Digital Literacy Modules

What is Pattern Recognition?

• Pattern recognition involves identifying repeating characteristics, ideas, or information 
• Noticing and discerning what, when, and how often a set of information (pattern) repeats can help answer questions and solve problems. 
• Sometimes pattern recognition includes recognizing when differences occur in otherwise standard data, especially if these differences repeat or resemble each other.

Example:

• A cybersecurity analyst notices failed log-in attempts to a campus system at 3:00 am, 3:15 am, 3:30 am, and 3:45 am on a Tuesday morning. The analyst believes this might be an automated attack because the log-in attempts occur at regular 15-minute intervals. 

Computational Thinking: Pattern Recognition Video by Curriki

Watch the first 7 minutes and 32 seconds (0:00-7:32) of the video to learn more about pattern recognition and how it is useful in problem solving across subjects ranging from art history to computer science.

What are Generalization and Abstraction?

• The processes of generalization and abstraction involve recognizing that there are common patterns that can be expressed by simplified representations. 
• The simplified expressions often demonstrate relationships and can be used to solve various types of problems. 
• Exercising generalization and abstraction effectively frequently requires identifying and eliminating unnecessary information. 

Example:

• Maps are abstractions of real world locations on earth. Maps share common features such as measures of distance, street and landmark names, directional orientation (north-south, east-west), and more, depending on the map's purpose. We use maps to solve problems like how long will it take to drive from San Antonio to Houston following a specific route? If there is heavy traffic on a given highway, what would be an alternative route to follow? Where would be a halfway point to stop for lunch? 
• In the map example, some pieces of information, such as street widths, may be left out to allow for more efficient abstraction. 

Computational Thinking: Abstraction and Pattern Generalization Video by Curriki

Watch the first 6 minutes and 7 seconds (0:00-6:07) of this video for an overview of abstraction and pattern generalization for problem solving with basic examples of applications. For a more detailed example of applying abstraction and pattern generalization, watch the entire video. 

What is Algorithm Design? 

• In simple terms, algorithm design is creating a set of instructions for completing a task.
• The other three components of computational thinking (decomposition, pattern recognition, and generalization and abstraction) facilitate algorithm design.
• Algorithms should have a clear stopping condition.
• Algorithms in math may be expressed as equations but algorithms do not require numbers. 

Example:

• A recipe is an example of a common algorithm. 
• To bake a cake from a recipe, you need to follow a series of steps, often in sequential order, to have an edible result at the end.
• While the order of some steps may be switched without negative consequences, others cannot. For example, greasing the pan after baking the cake would not make sense. 
• Baking a cake involves a finite stop. You would not want to keep baking the cake after it is done. 
• Substituting some ingredients may still result in a tasty cake but incorrect measurements or omitting other ingredients could yield a poor product.

Computational Thinking: Algorithm Design Video by Curriki

Watch the first 4 minutes and 16 seconds (0:00-4:16) to gain a basic understanding of algorithmic design applied to different situations.