How I Monitor 38 Education Bills While I Sleep

On March 30, a Senate amendment quietly changed the virtual school closure bill to shift authority from the state to individual LEAs. It wasn't in the bill summary. It wasn't in the newsletter from our advocacy group. It was buried in SA0611, one of 51 amendments I was tracking across 38 bills.

I caught it because I built a system that catches things like that.

The Problem Every Small District Has

Large districts have government relations staff. State associations put out weekly summaries, but they're broad, they lag by a few days, and they don't tell you what a bill means for *your* district. When you're an assistant director at a 2,800-student district, legislative monitoring is something you do between budget meetings and bus route complaints.

During the 2025-2026 Tennessee General Assembly session, I started with the same approach everyone uses: skimming the TSBA updates, reading EdTrust emails, occasionally pulling up a bill on the legislature's website. It worked fine until it didn't. Bills were moving fast. Amendments were changing the substance of what I thought I understood. And I realized I was spending hours each week just trying to stay current, let alone actually analyzing what any of it meant for us.

Building the Tracker

In early March, I built a Claude Code skill that scrapes the Tennessee General Assembly website, pulls status, votes, amendments, fiscal notes, and sponsors for every bill I'm tracking, then generates a color-coded Excel workbook with six sheets: the main tracker, category breakdown, timeline, full details, amendments, and vote analysis. It rates each bill's impact on our district, flags close votes, and highlights fiscal notes.

The first run pulled 32 bills from a State Affairs Pro report. By April, we were at 38 bills and 53 amendments.

The system runs in parallel, eight bills at a time, fetching directly from wapp.capitol.tn.gov. A full update takes a few minutes. I scheduled automated runs for heavy hearing days using launchd, so updates just showed up in my inbox after evening floor votes.

Screenshot of tracker spreadsheet

Where It Actually Mattered

The tracker isn't interesting because it's automated. It's interesting because it changed what I could do with the information.

When HB 2485 (expanding the TISA economically disadvantaged definition, $83.7M fiscal note) came up for subcommittee, I already had the data I needed. I pulled our enrollment numbers, the gap between our CEP methodology and the TISA classification, and the roughly $800,000 annual funding impact, then sent tailored emails to our representative and senator the same day the alert came in. The tracker had already done the homework. I just had to write the email.

When HB 2393 (K-5 digital device restrictions) passed the House 87-6, I knew immediately that the original prohibition had been softened by amendment SA0607 into a policy-based approach with eight permitted use categories. That distinction matters for how we plan our implementation. Without amendment tracking, I would have read "digital device bill passes" and assumed the worst.

When HB 0793 (immigration enrollment verification) accumulated nine amendments across both chambers, the tracker flagged each one and assessed whether it changed the compliance burden. The headline bill and the bill-as-amended were two very different things.

The Evolution

This is the part I think matters most for anyone considering something similar. The tool didn't start as what it is now. It started as a spreadsheet generator. Then I realized amendments were changing bills substantively, so I added amendment tracking. Then I caught a discrepancy between what the tracker reported and what was actually on the legislature's website, so I built a fact-check mode that runs three parallel verification agents across all 38 bills. Then I got tired of the plain-text email summaries, so I added an HTML email generator with color-coded status badges and hearing schedules that I can forward directly to my leadership team.

Each addition came from hitting a real wall, not from planning features in advance.

What This Means for Districts Like Mine

I open-sourced the skill on GitHub with the PII scrubbed. It's built for Tennessee's legislature website, but the architecture is the same for any state: fetch bill status from the official source, track amendments, categorize by district impact, generate reports.

The point isn't that every district administrator should learn to build Claude Code skills. The point is that the gap between what large districts can monitor and what small districts can monitor just got a lot smaller. The tools exist. The data is public. The bottleneck was always the human time to pull it all together, and that bottleneck is gone.

Three bills from my tracker are now law. Three more are on the governor's desk. The ESA expansion bill, at $150 million the biggest fiscal item on the tracker, goes to full Finance Committee on Monday. I'll know what happened before I finish my coffee Tuesday morning.

That's not a lobbyist. That's a laptop and a well-built prompt.

Predicting Achievement Without Test Scores

I Can Predict Your School's Achievement Without Looking at a Single Test Score

A machine learning analysis of roughly 1,700 Tennessee public schools across two years, comparing what letter grades tell us versus what they hide.

Tennessee gives every public school a letter grade. A through F, just like report cards. The state calculates it from a formula that weighs achievement scores, growth, chronic absenteeism, English learner progress, and for high schools, graduation rates and college/career readiness.

The formula is public. If you know a school's test scores, you can basically calculate the grade yourself. Which raises a question I've been chewing on: what if you strip out all the test-based inputs and just look at the structural stuff, the demographics, staffing, funding, discipline rates, the conditions a school operates under? How much can you predict?

