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The Teacher Hub is an environment for teachers and career development practitioners. It has professional learning resources for computational thinking and career education

Algorithms & Evaluation

[Music plays and an image appears of a CSIRO logo on a black screen]

[Image changes to show a title slide displaying text: Algorithms & Evaluation, Understanding Computational Thinking and its Place in the Curriculum, Created By Allira Crowe, Digital Careers – CSIRO Education & Outreach, Presented by Allira Crowe, Education & Outreach, www.csiro.au]

Allira Crowe: My name’s Allira. I’m from Digital Careers, part of CSIRO Education and Outreach. So, let’s get started looking at computational thinking.

[Image changes to show a new slide displaying the breakup of CSIRO Education and Outreach and text appears on the slide: CSIRO Education and Outreach, 12+ STEM Programs, 100s of 1000s Students per year, 300+ Partners, 35+ years, 2000+ Teachers In STEM Professionals In Schools]

So let’s look at the broader picture first and I’ll set the scene. So, who is CSIRO Education and Outreach? Well, CSIRO have been delivering STEM learning experiences for Australian students for more than 35 years. With over 80 staff across the country we have a national footprint. We deliver 12 different STEM programmes which are scalable and mapped to the school curriculum. Our programmes are deployed in thousands of schools every year.

[Image changes to show a new slide showing the different programmes in coloured text boxes below text heading: CSIRO Education & Outreach Programs]

As you can see on your screen we have a myriad of education and outreach programmes available to schools. I’m from Digital Careers. So, we’re in the kind of in the middle in the dark blue and we actually partner with various organisations to provide you and your students with valuable learning experiences. So, on your screen you can there are Young ICT Explorers, Big Day In, and Bebras.

[Image changes to show a slide displaying the CSIRO logo and a text heading: Digital Careers]

So, who are Digital Careers? Well, as I said we’re part of CSIRO Education and Outreach. We came about from a field of research that shows that predictions show that 40% of the current jobs will actually be affected by digital disruption and the fact that we know that there is a critical shortage of digital literacy and enterprise skills.

So, enterprise skills are actually the computational thinking skills plus the soft skills. And the young people joining the workforce actually don’t have these very well developed. Now, we are actually here as part of Digital Careers to help students to prepare for this uncertain and ever changing future.

So, we do this by helping them get on track, by offering free programmes such as the Bebras Australia Computational Thinking Challenge in order to start building and increasing awareness, interest and participation in computational thinking and digital technologies.

This assists in building that essential foundational knowledge for students moving forward. Now we also help teachers by unpacking and demystifying the Digital Technologies Curriculum through education and training via webinars and providing resources.

So, finally, we also work with teachers, students and careers counsellors helping them understand what ICT and STEM jobs actually look like. So, how they’re going to change in the future as well and here we talk about how any job in any industry, not just STEM related fields, how they are actually going to be changed by technology.

Due to all of this state of flux and this change we actually know that computational thinking skills and enterprise skills are really becoming increasingly important.

[Image changes to show a diagram on the slide depicting an overview of computational thinking skills and text appears: What is Computational Thinking? An Overview]

So, for those who have joined us before we’re going to do an overview of computational thinking. So, for those who are new I’m just going to give you a bit of a recap. So, computational thinking is actually a way of thinking that promotes problem solving. Now, it’s got a number of skills and attributes within it and it actually uses technology to enhance those skills. So, as I said computational thinking is made up of skills and attributes.

Now, those skills are those traditional kind of technical skills or known as those hard skills and are a bit easier to teach in the classroom. The attributes on the other hand are known as those soft skills, things like communication, leadership, all of those kind of skills, and they’re far more difficult to teach in the classroom. So, what does that look like? Well it’s the ability for students to develop the confidence to tinker and problem solve when coming up with solutions to problems and experiment with those solutions.

They also would be able to develop the resilience to work through difficult problems, and persevere with questions, or challenges, that may be open ended or a little bit ambiguous. Also using creativity and that trial and error mindset when problem solving.

And finally, the ability to collaborate when students are asked to solve problems within a team environment.

So, that then brings us to the six skills which we are going to be focussing on two of them today but I’ll give you an overview of the six.

So, computational thinking involves taking a complex problem, and breaking it down into a series of small more manageable parts or problems, so decomposition.

Each of these smaller problems can then be addressed individually considering how similar problems have actually been solved in the past. So, pattern recognition. And then focussing on the important details while ignoring irrelevant information, so abstraction.

The next step is starting with simple steps or rules to solve each of the smaller problems and that can be designed. So, algorithms. And then once the final solution is considered it is tested through modelling and simulation and made sure that it actually satisfied the original question or problem and that the solution fits, it’s accurate and it’s actually efficient. So, that’s the process of evaluation.

Now, after you’ve gone through those six computational thinking skills to solve your problem, the final step is to actually use ICT to programme that solution. Now, this is isn’t actually necessary in all problems and problem solving processes.

OK. So, we’ve talked about computational thinking. You might be a little bit confused we’re Digital Careers. Why are we focussing on computational thinking rather than programming and coding and that more technical, those tech based skills and actually physically using the technology?

