Can student demographics alone predict a school's letter grade? Using machine learning algorithms like Gradient Boosting and K-means clustering, this analysis explores the deep (but not deterministic) link between poverty, diversity, and Tennessee's latest accountability scores.
Read MoreThe Tennessee Department of Education released the 2024-25 school letter grades on December 18, 2024. This is the third year of letter grades under the revamped formula that emphasizes academic achievement over growth. In this post, I continue my analysis of these letter grades, examining distribution trends, demographic correlations, and standout schools.
The data files used for this analysis are available from the Tennessee Department of Education's data downloads page. I merged the 2024-25 Letter Grade File with the 2024-25 School Profile data to examine demographic patterns.
Out of 1,905 schools listed for letter grades, 208 (10.9%) were ineligible to receive a grade. These schools were excluded from this analysis, leaving 1,697 eligible schools.
The 2024-25 letter grades were distributed as follows:
A: 355 (20.9%)
B: 483 (28.5%)
C: 491 (28.9%)
D: 302 (17.8%)
F: 66 (3.9%)
This continues the positive trend we've seen over the past three years. The percentage of A schools has increased from 17.4% in 2022-23 to 20.9% in 2024-25, while F schools have decreased from 5.4% to 3.9%.
Distribution Comparison
| Grade | 2022-23 | 2023-24 | 2024-25 | 3-Year Change |
|---|---|---|---|---|
| A | 17.4% | 19.0% | 20.9% | +3.5% |
| B | 26.1% | 26.8% | 28.5% | +2.4% |
| C | 30.4% | 29.8% | 28.9% | -1.5% |
| D | 20.7% | 19.6% | 17.8% | -2.9% |
| F | 5.4% | 4.8% | 3.9% | -1.5% |
The data shows steady improvement: more schools are earning A's and B's while fewer are receiving D's and F's.
| Grade | Achievement | Growth | Growth25 | Success Rate | LG Score |
|---|---|---|---|---|---|
| A | 4.85 | 4.94 | 4.29 | 58.2% | 4.83 |
| B | 4.06 | 3.78 | 3.47 | 44.5% | 3.91 |
| C | 3.21 | 2.52 | 2.91 | 35.4% | 2.94 |
| D | 2.08 | 1.61 | 2.71 | 23.7% | 2.01 |
| F | 1.00 | 1.00 | 2.90 | 12.5% | 1.18 |
A notable pattern persists from previous years: schools with an F grade actually show higher Growth25 scores (2.90) than D schools (2.71). This metric measures the progress of the lowest-performing 25% of students. While F schools are making gains with their struggling students, this improvement is not sufficiently weighted to improve their overall letter grade.
Summary Dashboard
The relationship between demographic factors and letter grades remains significant.
| Grade | 2022-23 | 2023-24 | 2024-25 |
|---|---|---|---|
| A | 18.3% | 22.3% | 20.5% |
| B | 28.9% | 31.1% | 29.2% |
| C | 36.5% | 38.9% | 35.0% |
| D | 42.0% | 45.9% | 41.7% |
| F | 54.5% | 49.6% | 56.8% |
| Grade | 2022-23 | 2023-24 | 2024-25 |
|---|---|---|---|
| A | 21.8% | 35.8% | 27.4% |
| B | 27.7% | 48.7% | 36.3% |
| C | 39.5% | 56.0% | 42.0% |
| D | 55.0% | 65.8% | 52.5% |
| F | 80.1% | 82.2% | 84.5% |
The gap between A schools and F schools remains substantial. A schools average 20.5% economically disadvantaged students compared to 56.8% in F schools. For BHN students, the gap is even more pronounced: 27.4% in A schools versus 84.5% in F schools.
Subgroup Three-Year Comparison
The correlations between demographic factors and letter grade scores show a consistent pattern across all three years:
| Subgroup | 2022-23 | 2023-24 | 2024-25 |
|---|---|---|---|
| Economically Disadvantaged | -0.50 | -0.44 | -0.47 |
| Black/Hispanic/Native American | -0.37 | -0.39 | -0.34 |
| Students with Disabilities | -0.09 | -0.13 | -0.12 |
The correlation between economically disadvantaged percentage and letter grade score (r = -0.47) indicates a moderate negative relationship. Schools with higher percentages of economically disadvantaged students tend to receive lower letter grades.
Correlation Scatterplots
Correlation Heatmap
The 2024-25 letter grades show continued improvement across Tennessee schools. Key findings include:
1. More schools are earning A's (20.9%, up from 17.4% three years ago) and fewer are receiving F's (3.9%, down from 5.4%).
2. The correlation between poverty and letter grades remains strong (r = -0.47), but some high-poverty schools continue to beat the odds.
3. F schools continue to show strong Growth25 scores, meaning they are making progress with their lowest-performing students, but this is not reflected in their overall grades.
The letter grade system continues to simplify complex accountability measures into easily digestible grades. While this provides transparency for families and communities, it's important to remember that these grades are heavily influenced by factors outside of schools' direct control, including poverty and segregation. Schools serving high-need populations face steeper challenges in achieving high letter grades, making the success of many schools all the more remarkable.
Also, it is important to note that many schools in Northeast TN (Johnson, Carter, Unicoi, Greene, and Washington Counties) went through major flooding in the fall, and they worked very hard to just have school, much less show improvement in accountability data.
This analysis used Python with pandas, matplotlib, seaborn, and scipy for data processing and visualization. I used Claude to do the coding and proof my writing.
Dear Members of the Board:
When I was in college at ETSU, I had the chance to study in Paris twice. We stayed at the Cité Universitaire, a community built after World War I so students from around the world could live and learn together. The purpose was simple: understanding. That, at its core, is what studying a world language gives our students.
Learning another language opens doors. Even the basic knowledge gained in high school helps students communicate, connect, and participate more confidently in a global economy. If our goal is to prepare young people for the future, giving them at least an introduction to another language seems essential.
It also expands their understanding of people beyond our borders. Many of our students may never travel internationally, but world language classes give them access to other cultures, traditions, and ways of thinking. These courses build cultural awareness, empathy, and curiosity: qualities that strengthen citizenship at home. These are qualities we hope to see in the citizens of Tennessee.
Mark Twain once said that travel is “fatal to prejudice, bigotry, and narrow-mindedness.” The study of world languages offers a similar antidote. It broadens young people’s perspectives and helps them see the world, and others, with clearer eyes.
Please do not eliminate world languages as a graduation credit. Tennessee already provides flexibility for students who wish to pursue an additional program of study in place of World Languages and Fine Arts. There is no compelling reason to remove this requirement entirely, and doing so would limit the opportunities we give our students.
Finally, consider what unintended consequences this would have on World Language programs in small, rural counties. Large metropolitan districts could absorb the cost of this, but schools on smaller budgets would have to consider eliminating these programs altogether.
Thank you for your consideration.
Jason B. Horne, Ed.D.
French Teacher
Assistant Director of Schools, Greeneville City Schools
You can find my TASBO presentation here.
When researching fantasy football strategies, you’ll notice that they almost all say the same thing: draft Ja’Marr Chase or Justin Jefferson in the first round. Beyond that, it gets a little hazy. Some say to go heavy with WRs and some say to go heavy with RBs, and some say don’t even look at a RB or QB until the 5th round. Sifting through all of this great advice is a slog, and ultimately, you’re left a bit more confused than you were in the beginning. This year, I’m turning to AI to organize this information into something that I can use both ahead of time and in real time to draft my fantasy football team.
