Reading the Discipline and Achievement Data Before the Rollout

The request asks me to confirm a decision that has not been made. I would separate description from endorsement. By Monday I can produce a factual one-pager, and I would attach the questions the data cannot yet answer.

Analyses needed before attributing the rise to student behavior:

• Referrals per 100 students by month, to see whether 18% is a real increase or a change in reporting practice.
• Disaggregation by race, grade, teacher, time, location, and category. The 58% figure means something only against the 34% enrollment base rate - compute a risk ratio, and check whether a small number of staff account for a large share of referrals.
• The referral-to-consequence pathway: are similar behaviors handled differently by different teachers?
• Sixth grade: does the 11:00-12:30 cluster map onto lunch and recess transitions and supervision coverage?
• Algebra 1: are failures concentrated in one teacher, or in students who entered without prerequisite mastery? Check prior scores, attendance, and schedule.
• Reading: 41% versus 19% suggests a transition or instructional issue rather than a bad cohort; check feeder patterns and intervention fidelity.

The summary should say plainly: the data show disparities; they do not yet show whether the cause is behavior, adult referral practice, supervision, or instruction, and zero tolerance addresses none of those.

Recommendations mapped to the data:

• Discipline: replace zero tolerance with a tiered response. Define minor versus major behaviors with staff input, require documentation, and use supervised restorative conferencing as an alternative to a first suspension. Goal: 25% reduction in disrespect/defiance referrals and a Black-student risk ratio moving toward 1.0.
• Sixth grade: restructure the 11:00-12:30 block - stagger transitions, add active supervision, and run a Tier 2 group for the highest-referral students. Teach expectations in advisory rather than at an assembly.
• Algebra 1: examine placement and prerequisite data, add a co-taught or double-block section, and provide weekly progress monitoring with targeted tutoring for students at D or F by the first progress report.
• Reading: verify intervention fidelity, add Tier 3 intervention for the lowest 15%, and schedule a family literacy event.

Measurement: monthly referral rate per 100 students, risk ratio by race, course failure counts at each grading period, and screener movement from fall to winter.

Sequence: staff input first, then training, then implementation with a six-week review. People support what they help build, and a one-day assembly cannot change a transition-time pattern that recurs five days a week.

Interpretive errors to flag:

• Percentages without base rates. 58% of referrals is not comparable to 34% enrollment; risk ratios and per-100 rates are.
• Small numbers. One team or one teacher can drive a monthly spike; do not build policy on 12 referrals.
• Reporting artifact. A new referral form or a new assistant principal can raise counts without raising behavior.
• Aggregation hiding subgroups. School-wide averages can mask ELL or disability patterns.
• Correlation treated as cause. Algebra failures among students with prior C grades point to prerequisite gaps, not effort.
• Pre/post with no comparison. Improvement after an assembly may simply be seasonal.

Communicating without blame: present data at the department level, not the individual teacher level, unless the pattern is unmistakable and then handle it privately with the administrator. Open the meeting with the shared goal, show the pattern, ask what is happening at 11:30 that we could change, and let staff generate solutions. Blame produces defensiveness; data framed as a mirror for the system produce ownership.