Program Overview

Structure, arc, risks, and what every facilitator must know before Day 1
Driving Question
"How can you use data, equations, and code to build a robot athlete that wins on math, not luck?"
Program Format
4
Weeks
16
Days
4 hrs
Per Day
~3
Students / Team

Platform: BBC micro:bit v2 + Cutebot Pro (MakeCode). Team roles: Coder, Fabricator, Analyst — rotate weekly. Daily structure: Hook (20–25 min) → Challenge Block (45–70 min) → Snack / Lunch (20 min at Crosstown 8:00–12:00; 60 min at NYC sites 10:00–3:00) → Match-Build-Make (75–90 min) → Open Lab (20–40 min) → ENDS Closing Ritual (10 min).

If You're Overwhelmed, Protect These Five Things
Minimum Viable Program
  • 1Prediction before each match
  • 2Gap sentence after each match
  • 3Loss recovery after every loss
  • 4Students explain the math — not the facilitator
  • 5ENDS closure at the end of each day

If these happen consistently, the program still works. Everything else strengthens the experience — these are the floor.

Four-Week Arc
WeekDaysTheme + Math FocusCompetitive Structure
Week 1 1–4 Play Lab + Boot Camp Proportional reasoning (Speed), rate of change and linear decay (Endurance), coordinate geometry (Turning), proportional vs. non-proportional contrast — Speed linear vs. Power quadratic Curious, safe, slightly chaotic. Lean into the chaos. No scoring. Bot naming ceremony Day 2. Simulation practice only. Build identity and trust before competition.
Week 2 5–8 The Build + Season 1 Full budget equation (20-point constraint), slope-intercept form from Endurance decay, scatter plots, gap sentences, percent change vs. percentage point difference Competitive, invested, occasionally frustrated. The leaderboard is live. Leaderboard live Day 7. Round-robin matches. Loss recovery active from Day 7. Rivalry Cards on Day 8.
Week 3 9–12 Commissioner's Week + Season 2 Proportional scaling (linear vs. quadratic), systems of inequalities, distance formula deepened, linear equations from match data, Commissioner proposals requiring causal reasoning Empowered, analytical, creative. Students run the rules committee. Underdog mechanic Day 11. Commissioner's Meeting Day 12 — students vote to change the rules. Season 2 begins under new rules.
Week 4 13–16 Season 2 + Exhibition Residuals and model error, percent change across seasons, slope-intercept from match data, pre-match pitches to judges Proud, expert, generous. Students teach others. Season 2 final matches Days 13–15. Exhibition Day 16. Best Teacher Award for clearest math explanation, not highest score.
On Confusion

Confusion is useful only when students are still moving, trying, writing, testing, or discussing. If students freeze, withdraw, or wait passively, intervene.

What Makes This Program Hard

This program is not hard because the math is hard. It is hard because students are building, competing, losing, recovering, and explaining in public.

Core Facilitation Commitments

These commitments protect the program's learning sequence. When any of them slips consistently, student thinking degrades faster than most facilitators expect.

If You're Running Behind

Cut extended debrief first. Do not cut prediction, match logging, loss recovery, or closure.

1
7-second wait time after questions Strongly Recommended

Seven seconds activates thinking across all confidence levels. Under 3 seconds, only high-confidence students respond. Protect this whenever possible. If a student is genuinely lost rather than processing, respond to them directly.

2
Run the 3-step loss recovery script on every losing team, every match day Must Have

Default expectation: every losing team completes all three steps before their next match begins. This is the single highest-dropout-risk moment in the program. If time collapses, preserve the core step — Step 2 (locate the decision in the data).

3
No blanks in the data dashboard Must Have

Under normal conditions, every match gets a prediction logged before it starts and an actual logged immediately after. A prediction entered after seeing the result is worthless as data. Circulate and verify during every post-match window.

4
Redirect correctness questions — don't answer them Strongly Recommended

"Did we do this right?" → "What would a correct answer look like? Is that what you see?" The match result is the teacher. Your job is to keep students using their own data, not to validate in real time.

5
Commissioner's Meeting: format compliance only, not proposal improvement Strongly Recommended

Before the meeting, confirm proposals include two data points and name one math concept. During the meeting, redirect accuracy challenges to the room. Weak proposals go to the floor — the debate is the learning.