My AI strategy
My plan is to use NotebookLM as my draft day guide and the place where all of my research ends up. You can see my notebook at the link above.
In the notebook, I had ChatGPT generate a deep research prompt to use. This is what it came up with:
"Fantasy Football Deep Research Prompt I am preparing for a 10-team PPR fantasy football league with the following setup: Roster Size: 16 players Starters: 9 Bench: 7 (+1 IR) Positions: QB: 1 (max 4) RB: 2 (max 8) WR: 2 (max 8) TE: 1 (max 3) FLEX: 1 (RB/WR/TE) D/ST: 1 (max 3) K: 1 (max 3) Scoring System: Head-to-Head Points, PPR (point per reception). Draft Type: Snake Draft. Research Goals Draft Rankings & Strategy Provide updated player rankings and tiers for QB, RB, WR, TE, FLEX, D/ST, and K in a 10-team PPR league. Identify early-round must-target players at each position based on projected points per game, positional scarcity, and upside. Recommend value picks and sleepers (players likely to outperform ADP). List players to avoid (injury risk, overvalued, declining production). Positional Breakdown Ideal draft strategy by round (e.g., RB-heavy early vs. WR priority). Depth chart analysis: when to prioritize QB vs. TE vs. FLEX depth. Defense & kicker strategy (when to draft, which matchups to target). Schedule & Playoff Outlook Identify players/teams with the most favorable fantasy playoff schedules (Weeks 15–17). Highlight key players with tough matchups to avoid. Advanced Analytics Use expected fantasy points (xFP), target share, snap counts, and red-zone usage to identify breakout players. Incorporate injury history and workload trends into rankings. Compare upside vs. floor for mid-round picks. League-Specific Adjustments How roster depth (7 bench spots, 1 IR) should affect draft strategy. Best balance between high-floor starters vs. high-upside bench stashes. Waiver wire strategy recommendations given a 10-team setup (shallower league, stronger FA pool) This should give you a highly targeted research pack with rankings, projections, draft plans, and league-specific strategy tailored to your exact format.I then used this same prompt for ChatGPT, Google Gemini, Claude, and Perplexity.
I downloaded each result to a PDF and then added it my NotebookLM. I generated a Mindmap, Podcast, and Presentation from NotebookLM (mostly for fun). The mindmap is very useful.
My plan is to talk to NotebookLM in real-time to let it know who has been drafted to give me a suggestion for my next pick. I will also put the same questions into AI. I only have 60 seconds to draft my next player, so I will have to be fast and be ready to go.
I’ll let you know how it turns out. Last year, I forgot about the draft only 15 minutes after sitting down to do it, and I finished 2nd in the league, so maybe this year will be better (or much worse).
Enjoy this video overview that my NotebookLM generated from my fantasy research docs, and don’t miss the podcast that it generated.
What can a Philip K. Dick novel teach us about the authenticity of AI-generated work? This post explores the cultural ideas of Wu and Wabi in the age of generative technology.
Read MoreSchool operations are not just behind the scenes. This post explores research showing how facilities, HVAC systems, nutrition, and support staff directly impact academic performance, attendance, and well-being.
Read MoreAs AI tools like ChatGPT become more common in schools, some educators are reaching for bluebooks in an attempt to restore academic integrity. But are we asking the right questions? In this post, I explore whether writing should remain the default method for assessing knowledge, or if it’s time to reimagine our approach. From speeches to projects to presentations, there may be better ways to engage students in deep, meaningful learning—especially in a world where AI is part of the process, not just a threat to it.
Read MoreA student asked me for advice for an assistant principal interview. I rattled off some things, and then I had ChatGPT fact-check them, and it turned into this blog post.
Interviews are more than just a series of questions—they’re a chance to show who you are, how you think, and why you're a great fit for a leadership role in a school. Here’s a research-supported guide that blends practical advice with professionalism.
People can spot inauthenticity quickly. Trying to be someone you're not is hard to maintain—especially under pressure. Research shows that authenticity in high-stress interviews improves how candidates are perceived (Krumhuber et al., 2022). Be yourself. Be prepared. That’s more than enough.
The strongest interviews feel like a dialogue, not an interrogation. When candidates ask thoughtful questions and stay conversational, it builds rapport and demonstrates interest. SHRM (2021) notes that hiring panels consistently rank these candidates higher.
Avoid generalities like “I’m ready for a change” or “I want a promotion.” Talk about why you want this job in this school with these people. Research on person-organization fit shows that alignment with values and mission significantly increases hiring likelihood (Kristof-Brown et al., 2005).
Come with a clean, professional copy of your resume, letter of intent, and any relevant work samples. A 2023 LinkedIn survey found that 74% of hiring managers value candidates who bring a portfolio or artifacts to support their answers.
You may be asked to show something electronically—lesson plans, data reports, or digital tools. Being able to pull something up shows you're prepared and tech-capable.
Even if it’s just jotting down a few words, taking notes signals engagement and gives you a moment to think before responding. According to Forbes (2021), it also makes you appear focused and professional.
Your clothing sends a message before you speak. Professional dress still impacts perceived competence and leadership. For men, a jacket tends to increase perceived authority. For women, conservative neckline choices receive more serious consideration—largely due to unconscious bias (Psychology Today, 2022; Bègue et al., 2019). This isn’t about style policing—it’s about managing perception.
I always bring a bright-orange Yeti to high-stakes meetings. It helps keep me calm. Research supports the idea that small comfort objects can lower anxiety and improve focus (Clinical Psychological Science, 2018).
Use the “thirds rule”: spend part of your time making eye contact with the center, part with the left, and part with the right side of the panel. This creates a sense of inclusion and presence (Toastmasters International).
If you don’t have a perfect answer, say so. Ask a clarifying question, take a breath, and gather your thoughts. Hiring managers respect honesty and thoughtfulness over a shaky bluff.
Honesty and humility remain two of the top-rated traits hiring committees look for in leadership roles (Indeed Hiring Lab, 2022).
Be humble. Be confident. Be prepared.
But most of all—be yourself.
I had a mentor teacher who was fairly unethical, and once said to me,
“Horne, if you start getting complaints, give ’em all A’s. Those complaints will dry up.”
It was meant as a joke (I think), but like most bad jokes, it carried too much truth. The idea was simple: avoid scrutiny by keeping everyone happy even if it meant compromising the core purpose of education: teaching and learning.
That memory has been on my mind lately as I think about the role of AI in schools.
What happens when a teacher uses AI to design the lesson… AI to generate the assessment… students use AI to complete it… and the teacher uses AI to grade it… and everyone makes an A?
Nobody complains.
But nobody learns.
And everyone quietly agrees not to say the obvious part out loud: This isn’t real.
That’s my greatest concern—not that AI will destroy education, but that it will dull it. That we’ll settle for the illusion of learning because it’s easier, faster, quieter. That we’ll stop asking, “Did they grow?” and start asking, “Did it look good?”
We could end up with a system where everyone is satisfied—teachers, students, parents, even administrators—and yet nothing of substance is happening. It looks like learning, but it’s just going through the motions.
That’s the invisible handshake.
In early 2024, I published a blog post exploring how machine learning could predict Tennessee school letter grades based on demographic data. That analysis provided insight into the structural factors influencing school performance metrics, particularly the significant role of economic disadvantage. Since then, I’ve expanded the dataset to include both the 2023 and 2024 school letter grades, refining the model to capture trends over time and further assess the predictive power of demographic variables. This post serves as an update on that work and a precursor to a more detailed research paper.
