February 16, 2016

This module introduces the concept and process of pattern recognition, the second step in Computational Thinking. Examples of pattern recognition are shown and resources for teaching pattern recognition skills in the classroom are introduced.

- Computer Science > Coding
- Computer Science > Computational Thinking
- Computer Science > Computers in Society
- Computer Science > Human Computer Interaction

- Grade 6
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- Grade 8
- Grade 9
- Grade 10
- Professional Education & Development
- Vocational Training

Determine whether a group of objects (up to 20) has an odd or even number of members, e.g., by pairing objects or counting them by 2s; write an equation to express an even number as a sum of two equal addends.

Identify arithmetic patterns (including patterns in the addition table or multiplication table), and explain them using properties of operations.

Generate two numerical patterns using two given rules. Identify apparent relationships between corresponding terms. Form ordered pairs consisting of corresponding terms from the two patterns, and graph the ordered pairs on a coordinate plane.

Count within 1000; skip-count by 5s, 10s, and 100s.

Explain patterns in the number of zeros of the product when multiplying a number by powers of 10, and explain patterns in the placement of the decimal point when a decimal is multiplied or divided by a power of 10. Use whole-number exponents to denote powers of 10.

Classify objects into given categories; count the numbers of objects in each category and sort the categories by count.

Measure volumes by counting unit cubes, using cubic cm, cubic in, cubic ft, and improvised units.

Recognize and draw shapes having specified attributes, such as a given number of angles or a given number of equal faces. Identify triangles, quadrilaterals, pentagons, hexagons, and cubes.

Draw points, lines, line segments, rays, angles (right, acute, obtuse), and perpendicular and parallel lines. Identify these in two-dimensional figures.

Classify two-dimensional figures based on the presence or absence of parallel or perpendicular lines, or the presence or absence of angles of a specified size. Recognize right triangles as a category, and identify right triangles.

Recognize a line of symmetry for a two-dimensional figure as a line across the figure such that the figure can be folded along the line into matching parts. Identify line-symmetric figures and draw lines of symmetry.

Understand that attributes belonging to a category of two-dimensional figures also belong to all subcategories of that category.

Describe the two-dimensional figures that result from slicing three-dimensional figures, as in plane sections of right rectangular prisms and right rectangular pyramids.

Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability.

Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations.

Use observations (firsthand or from media) to describe patterns in the natural world in order to answer scientific questions.

Construct and interpret graphical displays of data to identify linear and nonlinear relationships.

Ask questions that can be investigated based on patterns such as cause and effect relationships.

Use evidence (e.g., measurements, observations, patterns) to construct an explanation.

Patterns in the natural and human designed world can be observed and used as evidence.

Similarities and differences in patterns can be used to sort and classify natural phenomena.

Similarities and differences in patterns can be used to sort and classify designed products.

Patterns can be used to identify cause and effect relationships.

Graphs, charts, and images can be used to identify patterns in data.

Patterns in rates of change and other numerical relationships can provide information about natural systems.

Different patterns may be observed at each of the scales at which a system is studied and can provide evidence for causality in explanations of phenomena.

Cause and effect relationships are routinely identified, tested, and used to explain change.

A system can be described in terms of its components and their interactions.

Models can be used to represent systems and their interactions—such as inputs, processes and outputs— and energy, matter, and information flows within systems.

When investigating or describing a system, the boundaries and initial conditions of the system need to be defined and their inputs and outputs analyzed and described using models.

Models can be used to predict the behavior of a system, but these predictions have limited precision and reliability due to the assumptions and approximations inherent in models.

Models (e.g., physical, mathematical, computer models) can be used to simulate systems and interactions—including energy, matter, and information flows—within and between systems at different scales.

Science assumes natural events happen today as they happened in the past.

Many events are repeated.

Science assumes consistent patterns in natural systems.

Science assumes that objects and events in natural systems occur in consistent patterns that are understandable through measurement and observation.