 12th grade to see if the gap widens over time."

13. "Calculate the standard deviation of test scores for middle school grades (6-8) versus high school grades (9-12)."

14. "Determine if there is a significant difference in score variance between transition years (e.g., 6th grade, 9th grade) and non-transition years."

15. "Visualize the variance in reading proficiency scores across all elementary grade levels."

16. "Identify which grade level exhibits the lowest variance in math performance."

17. "Compare the variance in test scores for students repeating a grade versus those promoted on time."

18. "Analyze if grade-level variance differs significantly between the Fall and Spring assessment periods."

19. "Calculate the interquartile range (IQR) for test scores in Grade 4 and compare it to Grade 10."

20. "Assess if the variance in scores for early literacy tests in 1st grade predicts variance in 3rd-grade standardized tests."

Test Item & Assessment Variance

21. "Calculate the variance in student performance on multiple-choice items versus open-ended essay questions."

22. "Identify which specific test questions have the highest variance in student responses."

23. "Analyze the item difficulty variance for the end-of-year mathematics assessment."

24. "Compare the variance in scores for questions tagged as 'Critical Thinking' versus those tagged as 'Rote Memorization'."

25. "Determine if computer-adaptive testing results in lower variance per item compared to fixed-form testing."

26. "Calculate the variance in time spent per test item and its correlation with the correctness of the answer."

27. "Analyze the internal consistency and variance of sub-scales within the Science assessment."

28. "Identify items with zero variance (where all students answered correctly or incorrectly) and flag them for review."

29. "Compare the variance in scores for items requiring visual analysis (graphs/charts) versus text-only items."

30. "Evaluate the variance in distracter selection for multiple-choice items to identify confusing questions."

District and Geographic Variance

31. "Compare the variance in aggregate test scores between District A and District B."

32. "Analyze the variance in Math scores across all districts in the state to identify outliers."

33. "Calculate the between-district variance versus the within-district variance for reading comprehension scores."

34. "Map the variance of standardized test scores across rural, suburban, and urban school districts."

35. "Determine if funding per student correlates with the variance in test scores between districts."

36. "Rank all districts by the standard deviation of their graduating class's SAT scores."

37. "Analyze the variance in test score improvement rates between neighboring districts."

38. "Compare the variance in special education student scores across different school districts."

39. "Identify districts where the variance in scores has decreased significantly over the last five years."

40. "Contrast the variance in English Language Learner (ELL) test scores between northern and southern districts."

School Variance & Individual School Analysis

41. "Compare the variance in test scores between Charter Schools and Public Schools within the same district."

42. "Calculate the variance in student performance for School X compared to the district average."

43. "Identify schools with the highest internal variance in student scores (widest achievement gap)."

44. "Analyze the variance in test scores for Title I schools versus non-Title I schools."

45. "Compare the variance in Science scores between single-gender schools and co-ed schools."

46. "Determine if smaller class sizes at specific schools correlate with lower variance in test scores."

47. "Rank schools based on the stability (low variance) of their test scores over a 3-year period."

48. "Analyze the variance in scores between different 'houses' or 'teams' within the same large high school."

49. "Compare the variance in test scores for students attending magnet schools versus neighborhood schools."

50. "Calculate the variance in teacher value-added ratings based on student test scores within a single school."

    --------------------------------------------------

     

    I have analyzed the structure of your CSV file and the 50 variance command prompts. Since the CSV contains aggregated scorecard data (School/District level) rather than individual student or test-item data, not all 50 prompts can be directly calculated (specifically the "Test Item" and "Student-Level" prompts).

    However, I have generated a Python script that executes the Subject, Grade Level (via School Type), District, and School variance analyses that are possible with this dataset. I also included a methodology document explaining how the prompts map to your specific file columns.

    Here are the files to run this analysis:

    Scorecard Variance Analysis

    Nov 18, 8:04 AM

    Analysis Methodology

    Nov 18, 8:04 AM

    Summary of Actions

    1. Created scorecard_variance_analysis.py: A Python script tailored to your specific CSV columns. It cleans the data (removing State-level aggregates) and calculates variance for Subjects, School Types, Districts, and Title I status.

    2. Created analysis_methodology.md: A guide explaining which of the 50 prompts were used, which were skipped (due to data limitations), and how to interpret the results.

    You can run the script in any Python environment with Pandas installed to get the specific variance numbers for your state's data.