Tennessee assigns letter grades (A–F) to schools based on multiple criteria, including student achievement, academic growth, and college/career readiness. While these grades are intended to reflect school quality, they often correlate strongly with socioeconomic and demographic factors.
For this updated analysis, I merged data from both years to improve model robustness. The methodology remained consistent:
Data Collection: Letter grades and demographic data were sourced from the Tennessee Department of Education.
Data Processing: Letter grades were converted to a numeric scale, and missing values were cleaned.
Machine Learning Models:
Random Forest Regression for feature importance analysis
Linear Regression to estimate the impact of individual factors
Correlation Analysis to identify relationships between variables
Letter Grade Scale
A 4.5-5.0
B 3.5-4.4
C 2.5-3.4
D 1.5-2.4
F 1.0-1.4
Economic Disadvantage Remains the Strongest Predictor
Schools with higher percentages of economically disadvantaged students continue to receive lower letter grades. The model estimates that for every 10% increase in economically disadvantaged students, the expected letter grade drops by 0.35 on a 1.0–5.0 scale.
Special Education Enrollment Also Impacts Letter Grades
The presence of students with disabilities (SWDs) is another contributing factor, though to a lesser extent. A 10% increase in SWD enrollment is associated with a 0.11-point decrease in letter grade.
Race and Economics Are Closely Linked
Schools with higher percentages of Black, Hispanic, and Native American students tend to have lower grades. However, when controlling for economic disadvantage, racial composition becomes a less significant predictor. This suggests that economic factors, rather than race itself, drive these disparities.
See the correlation matrix below:
Demographics Explain About One-Third of Grade Variation
The updated linear regression model explains 29% of the variance in school letter grades, meaning that while demographics play a measurable role, other factors—such as instructional quality, funding, and school leadership—are also crucial.
What If Demographics Were Removed?
A simulated model scenario where a school had no economically disadvantaged students and no SWDs predicted an average letter grade of 4.5 (A range). This reinforces the idea that many low-performing schools face structural challenges beyond their control.
In the context of Tennessee’s voucher era, public schools operate under significantly different—and often more challenging—conditions than private schools, making their achievements all the more remarkable. The assumption that private schools inherently provide a superior education doesn’t hold up under scrutiny.
Unlike private institutions, which can be selective in their admissions, public schools serve every student—regardless of economic status, disability, or other challenges. Private schools often have the luxury of limiting enrollment to students who fit their preferred criteria, while public schools are tasked with educating all children who walk through their doors—often with fewer resources and greater accountability.
Despite these challenges, many public schools deliver exceptional outcomes. Educators work tirelessly to support students from diverse backgrounds, implementing innovative instructional strategies and targeted interventions to ensure student success. Their ability to foster academic achievement and social growth, even in the face of structural obstacles, speaks to the public education system's resilience, dedication, and effectiveness.
Public schools should be celebrated rather than maligned for their role in serving all students, strengthening communities, and proving that educational excellence is not exclusive to selective institutions.
This blog post was proofread with suggested corrections by Grammarly.
This post features an interactive table and map to help you explore the approved schools. Using the TN Non-Public School list, I made an interactive table of the voucher-eligible non-public schools in TN.
I also made a map of them below. Suggestion: Zoom in.
Enjoy.
Only 12 private schools qualify for vouchers in Northeast TN. Part of the voucher law said that only private schools that were category I, II, or III can receive voucher funds. You can see the list of Non-Public Schools here: https://www.tn.gov/education/families/school-options/non-public-schools.html
In Tennessee, non-public schools are classified into five categories, each with distinct approval processes and operational requirements:
Category I: These schools are directly approved by the Tennessee Department of Education. They must adhere to state regulations similar to public schools, including employing licensed teachers and conducting annual standardized testing.
Category II: Schools in this category are accredited by private agencies recognized by the State Board of Education. These accrediting agencies oversee the schools to ensure they meet specific educational standards.
Category III: These are schools accredited by regional accrediting bodies approved by the State Board of Education. They operate under the guidelines of their respective accrediting organizations.
Category IV: Known as church-related schools, these institutions are affiliated with religious organizations and are exempt from certain state regulations. They are recognized by associations listed in Tennessee Code Annotated § 49-50-801.
Category V: These schools are acknowledged for operation with minimal state requirements. Teachers are required to hold at least a bachelor's degree but are not mandated to have teaching certificates.
Lakeway Christian Schools: Tri-Cities Christian Academy Campus – Blountville
Living Springs Christian Academy – Gray
Greeneville Adventist Academy – Greeneville
Lakeway Christian Schools: Boones Creek Christian Academy – Johnson City
Munsey Kindergarten – Johnson City
Providence Academy – Johnson City
St. Mary School - Johnson City – Johnson City
Central Baptist Kindergarten – Johnson City
St. Dominic Catholic School – Kingsport
All Saints' Episcopal School – Morristown
Lakeway Christian Schools: Cornerstone Christian Academy Campus – Morristown
Lighthouse Christian Academy (formerly Morristown SDA School) – Morristown
In my previous blog, I provided a detailed breakdown of the TN Letter Grades. In this post, I want to shift focus and highlight the schools that stand out as outliers, defying the norms. We’ll not only look at the schools that achieved perfect scores on their TN Letter Grades—excelling in Achievement, Growth, Growth 25, and College and Career Readiness (for high schools)—but also examine schools with high percentages of Economically Disadvantaged (ED) students, Students with Disabilities (SWD), and Black, Hispanic, and Native American (BHN) students that still managed to earn an “A.”
