Problem-solving processes

What's this about?

Designing involves representing how the solution will be created and what the solution will look like (user interface). It is the ‘how’ process.

An algorithm is a step-by-step process or series of instructions to achieve a particular outcome. It is used to show how the solution will function – it is the rules, the sequence and decisions.

Algorithms can be written as a series of steps or drawn as a flow chart. Creating an algorithm is an integral part of computational thinking and of creating a program to instruct a computer or robotic device. Computational thinking helps break down a complex task into smaller chunks. Looking for patterns in the algorithm helps us work out opportunities to use loops where code is repeated.

A paper prototype can be used to map out design ideas; for example, what is on screen, the logic behind transitioning between screens and how various elements work together as a system. The paper prototype can inform algorithm development.

Once the algorithms have been completed they are converted into a program, so that those instructions can be executed by the digital system. At this level, students use a visual programming language.

What's this about?

A visual programming language enables students to sequence commands (displayed as blocks) to create a program (or digital solution). This could be a simple task of animating a character (sprite) in a story; or it could be creating complex programs to model a real-world application.

In programming languages, decisions (branching) are implemented using if/then or if/then, else statements. Repetition is implemented using loop statements.

As students form their sequential blocks, they can introduce the repeat/loop block to avoid repetition in code as a more advanced aspect of sequences. For example, instead of putting the same single blocks one after the other, we can wrap an iteration (repeat/loop) block around blocks they would like to repeat, to tell the computer to execute the code a certain number of times. Repeat loop blocks allow us to set a value to control how many times the loop is executed. For example, when creating a quiz, the questions are repeated until the correct response is given.

User input is a way the user interacts with the computer program. For example, a user might click on a sprite or avatar in a game or animation to make it react in some way, or they could enter their name or a quiz answer when prompted. When we think of input and output, we can characterise the images on the screen and sound as output. Input is anything that provides some information to our program – such as a click of the mouse or entered text, which in turn will activate or modify a process.

What's this about?

Evaluation takes places at two levels. The ‘micro’ level is where students judge if the solution they created met the functional requirements identified in the defining process. The ‘macro’ level, which takes a broader view, asks students to consider how their and existing solutions used in information systems, such as a library borrowing system, would be judged on the basis of being sustainable and able to meet the current and future needs of a community.

Sustainability includes factors such as the energy levels required to operate the solution and other resources used such as paper for printed output. Future needs could include whether new data, such as new library books and DVDs, could be used in the solution.

Evaluating draws on systems thinking where students need to consider how the outputs (solution) meet and affect the users.

Artificial Intelligence (AI) has potential to be an integral part of systems across many industries and transform the way we do things. This technology though, needs to be used carefully and thoughtfully. What are some of the challenges we face when implementing AI systems? How can AI be used for good? How do we ensure fairness for all? AI systems need to be safe and reliable. We need our personal information and privacy protected. These are all ethical considerations that must be part of the development and lifecycle of an AI system.