If you have ever baked brownies, you know that you first have to gather the ingredients,

then measure them, mix them together, prepare the pan, heat the oven and cook them. If you

forget the sugar they don’t taste good and you have to start over. If the oven is too

hot you may burn them. Determining the right steps, following them correctly and completely

and learning from mistakes are all part of the process of algorithm design.

Algorithms are step by step instructions to get something done or the set of rules describing

how something works. A recipe, or a set of dance steps, or the storyboard for an animation

are all algorithms.

Throughout the three previous steps in the Computational Thinking process—decomposition,

pattern recognition and abstraction you have been working toward the design of an algorithm,

or “a set of instructions for completing a task.” Algorithmic design moves the problem

from the modelling phase, to the operation stage. Algorithms take the form of a recipe

or list of instructions that utilize efficiency and are written in such a way that they could

allow for automation, or use of computers to execute tasks, or they could simply be

followed by a human. This set of instructions should be sequential, complete, accurate and

have a clear end point. If written for a computer the algorithm must be comprised of a series

of tasks written in a way that a computer is able to perform.

In this video we will focus first on algorithms that don’t necessarily involve computers,

and secondly on some of the key understandings necessary to prepare an algorithm so that

it could be executed by a computer.

Although the term “algorithm” might strike fear into the hearts of some who last encountered

it in high school or college math classes, the definition of this term is simply “a

set of rules for solving a problem in a finite number of steps.” An algorithm is a set

of instructions and may or may not involve mathematics.

For example, a simple, digestible algorithm that we all use is a recipe. The sequence

of steps in a recipe must be followed in a certain order for the final product to taste

good. You wouldn’t bake brownies before you mix the ingredients together. Or grease

the pan after the batter has been poured in.

Another simple example is getting dressed in the morning. If you put your jeans on before

your underwear you might draw some funny looks!

We have been working with this necklace example problem throughout the course, one step at

a time:

A necklace is constructed from 20 blue beads and 14 red beads. Each blue bead costs $0.22

while the red beads cost $0.33 each. The cost of the thread is $0.14 per inch, and 24 inches

are required. What is the total cost of materials for the necklace?

To create an algorithm, let's write down all the steps it takes to solve this problem:

1. Break the problem down into its components—blue beads, red beads, thread, their costs and

amounts 2. Identify subproblems that need to be solved.

Determine the cost of each quantity of material (cost of blue beads, cost of red beads, cost

of thread.), then add the costs of all three materials together.

3. Recognize patterns between subproblems--in this case, the first three subproblems can

be solved in parallel, they don’t have to be solved in a particular order. The second

subproblem is sequential--meaning the costs for each material must be calculated before

they are added together. 4. Set up an operation that can be used for

the first subproblems. (material quantity x material cost)

5. Calculate the cost for each type of material using the appropriate variables

6. Add the costs for each of the three materials together to determine the total cost of the

necklace Let’s return to and review the Make a Monster

example. We first decomposed the monsters into parts (eyes, nose, mouth, etc.) and then

found similarities between the features of the three monsters. We then used abstraction

to replace the specific types of body parts with blanks so that any type of feature could

be inserted. We have now created a recipe to use for our algorithm. We can use it to

instruct other students to recreate the same or design new monsters based on the feature

types. For example, if you provide this algorithm, you come up with this new monster.

When we were discussing decomposition we took some time to explore Bee-bot, the bee that

moves a step at a time towards its destination. We looked at how its overall path was broken

down into steps. Now we will focus on Bee-bot in terms of algorithms. Can you give Bee-bot

the correct set of instructions to move from the start to the flower for Level 3? How about

for Level 5? Level 6?

Similar to Bee-bot, write out a set of instructions and have someone else follow your instructions

exactly to complete a task. You can emulate Bee-bot by finding a tiled floor or using

chalk on pavement to draw a grid. Take turns writing out instructions for each other to

follow to move from square A to square B. Students must enact instructions exactly as

they are written to see if all steps are correct. The person who wrote the instructions may

need to revise them if they don’t work.

Here is another similar activity, but in this one the instructions are for building a structure

rather than moving around. Students work in groups to develop algorithms to recreate models

made from plastic bricks.

Algorithmic design is critical in the Computational Thinking process. If a computer is not involved,

algorithmic design provides a recipe or instructions to solve the problem. If a computer is involved,

a human has to be able to tell it, in a language it can understand, how to solve the problem.

For a computer to be able to aid in the efficiency of solving your problem, the algorithm must

be comprised of a series of tasks written in a way that a computer is able to understand

and perform. This involves a translation between the language of humans and the high level

language of computers. Humans speak languages like English, Spanish, or Arabic. Computers

speak in bits, the basic unit of information in computing. You may have heard of the binary

system, where the numbers 0 and 1 are used to communicate information. These are bits.