Oak Ridge: Willow Brook Elementary
Blount County: Carpenters Elementary School
Maryville: John Sevier Elementary
Carter County: Little Milligan
Elizabethton: West Side Elementary
Cheatham County: Kingston Springs Elementary
Cocke County: Grassy Fork Elementary
Metro Nashville Public Schools: Meigs Middle
Metro Nashville Public Schools: Valor Flagship Academy
Metro Nashville Public Schools: Valor Voyager Academy
Decatur County: Decaturville Elementary
Dickson County: Stuart Burns Elementary
Dickson County: Centennial Elementary
Gibson Co Sp Dist: South Gibson County Elementary School
Greeneville: Tusculum View Elementary
Hamilton County: Daisy Elementary School
Hamilton County: McConnell Elementary School
Hamilton County: Thrasher Elementary School
Hawkins County: Mt Carmel Elementary
Humphreys County: Waverly Elementary
Knox County: Blue Grass Elementary
Knox County: A L Lotts Elementary
Knox County: Farragut Intermediate
Knox County: Shannondale Elementary
Loudon County: Highland Park Elementary
McNairy County: Selmer Elementary
Madison County: Community Montessori School
Madison County: South Elementary
Marion County: Whitwell Middle School
Montgomery County: Sango Elementary
Rhea County: Frazier Elementary
Rutherford County: Buchanan Elementary
Rutherford County: Christiana Elementary
Rutherford County: Eagleville School
Rutherford County: Lascassas Elementary
Rutherford County: Thurman Francis Arts Academy/Magnet School for the Arts
Rutherford County: Walter Hill Elementary
Rutherford County: Wilson Elementary School
Rutherford County: Stewartsboro Elementary
Rutherford County: Stewarts Creek Elementary School
Collierville: Tara Oaks Elementary School
Germantown: Dogwood Elementary School
Lakeland: Lakeland Elementary School
Bristol: Holston View Elementary
Kingsport: Thomas Jefferson Elementary School
Kingsport: Andrew Johnson Elementary School
Sumner County: Bethpage Elementary
Sumner County: Howard Elementary
Sumner County: Liberty Creek Elementary
Sumner County: Oakmont Elementary
Sumner County: Clyde Riggs Elementary
Sumner County: Union Elementary School
Johnson City: Lake Ridge Elementary
Johnson City: South Side Elementary
Williamson County: Creekside Elementary School
Williamson County: Trinity Elementary
Wilson County: Lakeview Elementary School
Wilson County: Rutland Elementary
Oak Ridge: Oak Ridge High School
Metro Nashville Public Schools: Hume - Fogg High
Metro Nashville Public Schools: Martin Luther King Jr School
Metro Nashville Public Schools: Valor Flagship Academy
Greeneville: Greeneville High School
Hamilton County: Chattanooga High School Center for Creative Arts
Hamilton County: Chattanooga School for the Arts and Sciences Upper
Hamilton County: East Hamilton High School
Hamilton County: Hamilton County Collegiate High at Chattanooga State
Hamilton County: STEM School Chattanooga
Knox County: L N STEM Academy
Madison County: Jackson Central-Merry Early College High
Madison County: Madison Academic Magnet High School
Rutherford County: Central Magnet School
Rutherford County: Eagleville School
Memphis-Shelby County Schools: Medical District High School
Memphis-Shelby County Schools: East High
Memphis-Shelby County Schools: Middle College High
Memphis-Shelby County Schools: University High School
Memphis-Shelby County Schools: White Station High
Memphis-Shelby County Schools: Whitehaven High
Arlington: Arlington High
Bartlett: Bartlett High School
Collierville: Collierville High School
Williamson County: Franklin High School
Williamson County: Independence High School
Williamson County: Nolensville High School
Williamson County: Ravenwood High School
Wilson County: Green Hill High School
Tennessee Public Charter School Commission: KIPP Antioch Global High School
For 2023-2024 students, the average percentage of ED students in F schools was 49.64%. Using that as a benchmark, here are the schools that earned an A with ED percentage of 50% or higher.
Putnam County: Burks Elementary (66%)
Memphis-Shelby County Schools: Delano Elementary (63%)
Putnam County: Northeast Elementary (58%)
Memphis-Shelby County Schools: Leadership Preparatory Charter School (58%)
Memphis-Shelby County Schools: Newberry Elementary (55%)
For 2023-2024 students, the average percentage of BHN students in F schools was 82.2%. Using that as a benchmark, here are the schools that earned an A with a BHN percentage of 83% or higher:
Memphis-Shelby County Schools: Medical District High School (97.5%)
Memphis-Shelby County Schools: Southwind Elementary (94.0%)
Memphis-Shelby County Schools: Delano Elementary (97.5%)
Memphis-Shelby County Schools: East High (85.0%)
Memphis-Shelby County Schools: Middle College High (97.5%)
For 2023-2024 students, the average percentage of SWD students in F schools was 15.96%. Using this as a benchmark, here are the schools that earned an A with an SWD percentage of 16% or higher:
Anderson County: Grand Oaks Elementary (25%)
Clinton: North Clinton Elementary (31%)
Oak Ridge: Willow Brook Elementary (22%)
Bedford County: Cascade Elementary (16%)
Blount County: Carpenters Elementary School (18%)
Putnam County: Burks Elementary (66%)
Memphis-Shelby County Schools: Delano Elementary (63%)
Putnam County: Northeast Elementary (58%)
Memphis-Shelby County Schools: Leadership Preparatory Charter School (58%)
Memphis-Shelby County Schools: Newberry Elementary (55%)
The impact of school letter grades on schools, school leaders, teachers, and students has largely unfolded as anticipated. It's not just a coincidence that letter grade legislation often serves as a catalyst for voucher programs across numerous states. By simplifying school accountability into easily digestible grades, we risk oversimplifying a complex system that even experts grapple with. We must urge a more comprehensive understanding of educational quality that goes beyond mere letters, ensuring that real progress is made for our students and educators alike.
To understand how the computation of these letter grades affects schools, I took a preliminary look last year at the grade distribution. The improvement of achievement percentages is fundamental to federal accountability, but it has been entirely removed from these state letter grades.
In the second year, I was confident we would witness significant improvements. Given the wealth of experience that many educators possess, having navigated through the complexities of school accountability, I believe we would have effectively deciphered the strategies needed to enhance our performance and elevate those letter grades.
Let’s compare the two years.
Data Sources
2024 School-Level Profile Data
Out of the 1,905 schools listed for letter grades in Tennessee, 215 schools (11.29%) were ineligible to receive a grade. These schools were excluded from the analysis to focus on the distribution of grades among eligible schools. This is similar to 2022-2023 where 210 out of 1900 schools were ineligible. To see how this is determined, you can visit the TN DOE Webpage on letter grades here.
Distribution
In 2023-2024, the letter grades were distributed as follows:
A: 320 (17%)
B: 450 (24%)
C: 500 (27%)
D: 330 (18%)
F: 80 (4%)
Compared to 2022-2023, the distribution has shifted, highlighting some key trends:
Increase in A Grades:
The percentage of schools receiving an A increased slightly from 17% to 17% (294 to 320 schools).
Slight Decline in C Grades:
C grades decreased from 30% (513 schools) to 27% (500 schools), with schools likely moving into higher or lower grade categories.
Reduction in D and F Grades:
D grades fell from 21% (350 schools) to 18% (330 schools), and F grades dropped from 5% (92 schools) to 4% (80 schools). This is a positive trend, suggesting fewer schools are struggling at the lowest levels.
This year's distribution continues to resemble a normal curve, with a slight skew toward A grades. While last year, 77% of schools fell into the B, C, or D categories, this year that number has dropped slightly to 75%, reflecting improvements at both the top and bottom of the grading scale.
Generated by ChatGPT
The comparison of average scores across metrics provides insight into how schools in Tennessee have performed over the past two years. Here’s a breakdown of the trends by letter grade:
2022-2023:
A: 4.85 | B: 3.95 | C: 2.96 | D: 2.00 | F: 1.02
2023-2024:
A: 4.84 | B: 3.99 | C: 3.04 | D: 2.06 | F: 1.05
Key Changes:
Grades B and C saw slight improvements, with C grades showing a noticeable increase from 2.96 to 3.04.
Grades A, D, and F remained relatively stable, with minor differences that do not indicate significant shifts.
2022-2023:
A: 4.96 | B: 3.92 | C: 2.80 | D: 1.62 | F: 1.00
2023-2024:
A: 4.92 | B: 3.82 | C: 2.73 | D: 1.60 | F: 1.03
Key Changes:
A Grades dropped slightly from 4.96 to 4.92, although they remain well above the threshold for full growth points.
Grades B and C also saw modest declines, while F Grades slightly improved from 1.00 to 1.03.
2022-2023:
A: 4.45 | B: 3.58 | C: 3.10 | D: 2.64 | F: 3.21
2023-2024:
A: 4.40 | B: 3.54 | C: 2.99 | D: 2.64 | F: 2.69
Key Changes:
F Grades dropped from 3.21 to 2.69, reflecting a notable decrease in the lowest-performing schools' progress for students in the bottom quartile.