     

    ------------------------------------------------------------------------

     https://gemini.google.com/share/3e876c072f75

     ---------------------------------------------------------------------------------------------

     import pandas as pd
    import numpy as np
    
    def analyze_scorecard_variance(file_path):
        # Load the dataset
        try:
            df = pd.read_csv(file_path)
            print(f"Successfully loaded data from {file_path}")
        except Exception as e:
            print(f"Error loading file: {e}")
            return
    
        # --- DATA CLEANING ---
        # Filter out State Level records (District Code 999) to analyze actual Districts/Schools
        # Assuming '999' or 'State Record' indicates state aggregates
        df_clean = df[df['District Code'] != 999].copy()
        
        # Convert 'Not Reportable' or non-numeric strings to NaN
        performance_cols = [
            'Assessment ELA Performance Value', 
            'Assessment Math Performance Value',
            'Assessment ELA Progress Value',
            'Assessment Math Progress Value'
        ]
        
        for col in performance_cols:
            if col in df_clean.columns:
                df_clean[col] = pd.to_numeric(df_clean[col], errors='coerce')
    
        # Filter for a main "All Students" group if possible to avoid double-counting subgroups
        # Adjust the filter value 'Total' or 'All Students' based on your actual CSV content for "Subgroup"
        # If 'Total' doesn't exist, we perform analysis on the full dataset but note that it includes subgroups
        if 'Total' in df_clean['Subgroup'].values:
            df_main = df_clean[df_clean['Subgroup'] == 'Total']
            print("Filtering for 'Total' subgroup for aggregate analysis.")
        else:
            df_main = df_clean
            print("Note: 'Total' subgroup not found. Analysis includes all subgroups (Race, SES, etc.).")
    
        print("\n" + "="*50)
        print("VARIANCE ANALYSIS REPORT")
        print("="*50 + "\n")
    
        # ---------------------------------------------------------
        # 1. SUBJECT VARIANCE (Addressing Prompts #1, #3, #6)
        # ---------------------------------------------------------
        print("--- 1. SUBJECT VARIANCE (Math vs ELA) ---")
        if 'Assessment ELA Performance Value' in df_main.columns and 'Assessment Math Performance Value' in df_main.columns:
            ela_var = df_main['Assessment ELA Performance Value'].var()
            math_var = df_main['Assessment Math Performance Value'].var()
            ela_mean = df_main['Assessment ELA Performance Value'].mean()
            math_mean = df_main['Assessment Math Performance Value'].mean()
            
            print(f"ELA Performance Variance:  {ela_var:.4f} (Mean: {ela_mean:.4f})")
            print(f"Math Performance Variance: {math_var:.4f} (Mean: {math_mean:.4f})")
            
            # Coefficient of Variation (CV) = StdDev / Mean
            ela_cv = np.sqrt(ela_var) / ela_mean if ela_mean else 0
            math_cv = np.sqrt(math_var) / math_mean if math_mean else 0
            
            print(f"ELA Coeff. of Variation:   {ela_cv:.4f}")
            print(f"Math Coeff. of Variation:  {math_cv:.4f}")
            
            if math_var > ela_var:
                print(">> Result: Math scores show higher variance than ELA scores.")
            else:
                print(">> Result: ELA scores show higher variance than Math scores.")
        else:
            print("Columns for ELA/Math Performance Value not found.")
    
        # ---------------------------------------------------------
        # 2. GRADE LEVEL / SCHOOL TYPE VARIANCE (Addressing Prompts #11, #13, #15)
        # ---------------------------------------------------------
        print("\n--- 2. SCHOOL TYPE VARIANCE (Proxy for Grade Level) ---")
        if 'School Type' in df_main.columns:
            # Group by School Type (High, Middle, Elementary)
            type_stats = df_main.groupby('School Type')[['Assessment ELA Performance Value', 'Assessment Math Performance Value']].var()
            print("Variance by School Type:")
            print(type_stats)
            
            # Identify type with highest variance
            if not type_stats.empty:
                max_var_type = type_stats['Assessment Math Performance Value'].idxmax()
                print(f">> Result: {max_var_type} schools exhibit the highest variance in Math scores.")
        else:
            print("'School Type' column not found.")
    
