Instructor plan · Bachelor Statistics (English)

A flipped, data-driven statistics course

The interactive modules deliver and drill the content; the 2 contact hours per week become the things a screen can't do; and the instructor dashboard tells you, each week, exactly where the class is stuck. This plan maps 14 weeks, the assessment (30%+ on continuous progress), and a repeatable session template.

The core move: before each session, open statistics/admin.html and look at which sections stalled and which homework/practice problems scored lowest. Aim that week's class at those gaps. A statistics course taught with live data about the class itself is the whole thesis, lived.

1. Assessment — at least 30% on continuous progress

A suggested weighting (tune to your school's rules). The progress portion is fully evidenced by the dashboard: completion at mastery, homework & practice scores, time-on-task, reflections, and weekly in-class micro-checks.

30%

Continuous progress

Module mastery-completion + homework + Practice Arena scores + short reflections + weekly in-class micro-checks. Tracked automatically.

15%

In-class participation

Peer-instruction votes, simulations, case debates during the 2-hour sessions.

15%

Capstone

The pizzeria "board meeting": a group, data-defended business decision + presentation in Week 14.

40%

Final written exam

Closed-book, 6 exercises × 10 pts; 40/60 to pass (this hurdle stands within the component).

Integrity safeguards (so 30% on progress is defensible):

Per-student randomised numbers — the Practice Arena already seeds problems to each student; copying is pointless. built ✓
Mastery, not clicks — count a module "done" only at a score threshold, not on reaching the last screen.
Weekly in-class micro-check — a 5-minute paper question mirroring that week's homework; if online and in-person scores diverge sharply, investigate.
Time-on-task sanity — the dashboard logs active minutes; near-zero time with full completion is a flag.

2. The repeatable 2-hour session

Pre-class: students do the module + homework (graded progress). Class time is spent only on what needs a room.

Time	Block	Why it needs the room
15 min	Peer-instruction warm-up — one conceptual MCQ targeting last week's worst dashboard misconception. Vote → discuss in pairs → re-vote.	Surfaces and repairs misconceptions live.
25 min	Live-solve the hardest archetype (lowest-scoring exam card / homework problem), narrating the choice of method and the interpretation.	They can compute; they can't yet choose & interpret.
30 min	Hands-on activity / simulation / live class data (sample to build a sampling distribution; run a permutation test; collect a class survey).	The keystone made physical and active.
30 min	Business-decision case — the pizzeria advances; groups decide under uncertainty and defend it.	Judgment under uncertainty is a discussion.
15 min	"Stat in the wild" (dissect a real headline/ad) + the in-class micro-check + preview next module.	Literacy + accountability for the flip.

3. The 14-week schedule

Wk	Topic (Saylor)	Pre-class (online, graded)	In the 2-hour session
1	Foundations — why stats is hard; model ≠ truthModule 0	Module 0 + M0 homework	Course launch; "model not truth" discussion; collect a live class dataset; first peer-instruction vote.
2	Describe: displays & centre Ch 1 / Saylor 1–2	Ch 1 §1.1–1.3 + Practice Arena	Histogram & shape activity on class data; mean-vs-median live-solve; first "stat in the wild".
3	Describe: spread, position, box plots Ch 1	Ch 1 §1.4–1.7 + Ch 1 homework	Build a box plot by hand on class data; the pizzeria pricing decision; micro-check #1.
4	Probability Saylor 3	Ch 2 (probability) module + practice	Probability-intuition traps (base rates, gambler's fallacy); conditional-probability gut-checks.
5	Discrete random variables, binomial Saylor 4	Ch 2 (discrete RV) module + practice	Expected-value betting game; binomial simulation; "is this game fair?" decision case.
6	Continuous RVs & the normal model Saylor 5	Ch 2 (normal) module + Ch 2 homework	Normal-table clinic; z-score relay; micro-check #2.
7	Sampling distributions & the CLT Saylor 6	Sampling-distribution simulator + module	The keystone: hands-on sampling (candy/dice) to build a sampling distribution; "why does this work?" discussion.
8	Estimation & confidence intervals Saylor 7	Ch 3 (estimation) module + homework	Build a CI for the class data; the CI-misinterpretation clinic (peer instruction).
9	Hypothesis testing — logic & one-sample Saylor 8	Ch 4 module + practice	The p-value misconception clinic; a permutation test by shuffling labels; micro-check #3.
10	Two-sample problems Saylor 9	Ch 5 module + homework	Design & run a class A/B test (taste test / two recipes); paired vs independent.
11	Correlation & regression Saylor 10	Ch 6 module + practice	Fit a line to class data; the correlation-≠-causation case (confounders).
12	Regression inference & prediction Saylor 10	Ch 6 (inference) module + homework	Prediction & residuals clinic; "regression misuse in the wild"; micro-check #4.
13	Chi-square & ANOVA Saylor 11	Ch 7 module + practice	Contingency-table activity; the "which test do I use?" decision-tree drill.
14	Capstone & exam prep	Exam Cards (spaced review)	Pizzeria board-meeting presentations; exam review driven by the card deck and dashboard gaps.

4. Module-enhancement backlog

What makes the modules outstanding, in build order. Done items are live now.

DONE — Monitoring dashboard (who / when / time / completion / score).
DONE — Per-student randomised numbers (Practice Arena) — integrity + infinite practice.
DONE — Sampling-distribution simulator — the keystone made visible.
NEXT — Predict-then-commit checkpoints with misconception-targeted feedback (e.g. detect n vs n−1) + mastery gates.
NEXT — Collect-your-own-data: students run the widgets on real prices they gather (model ≠ truth, made personal).
NEXT — Spaced retrieval of the exam cards (a daily 5-minute review mode).
NEXT — "Stat in the wild" literacy module (dissect real claims).
NEXT — Excel / Google Sheets bridge ("do it in the widget, now in a spreadsheet").
NEXT — Reflection field per module (gradeable metacognition).

Living document · adjust freely. Course hub: statistics/index.html · Dashboard: statistics/admin.html