Grades A, B, and C showed small decreases, while D Grades remained unchanged at 2.64.
Grade A:
Continues to represent the highest levels of achievement and growth. However, small declines in both growth and growth25 scores may warrant further investigation into sustaining top-tier performance.
Grade B:
Slight improvements in achievement but minor decreases in growth and growth25 suggest consistent performance with room for growth.
Grade C:
The improvement in achievement (from 2.96 to 3.04) is encouraging, though declines in growth and growth25 could indicate challenges in maintaining momentum.
Grade D:
Minimal changes across metrics show stability but limited progress in improving scores.
Grade F:
While achievement scores improved slightly (1.02 to 1.05), the sharp decline in growth25 (3.21 to 2.69) highlights ongoing difficulties in addressing the needs of the lowest-performing students.
Subgroup Performance
One of the most revealing pieces of data from 2022-2023 was the high percentage of economically disadvantaged (ED) and Black/Hispanic/Native American (BHN) students in F schools. I wondered if that trend would stay the same or change, and for ED students, it has improved.
Generated by ChatGPT
Increase in Economic Disadvantage for Higher Grades:
Schools with an A grade saw an increase from 18.34% (2022-2023) to 22.27% (2023-2024) in economically disadvantaged students. This suggests a positive trend toward equity in top-performing schools.
Slight Increase for B and C Grades:
B grades rose from 28.92% to 31.10%, and C grades increased from 36.49% to 38.90%. These shifts indicate a growing representation of disadvantaged students in mid-tier schools.
Reduction in D and F Grades:
The percentage of disadvantaged students in D grades dropped from 41.97% to 45.93%. Similarly, F grades saw a reduction from 54.52% to 49.64%.
This may reflect targeted interventions or progress in struggling schools.
This trend is further reflected in the correlation between the percentage of economically disadvantaged students and letter grade scores. In 2022-2023, the correlation was -0.50, while in 2023-2024, it decreased slightly to -0.44. Although still significantly negative, the weaker correlation suggests a modest reduction in the impact of economic disadvantage on school performance.
BHN Improvement
One of the most disturbing and glaring trends from 2022-2023 was the high percentage of BHN students in F schools. This has improved dramatically in 2023-2024. Here is a comparison graph.
Generated by ChatGPT
Increases in A and B Grades:
BHN representation in A-grade schools increased significantly from 21.82% to 35.79%.
B-grade schools also saw a large jump from 27.74% to 48.72%, reflecting positive strides toward equity in higher-performing schools.
Consistent Growth Across Grades:
BHN percentages rose steadily across all letter grades, with C and D schools showing increases of ~20 percentage points each.
Marginal Change in F Grades:
BHN representation in F-grade schools increased slightly from 80.05% to 82.20%, indicating persistent challenges for the most disadvantaged schools.
The Pearson Correlation Coefficient between BHN student percentages and letter grade scores remained virtually unchanged, increasing slightly from -0.37 in 2022-2023 to -0.39 in 2023-2024.
Students with Disabilities Populations
The impact of Students with Disabilities (SWD) on letter grades felt counter-intuitive in 2022-2023. Schools with A and F grades had nearly identical percentages of SWD students, raising questions about how disability representation aligns with school performance metrics. In 2023-2024, this pattern shifted slightly, with SWD representation increasing across all letter grades.
Notably, the largest growth occurred in F-grade schools, where SWD percentages rose from 12.24% in 2022-2023 to 15.96% in 2023-2024. This sharp rise highlights a growing concentration of SWD students in the lowest-performing schools, emphasizing systemic challenges that disproportionately affect these populations.
Meanwhile, higher-performing schools also experienced modest increases in SWD percentages, with A-grade schools rising from 12.87% to 13.53%. Despite these gains, the gap between high- and low-performing schools widened, underscoring the need for equity-driven interventions to support SWD students more effectively.
This evolving dynamic invites further exploration into the role of SWD representation in shaping school letter grades and how policies can better address the unique challenges faced by these students.
Generated by ChatGPT
The Pearson Correlation Coefficient between SWD percentages and letter grade scores remained relatively weak, with a value of -0.13 in 2023-2024, compared to -0.09 in 2022-2023, indicating no significant relationship between the two.
Interestingly, the SWD distribution graph for 2023-2024 more closely resembles the patterns observed in the ED and BHN graphs, aligning with expectations and suggesting potential connections between these demographics and school performance metrics.
Conclusions
Schools in the state performed better in 2023-2024 on their school letter grades than they did in 2022-2023. The percentage of ED, BHN, and SWD students increased, reflecting a positive shift toward greater inclusion and representation across various demographics. However, disparities persist, particularly in the correlation between economic disadvantage and letter grades, as well as the overrepresentation of BHN and SWD students in lower-performing schools.
While the overall improvement in letter grades is encouraging, these results underscore the need for targeted interventions and support for schools serving disadvantaged populations. Policies should focus on addressing systemic inequities to ensure all students, regardless of background, have access to the resources and opportunities needed to succeed.
Future analyses should continue monitoring these trends to assess the long-term impact of accountability measures and demographic shifts on educational equity and performance.
AI Disclaimer
I used ChatGPT to generate comparative bar charts since I had them in two different Jupyter Notebooks. It was easier to send screenshots of them and have it create new ones. I also used ChatGPT to clean up my writing and formatting.
I only have a few weeks left in Campbell County, and I wanted to reflect on it. I want to write about the improvements that we have made since I’ve been here. In district supervisor positions, you’re always on a continuum of change and improvement. The truth is the job is never finished.
The other truth is that you never do this job alone. I have been part of one of the strongest teams I can even imagine. So please don’t read this blog that these are my accomplishments. I’ve only been fortunate to have been part of them.
It is a team effort that is led by the Director of Schools. If you don’t have a strong Director, then it is tough to get anything accomplished. In this situation, I had a very strong Director in Ms. Fields who pushed all of us to balance improvement while considering the already strenuous workload on teachers, principals, and students. Ms. Fields also reminded us regularly not to forget what it’s like to be in a classroom and to lead a school. She encouraged us to be empathetic to our people but to have high standards for our outcomes. This balance is what has allowed all of us to be successful.
In no order of importance, I thought I would reflect on some of the accomplishments that come to mind.
Collaborative Conferencing
Being involved in PECCA was a great experience. It really put me in touch with the past, present, and future of Campbell County. When I initially came on the Administration Team, there were a lot of employee organization leaders who had been involved in Collective Bargaining prior to 2011. I remember these folks and their work, and I noticed the MOU felt more like the Collective Bargaining Agreement than an MOU.
Something else that became obvious to me is that most teachers had no idea that they had an MOU and that they had all of these people working on it. It is hours of work after hours for which no one is getting paid anything extra. It is truly a labor of love, and for employee organizations, it is the most direct form of advocacy that they have.
While I agreed with most of what was there, there were some glaring loopholes that needed to be closed. For example, the MOU originally read that a teacher could take up to 20 consecutive days of unpaid leave before they were put on unpaid leave by the Director of Schools. This meant that a teacher could take 19.5 days off, come back for half a day, and then take 19.5 days off again. This means a teacher could keep their position while only showing up for 8 days a school year. It wasn’t until this was being discussed that I saw an employee test these waters, but we finally changed it.