The answer surprised me.

The Experiment

I pulled every publicly available dataset from the Tennessee Department of Education for the 2022-23 and 2023-24 school years: letter grades, school profiles, chronic absenteeism, discipline, educator experience, teacher retention, staffing ratios, per-pupil expenditures, funding sources, graduation rates, and dropout rates. Merged them all at the school level. About 1,690 eligible schools per year, observed across both years for 3,381 school-year observations.

Then I deliberately removed every variable that directly feeds Tennessee's letter grade formula. No achievement scores, no growth scores, no success rates, no CCR rates. What remained were 33 contextual features: things like percent economically disadvantaged, chronic absenteeism, teacher retention, per-pupil spending, and demographic composition.

I ran the analysis two ways. First, I tried to classify the letter grade itself (A through F). Then I switched the target to overall success rate, the continuous achievement percentage that drives the letter grade. Same features, different targets. The comparison is telling.

Round 1: Predicting the Letter Grade

Five models. Random Forest, XGBoost, Gradient Boosting, Logistic Regression, and an Ordinal Logistic model that respects the A > B > C > D > F ordering. Best accuracy across the board: about 40%.

Model	Accuracy	CV Accuracy	Mean Absolute Error
Logistic Regression	41.8%	39.9%	0.73 grades
Ordinal Logistic	41.4%	40.1%	0.74 grades
Random Forest	40.2%	40.3%	0.75 grades
XGBoost	34.4%	40.6%	0.83 grades
Gradient Boosting	37.1%	39.6%	0.79 grades

40% accuracy across five categories is better than random (20%), but not great. The models were off by about 0.75 letter grades on average. If a school is a C, the model might guess B or D. Close, but noisy.

The letter grade bins are doing real damage here. A school with a 49% success rate and a school with a 51% success rate might land in different grade buckets, but structurally they're nearly identical. The model sees the same features and reasonably groups them together, but the grading system draws an arbitrary line between them.

Round 2: Predicting Achievement Directly

Same 33 contextual features. Same schools. But instead of predicting A/B/C/D/F, I targeted the overall success rate, a continuous percentage from 5% to 95%.

R-squared comparison across models. Gradient Boosting and XGBoost both explain over 81% of variance in achievement.

Model	R-squared	Mean Absolute Error	CV R-squared
XGBoost (Tuned)	0.823	5.5 pct pts	—
Gradient Boosting	0.816	5.6 pct pts	0.819
XGBoost	0.815	5.7 pct pts	0.822
Random Forest	0.759	6.4 pct pts	0.783
Ridge Regression	0.698	7.2 pct pts	0.663
Linear Regression	0.698	7.2 pct pts	0.615
Lasso	0.689	7.3 pct pts	0.661

R² = 0.82 Contextual features alone explain 82% of the variance in school achievement. No test scores needed.

±5.5 pts The tuned model predicts a school's success rate within 5.5 percentage points on average.

That is a massive jump. The same features that could only guess a letter grade 40% of the time can explain 82% of the variance in achievement when you let the model see the actual number instead of a bucketed label.

Actual vs. predicted achievement. Points cluster around the diagonal, with an MAE of about 5.5 percentage points.

What Drives Achievement

SHAP (SHapley Additive exPlanations) tells us not just which features matter, but how much they move the needle and in which direction. The units here are percentage points of achievement.

Feature importance measured by mean absolute SHAP value. Two features dominate everything else.

Two features tower over the rest:

• Economically disadvantaged percentage: 5.3 points of influence on average. Higher poverty, lower achievement.
• Chronic absenteeism: 4.7 points of influence. More absent students, lower achievement.

After those two, a cluster of second-tier features emerges: local funding percentage (positive), demographic composition, experienced teachers (positive), teacher retention (positive), and discipline rates (negative). Each of these contributes roughly 0.6 to 1.3 percentage points.

SHAP beeswarm plot. Each dot is one school. Red means high feature value, blue means low. Dots pushed right increase the predicted success rate, dots pushed left decrease it.

Look at that SHAP summary. High economically disadvantaged percentage (red dots) consistently pushes predictions left (lower achievement). High chronic absenteeism does the same. High local funding and experienced teacher percentages push right (higher achievement). The patterns are clear and consistent.

Why the Comparison Matters

The letter grade classification flopped not because the features lack signal, but because the grading system collapses a continuous reality into five bins. A school at the 49th percentile and a school at the 51st percentile might be structurally identical, but one gets a C and the other a B. The model can't distinguish them because there's nothing structurally distinguishing to find.