6
Sentence starters posted and visible all four weeks Nice to Have

Post the four categories before Day 1 and leave them up. Students who struggle to start a gap sentence need the scaffold visible throughout, not just mentioned once at the beginning of the program.

7
ENDS ritual runs every day Must Have

Protect ENDS daily. It is not a wind-down — it is the day's consolidation moment. Each type targets a different reflection goal. If time collapses, run a 3-minute version rather than skipping it entirely.

8
Improvise within the principles — don't improvise the principles away Strongly Recommended

You will make judgment calls every day. Make them in the spirit of the framework: keep thinking in students' hands, use data as the authority, treat confusion as information. The tools are yours to use flexibly — the principles are not.

Three Highest-Risk Facilitation Moments
Risk 1 — Day 1 (and Day 15)
Math Shame Re-Triggering on the Diagnostic
The pre-task diagnostic is the moment most likely to re-trigger math shame before trust is established. Students who struggle with math see a question sheet and immediately associate it with prior failure. The identical risk recurs on Day 15 with the post-task diagnostic.
Mitigation

Frame the diagnostic before distributing it using the exact script in the Day 1 risk box. Use non-bot contexts (speed, distance, ordered pairs) so no student feels disadvantaged by not yet knowing the stat system. On Day 15, use the "comparing you to yourself" frame explicitly.

Risk 2 — Every Match Day (Days 7–10, 13–15)
First Real Loss Without a Structured Recovery
The first Season 1 loss is the single highest dropout-risk moment in the program. Facilitators who improvise emotional responses cut losing teams off from their data and from their next iteration. This risk repeats on every match day and peaks at the Championship.
Mitigation

Memorize the 3-step script before Day 1. Step 1: acknowledge the emotion (genuinely, briefly). Step 2: locate the decision point in the data. Step 3: restore student ownership with a hypothesis.

Risk 3 — Day 12 (Commissioner's Meeting)
Facilitator Intervening
The Commissioner's Meeting and the pre-match pitch are the two moments of maximum student ownership. Facilitators who correct proposals mid-presentation or suggest data to use undermine it.
Mitigation

Your only intervention during the Commissioner's Meeting should be redirecting accuracy checks to the room: "Does this proposal use the data correctly? Can anyone verify the math?" Clarifying how the budget formula works is fine. Improving what proposals say is not.

Motivation Structures

These are not decorations. They create multiple ways to matter besides winning.

Week 1, Day 2 · End of session
Bot Naming
The program's first permanent artifact. Bot names and origin stories are posted on the wall for all four weeks. Exhibition visitors read origin stories on Day 16, creating a narrative through-line from first day to last.
Week 2, Day 7 · First match day
Leaderboard
Updated after every match day. The Best Prediction column rewards mathematical accuracy, not just winning. A last-place team can lead the Best Prediction column — preventing early disengagement from teams that fall behind on wins.
Week 2, Day 8
Rivalry Cards
Data-backed trash talk. Each card must cite a specific stat or data point. This creates a social incentive to read and verify the opponent's dashboard data and do the math accurately before making a public claim.
Week 3, Day 11 · Public announcement
Underdog Mechanic
The last-place team gets 2 extra points to allocate before Season 2. Prevents the most common failure mode in competition-as-motivation programs: early losers who stop engaging because winning feels impossible.
Week 4, Day 16 · Awards ceremony
Best Teacher Award
Exhibition visitors vote for the student who explained the math most clearly — not the team that won. The program's closing statement: what matters is making mathematical thinking clear to someone who'd never heard it before.
Week 3 and 4 · Open Labs
Skills Arena
A dedicated side field for focused programming challenges. Teams attempt short missions, log predictions and actuals, earn visible Skill Badges. A parallel track for teams momentarily disengaged from the main competition.
Pre-Day 1 Preparation Checklist

Ten specific things to know, make, or practice before Day 1. Each represents a predictable new-facilitator failure when absent.