The section on unpaid leave is now more in line with policy and procedures. It states, “To prevent being placed on automatic leave, in cases where the teacher cannot apply for leave under the Family Medical Leave Act, the teacher must provide documentation to the Director of Schools to justify their unpaid leave. “This was a lot of work and discussion by the PECCA Members from both the Admin Team and the Teacher Team. I consider it a huge accomplishment that we got this done, and even added a definition of sick leave which was completely missing.
Let me be even more emphatic about it: there was no sick leave policy for certified employees at all. This is why it was being abused and people had an entitled attitude about “their days.” These days technically belong to the state which is why you get to transfer them from system to system, and the state is very clear about how they are to be used. Scanning other systems’ policies for sick leave showed just how restrictive they were elsewhere, and I felt like I was spinning my wheels trying to solve Chronic Student Absenteeism when I couldn’t solve Chronic Teacher Absenteeism.
It is very satisfying to look back on that work to see that everyone in the group saw that this wasn’t a tenable situation for a school system that wanted to be whole and healthy. At our best, we had everyone working together. There were some long evenings and not everyone saw eye-to-eye, but in the end, we got things done, and I will always be proud of that.
Test Scores
To be clear, the test scores always belong to the students. After all, they’re the ones who take the tests. Even though this is true, test scores are the backbone of school accountability, and even though that changed dramatically with the school letter grades, we’ve been getting school report cards for a long time.
For those who aren’t in education, there are two ways to look at your progress in test scores. One way is called “Achievement” and the other way is called “Growth.” Achievement is the percentage of students who score “proficient” on the TCAP or EOC. Growth is a complex measure that predicts what a student’s test score should be, and then whether it is above that or below it, it is considered positive or negative growth. When put all of that together, you get a teacher’s growth, a school’s growth, and so on.
In Campbell County, our Achievement has improved every single year. As you can see from this School Board Data Presentation, it has improved every year.
District Achievement Overview
The provided charts illustrate the trends in district achievement over time, specifically focusing on the growth in English Language Arts (ELA) and Math across different grade levels: elementary (grades 3-5), middle (grades 6-8), and high school (grades 9-12).
District Achievement Over Time
Grades 3-5: Achievement increased from 23 in 2020-2021 to 34 in 2022-2023, indicating an 11-point growth.
Grades 6-8: Achievement rose from 17.5 in 2020-2021 to 25 in 2022-2023, marking a 7.5-point growth.
Grades 9-12: Achievement saw a significant rise from 14.4 in 2020-2021 to 26.8 in 2022-2023, showing a 12.4-point growth.
District-wide Performance in ELA and Math
Overall Trends: Both ELA and Math have shown considerable growth over the years, with notable increases from 2021 to 2023.
ELA: Increased from 21.6 in 2017 to 28.4 in 2023.
Math: Grew from 17.7 in 2017 to 24.6 in 2023.
Elementary School Performance
ELA: The performance has steadily increased, reaching 34 in 2023 from 22 in 2017.
Math: Similarly, Math performance rose to 33 in 2023 from 22 in 2017.
Middle School Performance
ELA: After a drop in 2021, ELA performance rebounded to 22 in 2023, slightly below the 2019 peak of 25.
Math: Following a dip in 2021, Math performance recovered, reaching 27 in 2023, just shy of the 2019 peak of 31.
High School Performance
ELA: High school ELA performance has shown significant improvement, reaching 28 in 2023, up from 21 in 2017.
Math: Despite some fluctuations, Math performance improved to 12 in 2023 from a low of 5 in 2018.
Comparative Insights
Elementary School Improvement:
Both ELA and Math have shown consistent improvement, suggesting effective early education strategies. The continuous rise in performance indicates a solid foundation being built at this level.
Middle School Improvement:
ELA and Math faced a dip in 2021, likely due to disruptions caused by external factors (e.g., the pandemic). However, the rebound in 2022 and 2023 suggests recovery efforts are taking effect, though there's room for improvement to reach or exceed past peaks.
High School Improvement:
ELA shows a marked improvement, indicating effective teaching strategies and student engagement at the high school level. Math, while improving, shows more variability, highlighting the need for targeted interventions to sustain and further this growth
Growth
Growth tells a different story than achievement. While many of our students are performing better than they were in 2017, the overall district growth decreased from a level 3 to a level 1 in 2023. The reason for this is because of so many large negative growth scores. This suggests that students are not performing their best on the test. Given their benchmarking scores suggest they are capable of performing better than they did, it suggests that students are just not trying on the TCAP and EOC tests. We are putting in a lot of programs to make that happen.
Here are high school growth levels over the past three years:
The analysis of high school growth indices for the subjects Algebra I, Algebra II, English I, English II, and Geometry over the years 2021 to 2023 reveals several trends and insights.
Algebra I:
The Growth Index shows a positive trend initially, peaking in 2022. However, there is a noticeable decline in 2023, bringing the index closer to its 2021 level. This suggests that while there was initial improvement, the gains were not sustained.
Algebra II:
Similar to Algebra I, Algebra II also experienced an upward trend, peaking in 2022 before a significant decline in 2023. This indicates a potential issue in maintaining growth in Algebra II over the long term.
English I:
The Growth Index for English I shows an initial increase, peaking in 2022, followed by a decline in 2023. Despite the fluctuation, the overall trend suggests a need for focused strategies to sustain and improve growth.
English II:
English II demonstrates a consistent decline in the Growth Index from 2021 to 2023. This steady decrease highlights the need for interventions and targeted support to reverse the downward trend.
Geometry:
Geometry shows a positive and continuous increase in the Growth Index from 2021 to 2023, indicating successful strategies and improvements in teaching and learning practices in this subject.
Overall, the high school data reflects a mixed performance with notable areas for improvement, especially in maintaining growth in Algebra I, Algebra II, and English I, and reversing the decline in English II. The positive trend in Geometry is encouraging and may serve as a model for other subjects.
Here are growth rates for Math and ELA in Grades 3-8:
Subjects: English Language Arts (ELA) and Math
The analysis of growth indices for English Language Arts (ELA) and Math for grades 4-8 over the years 2021 to 2023 provides insights into student performance and areas needing attention.
English Language Arts (ELA):
Grade 4: The Growth Index for ELA in Grade 4 remained relatively stable, indicating consistent performance without significant fluctuations.
Grade 5: The Growth Index shows minor fluctuations but remains generally stable, suggesting a need for sustained support to drive further improvement.
Grade 6: There is a steady decline in the Growth Index from 2021 to 2023, indicating challenges in maintaining growth and highlighting the need for targeted interventions.
Grade 7: The trend shows a slight decline, calling for strategies to enhance growth and support students in this grade.
Grade 8: The Growth Index shows a consistent decline, indicating significant challenges and a need for robust interventions to reverse the trend.
Math:
Grade 4: Math shows a stable but slightly declining trend, emphasizing the need for continuous support.
Grade 5: The Growth Index for Math displays a declining trend, suggesting areas needing improvement.
Grade 6: Similar to ELA, the Math Growth Index shows a decline, pointing to a need for targeted efforts to boost performance.
Grade 7: The trend shows a decrease, indicating challenges in maintaining growth.
Grade 8: The Growth Index shows fluctuations, with a decline from 2022 to 2023, indicating areas needing attention and support.
Overall, the data for grades 4-8 highlights the need for sustained efforts to improve and maintain growth in both ELA and Math. While some grades show stability, others indicate significant areas for improvement, particularly in grades 6-8. Focused interventions and continuous support are essential to reverse declining trends and enhance overall student performance in these critical subjects.