When you let the model predict the actual achievement percentage, it stops fighting artificial boundaries and starts learning the real relationship between conditions and outcomes. The same data that produced a mediocre 40% classifier produces an R-squared of 0.82 when you ask the right question.

This is a data science lesson wrapped in education policy. If your outcome variable is discretized from something continuous, you're throwing away information. The letter grade system takes a rich, nuanced distribution of achievement and flattens it into a handful of buckets.

Left: the actual distribution of achievement across Tennessee schools. Right: the same data, grouped by letter grade. Notice the overlap, especially between B, C, and D schools.

A Case Study: Greeneville City Schools

I work for Greeneville City Schools, so I ran our numbers through the same lens. The model says poverty and absenteeism explain 82% of achievement. GCS has a district-wide economically disadvantaged rate around 29%, which puts us in the middle of the pack. Based on structural factors alone, the model would predict us to land somewhere around the state average.

We don't.

+8.1 pts In 2023-24, GCS scored 8.1 percentage points above the expected achievement for districts with our demographic profile, nearly double the 4.2-point gap from the year before.

15th of 98 Among districts with similar ED populations, GCS ranked 15th in achievement in 2023-24, up from 26th the year prior.

In 2023-24, four of our seven schools earned A grades. Here's every GCS school, year over year:

School	ED %	2022-23	2023-24	Change
Eastview Elementary	18%	56.9% (A)	61.2% (A)	+4.3 pts
Tusculum View Elementary	27%	41.8% (B)	50.0% (A)	+8.2 pts
Greeneville High School	24%	50.0% (A)	48.4% (A)	-1.6 pts
Greeneville Middle School	24%	44.9% (B)	47.8% (A)	+2.9 pts
Hal Henard Elementary	36%	49.7% (B)	48.3% (C)	-1.4 pts
Highland Elementary	54%	32.6% (C)	36.5% (C)	+3.9 pts
TOPS Greeneville	17%	29.1% (D)	37.5% (C)	+8.4 pts

Five of seven schools improved, several significantly. Tusculum View jumped from a B to an A with an 8.2-point gain. TOPS Greeneville climbed 8.4 points and moved from a D to a C. Even Highland Elementary, our highest-poverty school at 54% ED, scored 36.5%, well above the 24% state average for schools in that ED range. Highland ranks 13th out of 131 schools with similar poverty levels statewide.

The model says schools like ours should perform at a certain level given our demographics. We keep outperforming that prediction, and the gap is widening. That's not an accident. That's what happens when experienced teachers stay (we have strong retention), absenteeism is managed, and the district invests in the things that actually move the needle.

What This Means for Districts

If you run a school district in Tennessee, here is what 1,700 schools, two years of data, and seven models are telling you:

• Your letter grade is 82% predictable from factors that have nothing to do with how well you teach. Poverty and absenteeism alone account for most of the variance.
• The two highest-leverage things a district can invest in are reducing chronic absenteeism and supporting economically disadvantaged students. Everything else is a rounding error by comparison.
• Teacher experience and retention matter, but they're second-tier effects. A school with great teachers in a high-poverty, high-absenteeism context will still struggle on paper.
• Spending more money per pupil, counterintuitively, correlates negatively with achievement. This isn't because money hurts. It's because Title I funding flows to the schools that need it most, and need is correlated with the same factors that drag down scores.

None of this is new to anyone who runs schools. We all know poverty predicts outcomes. But there's a difference between knowing it and seeing a machine learning model explain 82% of the variance with nothing but contextual features. It puts a precise number on something we've felt in our bones for years.

The uncomfortable implication: Tennessee's letter grade system is, to a large degree, grading the ZIP code. A school's structural context is doing most of the talking, and the letter grade is mostly just a noisy echo of it. But districts like Greeneville show it doesn't have to be destiny. The 18% of variance the model can't explain? That's where the work happens.

Methodology Notes

Data: Tennessee Department of Education public data downloads for 2022-23 and 2023-24. All school-level. Schools flagged as ineligible for letter grades were excluded. Approximately 1,690 unique schools observed across both years, yielding 3,381 school-year observations (3,345 with valid achievement data).

Features: 33 contextual variables across demographics, teacher quality, discipline, absenteeism, finance, staffing, graduation, and dropout. All formula-input features (achievement scores, growth scores, success rates, CCR rates) were deliberately excluded.

Models: Seven regression models (Linear, Ridge, Lasso, ElasticNet, Random Forest, Gradient Boosting, XGBoost) plus hyperparameter tuning via RandomizedSearchCV. Five classification models for the letter grade comparison. 80/20 train/test split, stratified. 5-fold cross-validation on training sets.

SHAP values computed via TreeExplainer on the XGBoost regression model. All code available on request.

My greatest concern for AI in education: the invisible handshake

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.