        # ---------------------------------------------------------
        # 3. DISTRICT VARIANCE (Addressing Prompts #31, #32, #33)
        # ---------------------------------------------------------
        print("\n--- 3. DISTRICT VARIANCE ---")
        if 'District Name' in df_main.columns:
            # Calculate mean scores for each district first
            district_means = df_main.groupby('District Name')[['Assessment ELA Performance Value', 'Assessment Math Performance Value']].mean()
            
            # Calculate variance BETWEEN districts (Variance of the district means)
            between_district_var_ela = district_means['Assessment ELA Performance Value'].var()
            between_district_var_math = district_means['Assessment Math Performance Value'].var()
            
            print(f"Variance BETWEEN Districts (ELA):  {between_district_var_ela:.4f}")
            print(f"Variance BETWEEN Districts (Math): {between_district_var_math:.4f}")
            
            # Outlier Detection (Districts > 2 Std Dev from mean)
            math_std = district_means['Assessment Math Performance Value'].std()
            mean_of_means = district_means['Assessment Math Performance Value'].mean()
            outliers = district_means[
                (district_means['Assessment Math Performance Value'] > mean_of_means + 2*math_std) | 
                (district_means['Assessment Math Performance Value'] < mean_of_means - 2*math_std)
            ]
            if not outliers.empty:
                print(f">> Outlier Districts (Math > 2 SD from mean): {', '.join(outliers.index.tolist())}")
            else:
                print(">> No statistical outliers found among districts (Math).")
    
        # ---------------------------------------------------------
        # 4. SCHOOL VARIANCE (Addressing Prompts #42, #43)
        # ---------------------------------------------------------
        print("\n--- 4. SCHOOL VARIANCE ---")
        if 'School Name' in df_main.columns:
            school_var_math = df_main['Assessment Math Performance Value'].var()
            print(f"Total Variance Across All Schools (Math): {school_var_math:.4f}")
            
            # Compare Title I vs Non-Title I (Prompt #44)
            if 'Title 1 Indicator' in df_main.columns:
                t1_stats = df_main.groupby('Title 1 Indicator')['Assessment Math Performance Value'].var()
                print("\nVariance: Title I vs Non-Title I (Math):")
                print(t1_stats)
    
        # ---------------------------------------------------------
        # 5. SUBGROUP VARIANCE (Addressing Prompts #38, #40)
        # ---------------------------------------------------------
        print("\n--- 5. SUBGROUP VARIANCE (Demographics) ---")
        # Use the full cleaned dataframe here, not the 'Total' filtered one
        if 'Subgroup' in df_clean.columns and 'Assessment Math Performance Value' in df_clean.columns:
            # Calculate variance of the MEANS of each subgroup
            subgroup_means = df_clean.groupby('Subgroup')['Assessment Math Performance Value'].mean()
            print("Average Performance by Subgroup (Top 5 lowest):")
            print(subgroup_means.sort_values().head(5))
            
            print(f"\nVariance Between Subgroup Means: {subgroup_means.var():.4f}")
        
        print("\n" + "="*50)
        print("END OF ANALYSIS")
        print("="*50)
    
    # To run this script, ensure the csv file is in the same directory
    # analyze_scorecard_variance('WV_Balanced_Scorecard_Data_2025 (1).xlsx - SY25 Balanced Scorecard Results.csv')

    -----------------------------------------------------

    build an app for this 

    ---------------------------------------------------------------

     

    I have analyzed the structure of your West Virginia Balanced Scorecard data. To provide the most effective analysis, I have created a Streamlit Dashboard application.

    This interactive tool allows you to:

    1. Visualize Achievement Gaps: Instantly see the performance differences between subgroups (e.g., "White" vs. "Economically Disadvantaged" vs. "Children with Disabilities") based on the state-level records found in your data.

    2. Compare Districts: Select specific districts to compare their Math and ELA performance against the state average.

    3. Analyze School Trends: Explore correlations, such as the relationship between Attendance rates and Academic Performance.

    Here are the files to run this analysis.

    Scorecard Analysis Dashboard

    Nov 18, 8:28 AM
    Preliminary Analysis & Instructions

    Nov 18, 8:28 AM

    I have generated a Python Streamlit Dashboard and a Analysis Report to help you explore the Balanced Scorecard data.

    Files Included:

51. scorecard_app.py: An interactive dashboard that visualizes achievement gaps, compares district performance against state averages, and analyzes the correlation between attendance and grades.

52. README_analysis.md: Instructions on how to run the dashboard and a summary of key trends observed in the data snippet (specifically regarding achievement gaps).

Quick Start:

1. Make sure you have Python installed.

2. Install the required libraries: pip install streamlit pandas plotly.

3. Run the dashboard: streamlit run scorecard_app.py.

4. If the data doesn't load automatically, use the Upload CSV button in the sidebar to select your file.