One of the biggest changes we’ve made is the transparency with data. When I came on board, I was told that AMOs (Annual Measurable Objectives) were handed out on a sticky note. I was also told that they would present whatever they wanted about data before the board. While testing accountability wasn’t as hot and heavy as it now, they still had the same numbers to give the board.
Ultimately, more students are performing better on their state tests, and that is good news for the students in Campbell County. This is a group effort from students, teachers, principals, and central office administrators.
ACT
Campbell County improved its ACT average from 16.6 in 2020-2021 to 17.3 in 2022-2023. Also, the county the most students with an ACT over 21 in its history in 2022-2023. Also the participation rate for ACT is 99%. It had fallen below 95% in 2017-2018.
Ready Graduate
The percentage of Ready Graduates has increased from 26.5% in 2017-2018 to 40.1% in 2023-2024.
CTE Concentrators
The percentage of CTE Concentrators improved from 36.5 in 2017-2018 to 54.3% in 2022-2023.
PostSecondary Going Rates
Pre-Pandemic Growth:
The district saw a steady increase in postsecondary enrollment rates from 2016-2017 to 2018-2019, peaking at 57.3%. This suggests effective strategies and strong support systems were in place to encourage students to pursue higher education.
Impact of COVID-19:
The 2019-2020 and 2020-2021 academic years saw a decline in enrollment rates, dropping to 47.3% and 46.9%, respectively. The disruptions caused by the pandemic likely played a significant role in this decline. These years highlight the challenges faced by students and the need for adaptive strategies during crises.
Post-Pandemic Recovery:
The increase to 51.8% in 2021-2022 indicates a positive recovery trend. This suggests that the district's efforts to support students in the aftermath of the pandemic are beginning to take effect.
Processes
There have been so many processes that have improved since 2018. It is impossible to list them all, but here are the ones that come to mind. Again, these are all group efforts.
· A comprehensive VOIP telephone system for the entire county.
· A modern website with an app.
· A robocall system for absences and emergencies.
· Upgraded technology for every school.
· A Facebook page for the district and every school.
· Teacher of the Year recognition
· An online application and HR system
· Compliance with the new counselor standards
· Updated 504 procedures
· Enrollment Dashboarding
· Conduct Data Collection
· A Tiered System for Truancy
· Data and Accountability updates for principals
· Weekly TEAM reports
· Ayers training for Academic Coaches
· Regular meetings for Academic Coaches
· A leadership academy for teachers.
· A process for awarding and tracking tenure.
· A solid chain of command structure.
· McREL Training
· SREB Training
· Interventionist Positions
· Increased AP and Dual Enrollment participation
· Vans for CTE and for Homeless student transportation
· Ayers Scholars Program
· FAFSA Frenzy
· Turf fields at both high schools (JHS is currently being planned.).
· We trained over 250 substitute teachers while I was there.
· We built an online platform during COVID.
· We had some of the fewest COVID closures in the state.
One of the more interesting ways to look at data is to use machine learning to sort your data into clusters. The particular tool I used for this is called K-Means Clustering, a popular algorithm in the field of data science for its simplicity and efficiency. But what exactly is K-Means Clustering? At its core, K-Means is a method that aims to partition a dataset into distinct groups (clusters) such that the data points in each group are as similar to each other as possible, while also being as different as possible from the points in other groups.
Why is this approach useful? We often deal with large amounts of data that can seem impenetrable at first glance. By organizing this data into clusters, we can identify patterns and characteristics that are not obvious to us at first. For instance, when we analyze schools across various districts, K-Means Clustering can reveal groupings of schools with similar challenges or successes, helping us to tailor support and resources more effectively.
For this analysis, I wanted to look closer at Overall Success Rate, Economically Disadvantaged Percentage, and Black/Hispanic/Native American percentage using the same dataset I used for my initial Letter Grades report. I reduced the dataset to those three features, and I had to convert the values in the Success Rate column from <5% to 2.5 and >95% to 97.5 and the convert those values to floats.
Here are the basic descriptive statistics.
Descriptive Statistics
Here is how the data is distributed.
Histograms of each feature
The Success Rate histogram shows a unimodal distribution centered around 30-40%. The distribution is slightly skewed to the right, indicating that while most schools have a success rate in the middle range, there are fewer schools with very high success rates.
The distribution of the percentage of economically disadvantaged students is also unimodal and seems to be slightly skewed to the right. Most schools have between 20% to 40% economically disadvantaged students, with fewer schools having very high or very low percentages.
The BHN histogram is different from the other two, showing a bimodal distribution. One peak is around the 0-10% range, and another, more pronounced peak, is at the 90-100% range. This suggests that schools tend to have either a very low or very high percentage of Black, Hispanic, or Native American students, with fewer schools having a moderate percentage. This histogram supports Kozol’s research that American schools are still segregated.
Next, I wanted to see how each of these values correlated. I did scatterplots and ran a Pearson’s r to see the relationships between the data.
Scatterplots and Pearson’s r correlation coefficients
No surprise, but the data shows the following:
Success Rate vs. Economically Disadvantaged: The correlation coefficient is -0.72, indicating a strong negative correlation. This means that as the percentage of economically disadvantaged students increases, the overall success rate tends to decrease.
Success Rate vs. BHN: The correlation coefficient is -0.56, suggesting a moderate negative correlation. So, higher percentages of BHN students are associated with lower overall success rates.
Economically Disadvantaged vs. BHN: The correlation coefficient is 0.61, showing a strong positive correlation. This implies that higher percentages of economically disadvantaged students are often found in schools with higher percentages of BHN students.
The Clustering Model
Before running the model, I scaled the data using the standard scaler. This is crucial for K-Means Clustering. Here is an article about that if you want to read it. And of course, I ran an elbow plot to find the optimal number of clusters.
The elbow plot
The elbow plot settled on five clusters. After fitting the model and running it for 5 clusters, I generated a 3D Scatterplot of the 5 clusters just to have a visual of the differences. The red star represents the centroid of the cluster.
3D Scatterplot
The clusters that it generated can be described as follows:
Cluster 0 (286 schools) has a relatively low overall success rate of about 25%, a moderate percentage of economically disadvantaged students (around 38%), and a very high percentage of Black, Hispanic, or Native American students (approximately 70%).
Cluster 1 (187 schools) is characterized by a high overall success rate of around 71%, a low percentage of economically disadvantaged students (about 8%), and a lower percentage of Black, Hispanic, or Native American students (roughly 18%).
Cluster 2 (490 schools) features a low-to-moderate overall success rate of about 33%, a moderate percentage of economically disadvantaged students (also around 38%), but a lower percentage of Black, Hispanic, or Native American students (about 12.5%).
Cluster 3 (231 schools) has the lowest overall success rate of approximately 15%, the highest percentage of economically disadvantaged students (around 66%), and a very high percentage of Black, Hispanic, or Native American students (nearly 95%).
Cluster 4 (476 schools) shows a moderate overall success rate of around 45%, with a lower percentage of economically disadvantaged students (about 21%) and a percentage of Black, Hispanic, or Native American students similar to the previous value (around 21%).
Here is a bar chart showing the Cluster Profiles.
A bar chart of each cluster
And to illustrate how many schools are represented in each cluster, here is a humble pie-chart.
Conclusions
Diversity in School Profiles: The clusters represent a wide range of school profiles, from those with high success rates and low percentages of economically disadvantaged and minority students (Cluster 1) to those facing significant challenges with high percentages of disadvantaged and minority students and low success rates (Cluster 3).