#ItemWhy It Matters
1
Calculate all four stat formulas for every point value 1–8 in under 10 seconds each
You cannot spot an allocation error in real time if you cannot produce the answer faster than a student can.
2
Memorize the 3-step loss recovery script word-for-word and practice it aloud 5 times
You must deliver this automatically under emotional pressure — not recall it.
3
Setup and program a BBC micro:bit + Cutebot Pro bot from scratch and debug at least two MakeCode errors
You cannot facilitate Match-Build-Make without hands-on hardware experience.
4
Set up all the BBC micro:bit + Cutebot Pro bots and train them to recognize the black line. Install MakeCode.
Robots and laptops must be fully working before students arrive on Day 1.
5
Pre-fill one complete sample data dashboard with fake Season 1 data, including 2 mismatches >20%
You only spot a blank prediction entry or calculation error if you know what a correct dashboard looks like.
6
Physically rehearse all three improv activities required for Days 1–2
Students take their energy cue from your willingness to look foolish first — model these, don't describe them.
7
Practice three redirection responses until each is a reflex
Redirecting under pressure requires an automatic response, not recalled language.
8
Score two sample Commissioner proposals against the rubric before Day 11
You can only redirect accuracy checks to the room if you can quickly assess whether a cited data point is correct.
9
Set up the physical room: four stat-labeled corners, driving question visible from every seat
If the driving question is not visible from every seat, it does not function as a driving question.
10
Explain, without notes, why each stat is introduced on its specific day
Understanding the learning sequence prevents rushing the scaffold — all four stats on Day 1 would collapse it.
Sentence Starters — Post All Four Weeks

Post before Day 1. Leave them up. Students who cannot start a gap sentence need the scaffold visible, not just mentioned once.

Making a Claim from Data
"My data shows _____ because _____."
"When I changed _____ from _____ to _____, the result went from _____ to _____."
"The pattern I notice is _____. I know this because _____."
Describing a Calculation
"The percent change from _____ to _____ is _____% because I calculated (_____ − _____) ÷ _____ × 100."
"I used the formula _____ and plugged in _____ for _____, which gave me _____."
"My answer is _____. I got this by first _____, then _____, then _____."
Comparing Two Things
"_____ and _____ are different because _____. The numbers that show this are _____ and _____."
"Both _____ and _____ have _____ in common, but they differ in _____."
"_____ is better for _____ because _____, but _____ is better for _____ because _____."
Explaining a Mismatch
"The formula predicted _____, but the actual result was _____. The gap is _____ because _____."
"I expected _____ because _____, but instead I got _____. One reason could be _____."
Appendix: Design Rationale

This section explains why specific design choices were made. Not required reading before Day 1.

Why loss recovery is scripted, not improvised

Improvised emotional responses during losses tend to cut students off from their data — either by rushing past the emotion, or by dwelling in it without redirecting to action. The 3-step script creates a reliable container: acknowledge the emotion briefly, locate a specific decision in the data, restore student ownership with a hypothesis. Facilitators who internalize this structure handle the highest-stress moments with consistent quality.

Why wait time is 7 seconds, not "just wait longer"

Research on classroom wait time consistently shows that under 3 seconds, only high-confidence students respond. Seven seconds is a specific, noticeable target — long enough to feel uncomfortable the first time, which signals to facilitators that they are doing it correctly. "Just wait" produces 2–3 seconds in practice.

Why the four stats are introduced in sequence over four days

Each stat introduces a new mathematical relationship: Speed (linear proportion), Endurance (rate of change and decay), Turning (coordinate geometry), Power (non-linear scaling). Introducing all four on Day 1 eliminates the learning sequence — students would have no lived experience of each stat's behavior before being asked to allocate across all four.

Why the Commissioner's Meeting limits facilitator input

Student ownership peaks at the Commissioner's Meeting and the pre-match pitch. Both moments require students to make and defend a public mathematical argument without a safety net. Facilitators who improve proposals before they reach the floor remove the productive struggle. A weak proposal going to the floor is not a failure — it is the design.

Why the Best Teacher Award closes the program, not the championship

The final award signals what the program values: mathematical communication, not winning. Students who finished last in the competition can win this award. It reframes the entire four-week arc in the final ten minutes — from "who built the best bot" to "who understands this well enough to teach it."