| Q1 | Q2 | Q3 | Q4 | |
|---|---|---|---|---|
| Number: Quantities | N-Q.1, N-Q.2, N-Q.3 | N-Q.1, N-Q.2, N-Q.3 | N-Q.2, N-Q.3 | — |
| Algebra: Equations & Inequalities | A-REI.10 | A-REI.1, A-REI.3, A-REI.5, A-REI.6, A-REI.10, A-REI.11, A-REI.12 | A-REI.10, A-REI.11 | A-REI.4, A-REI.4a, A-REI.4b, A-REI.10, A-REI.11 |
| Functions: Interpreting | F-IF.1, F-IF.2, F-IF.3, F-IF.4, F-IF.5 | F-IF.1, F-IF.2, F-IF.3, F-IF.4, F-IF.6, F-IF.7, F-IF.9 | F-IF.4, F-IF.5, F-IF.6, F-IF.7, F-IF.8b, F-IF.9 | F-IF.4, F-IF.5, F-IF.6, F-IF.7, F-IF.8a, F-IF.9 |
| Functions: Building | F-BF.1 | F-BF.3 | F-BF.3 | F-BF.3 |
| Statistics: Quantitative Data | S-ID.6, S-ID.6a, S-ID.6b, S-ID.7, S-ID.8, S-ID.9 | — | S-ID.1, S-ID.2, S-ID.3, S-ID.5, S-ID.6a | S-ID.6a |
| Algebra: Expressions | — | A-SSE.1a | A-SSE.1b | A-SSE.1a, A-SSE.1b, A-SSE.2, A-SSE.3a, A-SSE.3b |
| Algebra: Creating Equations | — | A-CED.1, A-CED.2, A-CED.3, A-CED.4 | A-CED.1, A-CED.2 | A-CED.1, A-CED.2, A-CED.3, A-CED.4 |
| Functions: Linear & Exponential | — | F-LE.1a, F-LE.1b, F-LE.2 | F-LE.1a, F-LE.1b, F-LE.1c, F-LE.2, F-LE.3, F-LE.5 | F-LE.3 |
| Number: Real Number System | — | — | N-RN.3 | N-RN.3 |
| Algebra: Polynomials | — | — | — | A-APR.1, A-APR.3 |
| Statistics: Probability | — | — | — | S-CP.1, S-CP.2 |
From recognizing relationships → to function notation → to comparing and transforming linear, exponential, and quadratic functions
The function concept is the organizing structure of Algebra I. Students first learn what makes a relationship a function and how to use function notation (Module 1), then meet the linear (Module 2), exponential (Module 3), and quadratic (Module 5) families as members of the same system that differ only in how they grow. Anchoring each new family in the shared language of key features, domain, and rate of change keeps students from memorizing a separate procedure for each function type.
Common sticking point: The most common stall is F-IF.1: students confuse 'function' with 'equation' or 'formula.' Anchor it in real contexts — a vending machine (one input, one output) versus a lottery (one input, many outcomes) — before symbolic notation, or students will struggle to recognize non-functions in the quadratic and piecewise work later.
From quantities and rate of change → to writing and graphing linear functions → to solving equations, inequalities, and systems
Linear functions are the simplest and most pervasive model in the course. The arc begins with quantities and units and the bivariate regression work of Module 1, where slope and intercept first appear as model parameters. Module 2 then formalizes that understanding — connecting arithmetic sequences to linear functions, building and transforming them, and culminating in equations, inequalities, and systems.
Common sticking point: The transition from graphing a line to writing its equation from a context (A-CED.2) is where procedural knowledge breaks down. Build the bridge with table-to-equation tasks before word problems, and watch for students who solve correctly but cannot justify the steps (A-REI.1) — a sign of fluency without understanding of equality.
From solving linear equations → to systems → to quadratic equations using multiple methods
Equation solving is a progression, not a single skill. A-REI.1 establishes that each step follows from a property of equality; linear equations and systems (Module 2) build the fluency students later apply to quadratics. Quadratic methods (Module 5) come last because factoring, completing the square, and the quadratic formula each depend on prior mastery of polynomial structure (A-SSE, A-APR) and function key features.
Common sticking point: The most persistent error is sign mistakes when completing the square or applying the quadratic formula — usually a symptom of weak structure-sense (A-SSE.2) rather than careless arithmetic. Build seeing-structure as a visual skill with area models before introducing completing the square as a procedure.
From bivariate patterns → to comparing distributions → to categorical data and probability
Statistics follows a deliberate sequence. Bivariate analysis comes first (Module 1) because scatter plots and regression connect naturally to the linear functions students are building. Univariate and categorical distributions (Module 4) come later, when 'compared to what?' is a meaningful question. Probability (Module 6) closes the year, connecting frequency tables and conditional reasoning to decision-making.
Common sticking point: The correlation-versus-causation distinction (S-ID.9) is understood after one lesson but rarely transfers. Students who can recite 'correlation does not imply causation' still write 'therefore X causes Y.' Require a plausible lurking variable for every correlation before moving on.