Without getting too deep into Computer Science, suffice it to say that computers speak many

different kinds of high level languages---such as HTML or Javascript which are comprised

of bits. Computer programmers work to create instructions through coding that are born

from human ideas but translated for a computer to understand.

WISIWYG (What You See Is What You Get) interfaces can help you understand this concept--these

are designed so that non-computer science savvy people can use computers to build what

they need in their human language, but see how that translates to computer code.

Here are a few necessary understandings when thinking about designing algorithms for computing

(or otherwise).

Your instructions need to be unambiguous. For example, when baking it is more clear

to say bake until a toothpick comes out clean rather than just bake until done. An example

for a computer could be “choose a number between 1000 and 2000” instead of just “choose

a large number.”

You need defined inputs and outputs. The input for the Make a Monster example is one name

for each feature. The output of the algorithm is a new monster’s face. For the Bee-bot

it is a series of directional steps using forward, backward, left turn and right turn.

The output is the motion performed by the Bee-bot.

You need to have a defined end point. Your algorithm should answer a question or produce

a result and be clear when it has finished.

Your instructions must be sequenced in the correct order to complete the task at hand.

Flowcharts can help identify actions and the order in which they need to

occur. You may find you have sets or pieces of the instructions that need to happen over

and over again. This is called a loop or iteration.

You may have conditional statements that affect the variables (if/then, yes/no, true/false).

In these cases, the first condition dictates the second.

You need to be able to interpret the computer’s results. When designing an algorithm, it may

be difficult to know if it is working and the outputs are correct. Different algorithms

are developed to solve the same problems and they can produce different results. This is

why testing and revising algorithms is important. You can’t depend on the computer or machine

to do all the work--you have to interpret the results you get, think critically and

determine whether or not they make sense.

You have now experienced all the steps involved in Computational Thinking--you have decomposed

problems, identified patterns, abstracted information and designed algorithms or instructions

to solve a problem--using a computer...or not.

But how do you know when the process is finished? How do you know when you are done? It can

be difficult to know if you have accounted for all the important factors and if you are

truly solving the problem.

Steps and skills involved in Computational Thinking can and should be iterated (repeated).

Testing is the way to determine if it works or needs refinement. More often than not a

model that arises through Computational Thinking that is put into operation through algorithmic

design must be reevaluated and refined through testing. If your solution doesn’t quite

work or make sense, you have to return to the earlier steps in the process and try to

identify where a mistake may have occurred. This process of testing and identifying and

learning from mistakes is the key to solving big problems.

Just because you haven’t learned how to write computer code doesn’t mean you can’t

try creating algorithms for computers and programming! Several free online platforms

allow you and your students to manipulate pre-written blocks of code that represent

different tasks or actions. By sequencing, grouping, copying, repeating code, disguised

as colorful blocks, you are capable of creating and playing your own unique computer animations

and games! So you don’t have to know how to program (write code) but you do have to

understand algorithms (creating a logical set of instructions). In fact, doing this

is a fun way to introduce algorithms to your students. Creating a game or animation in

this way is an excellent method for learning how algorithms work.

We’ll use Scratch as an example of one of these programs. You can play with Scratch

without creating an account (via the Create tab), or, if you set up a free account on

the Scratch website you will be able to save your projects. You are immediately able to

start to experiment with creating algorithms. You can choose a “sprite” (character)

and and create and/or control its motion, looks, sounds, and other variables by dragging

and stacking coded boxes. Concepts such as loops or iteration can be explored using the

boxes in the “control” section. You can access operators to create conditional clauses

within your algorithms in both the “control” and “operators” sections (operators are

the conditional terms we talked about earlier). As you or your student’s understanding advances,

you can create and add in your own coded blocks for any functionality you can dream up!

Scratch is open source which means you can see and share your creations and also explore,

try out and remix the creations of others. In fact, the Bee-bot online program we have

been using during this PD was created in Scratch!

The ultimate goal for using computational power in solving problems is to solve seemingly

unsolvable problems, and to solve them as efficiently as possible. Figuring out what

instructions to give a computer for it to solve a problem efficiently means thinking

creatively.

Computational Thinking has led to many of the incredible tools that have become indispensable

in our everyday lives. Without Computational Thinking today’s cars wouldn’t run and

today’s planes wouldn’t fly as they are controlled by computers. Computational Thinking

has and continues to streamline and optimize efficiency in manufacturing processes to provide

you with the products you buy. Computational Thinking has led to such useful inventions

as Google Maps and virtual reality.

We as humans can learn from our mistakes and advance our understanding and knowledge to

solve bigger and more difficult problems. We can work together and it helps to have

a systematic method like the steps of Computational Thinking to help us tackle seemingly impossible

problems. Now especially, when solving problems can be greatly advanced by taking advantage

of the efficiency of machines and computers it is increasingly important to plan our problem

solving methods to address their incorporation. Today’s students are tomorrow’s problem

solvers!

Thank you for participating in this online professional development course.