AnimalWatch materials are organized into Learning Objectives that make it easy to connect AnimalWatch activities with your classroom instruction.
You can assign one Learning Objective to an entire class, or select different Learning Objectives for different students. You can also request a Review Learning Objective that will generate sets of review problems, e.g., for CST/STAR or AIMS test preparation.
Each Learning Objective is mapped to specific sections of the California Mathematics Content Standards for Grade 6. Learning Objectives can be assigned in any order; you don't have to follow them in sequence. Each Learning Objective includes three Word Problem Sets, as well as Skill Builder drills that help students build fluency with basic math facts and math vocabulary terms.
- Integers Part 1: Addition and subtraction of positive and negative integers
- Integers Part 2: Multiplication and division of positive and negative integers
- Fractions Part 1: Part-whole relations, addition and subtraction of fractions with like denominators
- Fractions Part 2: Addition and subtraction of fractions with like and like denominators, reduce/simplify fractions, improper fractions and mixed numbers
- Fractions Part 3: Multiplication and division with positive and negative fractions
- Fractions Part 4: Decimals, place value, number line, fraction-decimal-percent equivalents and conversions
Algebra and Functions
- Variables and expressions: Represent an unknown, solve one variable equation, equivalent expressions
- Units of measurement: Unit conversion, time, weight, temperature, length, distance
- Rate Speed Distance Time: RSDT conversions, computation of average rates, time intervals
- Money: Calculate tax, finance charge, discount, convert currency
Geometry and Measurement
- Shapes & Angles: identify 2D figures, find area of figure; identify angles, find supplementary and other angles
Statistics and Probability
- Statistics: compute averages (mean, median, mode), find range, outliers
- Probability: work with samples, interpret data, find outliers, probability equivalents