Name:
Pre-algebra
URL:
pre-algebra
Description:
This pre-algebra course that systematically embeds data science thinking across the full arc of foundational mathematics. Each lesson is anchored in a real-world dataset and uses CODAP as its primary analysis environment, building student fluency with that tool progressively, from simple dot plots in early lessons to scatter plots, box plots, derived variables, and plotted functions by the end. The mathematical content covers the standard pre-algebra curriculum: integer operations, rates and ratios, proportional reasoning, percentages, geometric classification, properties of operations, linear and nonlinear relationships, algebraic equations, probability, and statistical displays. What distinguishes this course is that these topics are never taught as abstract procedures. They arise from genuine investigative need. Students encounter integer subtraction because they want to quantify home court advantage. They encounter equations because they want to know what an NBA player needs to do to reach a performance benchmark. They encounter percentages because nutrition labels use rounding rules that require explanation. Mathematics is consistently positioned as a tool for reasoning about something real. A set of recurring sports contexts threads through multiple lessons, creating continuity and deepening student familiarity with analytical vocabulary across the year. Data science practices are woven throughout at increasing levels of sophistication. Students collect and clean data, create derived variables from existing ones, describe and compare distributions, build and test classification models (including a decision tree connected explicitly to spam filters and AI systems), evaluate model accuracy against real outcomes, and construct evidence-based arguments using multiple representations. The course also introduces several foundational ideas that students will encounter again in statistics and data science: variability and its sources, the difference between correlation and causation, model limitations, proportional comparison across unequal groups, and the Law of Large Numbers. By the end of the course, students have developed both the pre-algebra skill set required for Algebra I and the data reasoning habits that support deeper engagement with quantitative work across disciplines.
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