Economic Disadvantage and Success Rates: There appears to be a correlation between economic disadvantage and overall success rates, as seen in the negative correlation coefficients and the cluster characteristics. Schools with a higher percentage of economically disadvantaged students tend to have lower overall success rates (Cluster 0 and Cluster 3).
Racial and Economic Segregation: The bimodal distribution of the percentage of Black, Hispanic, and Native American students indicates potential racial and economic segregation within the school system. Some schools have very high percentages of minority students, while others have very low percentages, with fewer schools in between.
Most Schools Do Not Have a High Success Rates: Cluster 4 and Cluster 1 schools have high success rates. Typically, 45% is the bar schools want to reach because that represents maximum points in the federal accountability model for success rate. These only represent 39.6% of all schools. This means that 60.4% of all schools are falling below that mark.
Cluster 4 stands out
Cluster 4 stands out as a cluster with some diversity and high success rate. The means for Economically Disadvantaged (20.99) and BHN (21.24) are still much lower than the overall means for those categories.
What do you see in this data?
I have spent the past year and change exploring the possibilities, limitations, and risks of Large-Language-Model AI, especially ChatGPT. In my role at work, I haven’t done a lot on it because we don’t have an adopted Board Policy on it, and TSBA hasn’t written a model policy for boards. This leaves us in a weird space where we know that this is out there and people are using it, but we don’t have any guidance or governance for it. I don’t think ignoring it for now is the answer, and I wanted to share what I have communicated so far so that it might be helpful to other districts.
Handbook Policy
You don’t need a board policy to have a handbook policy, so we put this in our high school handbooks at the beginning of the year:
AI Handbook Entry for Academic Integrity and Honesty in the Use of Large Language Models
· Purpose
This handbook entry aims to ensure the upholding of academic integrity and honesty in the context of the use of large language models (LLMs) such as ChatGPT in our school environment.
· Scope
This handbook entry covers all students, staff, and any other individuals who interact with our school's academic programs and services, and who use LLMs for academic purposes.
· Handbook entry Guidelines
Proper Citation: Students must properly acknowledge and cite the use of LLMs in their work. Any idea, phrase, or output generated by AI must be cited just as any other source would be.
Original Work: While LLMs can be used for assistance and guidance, the work submitted by students must fundamentally be their own. The use of AI should be to facilitate and enhance the learning process, not to replace individual effort and creativity.
Collaboration: While working collaboratively, students must clearly state the contributions made by AI. Collective work should reflect a clear understanding of the contributions made by each student and the AI model used.
Access: All students should have equitable access to AI tools to ensure fairness. The school will strive to provide the necessary resources and training for all students.
Educator Guidelines: Teachers should educate students about the ethical use of AI and its potential impacts on academic integrity. They should also receive regular training to stay updated on the capabilities and limitations of AI.
· Implementation and Compliance
This handbook entry should be communicated effectively to all relevant parties. The school will conduct regular checks to ensure compliance. Any violation of this handbook entry will be considered a breach of academic integrity, and the school's standard disciplinary measures will be applied.
Simply enough, we have let students know ahead of time that students can’t use LLMs to produce final products. We’ve also let teachers know that they need to be teaching students how to use LLMs to their advantage.
Where are we now?
We haven’t, to my knowledge, had any issues with students getting caught cheating with LLMs, but that doesn’t mean it hasn’t happened. In fact, the whole inspiration for me writing this is that a student told me that she wouldn’t use AI to help her study French because another student had submitted an essay in her English class using AI and she got the same grade as him and it made her angry.
Because of that conversation, I put together a document for teachers, and I thought I’d share that content here.
So why not just avoid AI for as long as we can?
· You can tell when AI has written something, and I’m surprised when anyone can’t. Have you used AI enough to pick up its tone and patterns? It uses too many adverbs. In emails, it always says some affectation like “I hope this email finds you well.”
· AI isn’t going anywhere. As a matter of fact, it’s the worst quality and the least integrated today than it will ever be in our students’ lives. We have to learn to live with it, and students are going to need to know how to interact with AI now. It can really give them a huge advantage in life if used ethically and responsibly.
· Withholding the power of any technology from our students only withholds it from certain students. Typically, only the students who are disadvantaged will not learn to use technology when it is withheld from them in school.
· We can’t have students using this technology to cheat, and avoiding teaching them how to use it responsibly will not prevent them from cheating. In fact, letting students know that we are very knowledgeable about it will make them think twice about using it to cheat.
So how should students be learning to use AI?
· Helping them get organized.
· Asking it simple questions and interacting with it. For example, this student is having trouble with conversational French. It can have a conversation with her, and she can practice her French with it. You can’t get that anywhere else without a pen pal or French friend.
· Asking it to make a study guide.
· Asking it to quiz you on something.
· Asking it to help you with the pre-writing phase of writing.
· Asking it to proofread your paper (that you wrote) and give you feedback on it. You could even ask it to evaluate the paper with a rubric that the teacher gave them.
· Asking it to explain difficult concepts in simple ways.
· And many other ways…
Here are some samples:
Example: Helping them get organized.
Sample Prompt: We’re learning about cellular energy in my high school biology class in Tennessee. Can you help me get organized with an outline? I will keep you posted on what we’re studying in class so you can help me make a study guide.
Example: Asking it to quiz them on something.
Sample Prompt: We’re studying slope in Algebra I in Tennessee, can you give me some quiz questions and tell me how I did?
Example: Asking it to help with the pre-writing phase of writing.
Sample Prompt: I am writing a research paper on Romeo and Juliet and comparing it to other famous family feuds in more recent history. We’re going to the library to do research next week, and I need to get organized. Can you give me a checklist of what I should be searching? Do you know of any feuds I can research?
Example: Asking it to help with brainstorming
Sample Prompt: In US History, our teacher has asked us to explore the causes of war leading up to World War I. We are supposed to represent a country and their point of view. Help us brainstorm some ideas for this. We can’t choose Germany, Britain, France, or the US. We don’t know these other countries as well. What information do you need to help us with this?
Example: Ask it to proofread your paper and give you feedback.
Sample Prompt: I’m writing a paper for my World History class on the Ming Dynasty, but I need someone to proofread it for me. Can you proofread this and give me a list of suggestions for improving it. Please do not rewrite the paper for me; I do not want to get accused of cheating.
How do I stop cheating?
· Consider whether your assignments are easy for students to cheat on using AI.
· Get experienced enough with AI that you can spot how it writes.
· Take a writing sample at the beginning of the year for a comparison.
· Let students know that you won’t tolerate them using AI for final products, but you’d love for them to use it for brainstorming, outlining, and pre-writing.
I’d love to have a deeper conversation about this, but I want to be clear that we must tackle this issue head-on, and at some point, we’re all going to have to accept that AI is a technology tool that our students need to know how to use. Just like we teach students to use TI-85 calculators, nail guns, MIG and TIG welders, and 3D printers, we have to expose students to all technologies that will help them be successful in life.
There are many AI tools other than ChatGPT that are meant specifically for the classroom. I’m trying to keep a list of them: https://www.jasonhorne.org/ai-tools
I decided to build a TISA Dashboard to keep track of funding coming in the next year. I wanted to also keep track of numbers that inform the dashboard in order to spot potential data-entry errors.
Click here to access the dashboard.
Here is my video presentation.