Well, we actually believe that students need to build that foundation upon which to build all of those tech skills like the coding and the programming. In order to do that they need to have that foundation which is computational thinking. So, the technology as we know is changing really rapidly. You know, a new phone comes out every year. By the time you’ve actually mastered it, and what it can do, and it’s abilities, it’s generally obsolete. So, the learning of flexible and transferrable skills and knowledge, so the stuff behind the technology, that’s actually far more important and then can be applied to any piece of technology that is created.

So, as I said before, we actually go into more detail in each of those computational thinking skills in pairs in our webinars and videos. So, we look at decomposition and pattern recognition together, abstraction and modelling and simulation, and finally algorithms and evaluation which is what we are doing today.

[Image changes to show a new slide displaying a text heading and text below: Computational Thinking and The Curriculum, An Overview, Digital Technologies Key Concepts, Knowledge and Understanding, Process and Production Skills]

We also link the computational thinking skills to the Digital Technologies Curriculum Key Concepts. So, as we know there are ten key concepts and by building teacher confidence at that real broad level, it actually allows you to have more impact in the classroom with your students through implementing both knowledge and understanding and those process and production skills.

[Image changes to show a new slide depicting a table displaying the learning strands for the various year levels under the text heading: Computational Thinking and the Curriculum, Digital Technologies]

And on your screen you can actually see that we’ve delved a little bit further into the Australian Digital Technologies Curriculum. You can see we have actually aligned the content descriptors for each year level. Now, these are highlighted in the table and are mostly relevant to the process and production skills. So, that collecting, managing and analysing of data but also the creation of digital solutions.

So, this actually isn’t a blanket rule and will definitely depend on the activities that you implement into your classroom in order to address the curriculum.

[Image changes to show a new slide displaying information about algorithms below the text heading: What are Algorithms And Why Are They Important?, Algorithms, Create a series of ordered steps taken to solve a problem]

So, let’s stuck into the computational thinking skills. The first one we’re going to look at is algorithms. Now, according to ACARA and the Digital Technologies Curriculum, an algorithm is basically a plan. It’s a set of step by step instructions in order to solve a problem. Each instruction is identified and then the order in which they’re carried out is planned.

Algorithms must have a start and end and clear instructions in between. Algorithms are often used as a starting point for computer programming and can be represented as a flow chart which we’ll see a little later on, or pseudo code. Now in computer science, an algorithm is defined as a finite number of steps that frequently involves repetition of an operation.

Now, there are three main properties of algorithms. They are linear sequence, so tasks or statements that follow on from one another. Conditional, so that “if, then, else” decision making and loops. So that “while and for” addressing those repeated statements. So, why are algorithms so important?

Well, algorithms are applicable in every aspect of our lives. They’re important because they make us think and perform smarter, faster, and more efficiently. In computer science you can’t just start coding. You actually need to have the solution in mind before you start coding and algorithms are actually the way to do that.

Algorithms also allow analysts to make sense of big data. So, we as individuals actually reap the benefits. So, for example identification of important trends means that we, as individuals, can be prepared for the future. It also helps Google to maintain relevance and accuracy which is really important for us because let’s face it, we Google almost everything.

By identifying performance problems through algorithms it means that customers get the best products possible and there’s an awareness of customer habits which means that products that are coming up are actually designed with us, the consumer, in mind. Using algorithms is becoming increasingly important in everyday life and is going to continue to be implemented in far more tasks moving forward into the future. Think automation.

So, to recap, creating algorithms is mostly for efficiency and we’re basically trying to achieve something, wanting it to take less time and be a lot easier for us. So, today we’re going to refer to the broader definition of algorithms within computational thinking rather than just within the specific computer science field.

[Image changes to show a new slide displaying a cheese toasty underneath the text heading: Algorithms in The Everyday]

Now algorithms are present in everyday life from getting dressed, to brushing your teeth, to making a coffee, to watching the morning news and knowing that an algorithm actually helped the weather man or woman make correct and accurate predictions. Also, driving to work and following GPS directions when going to meetings.

Now, algorithms need to be written and followed in a certain order. If the order, or instructions are unclear or muddled then the outcome will actually not be what you intended. So, a simple example is getting dressed in the morning. You don’t put your shoes on before your socks and it’s unlikely that you put your underwear on after your pants unless you’re superman. So, clarity, specificity and sequencing is all important.

So, let’s consider and work through an example of an algorithm in more detail. So, making a cheese toasty as you can see on the screen. Algorithms have certain specifications, so they have inputs, outputs and processes. So, an order of instructions.

So, what would the algorithm for a cheese toasty look like?

[Image shows a flow chart appearing on the screen over the cheese toasty describing how to make a cheese toasty]

Well, as I mentioned earlier, one way of representing algorithms is to actually use flow charts as seen on your screen. Thank you so much to GitHub for creating that one.

Now, they’re a really useful way of planning how a computer programme might work and show others the way that you are thinking. So, as you can see there is a clear start which is represented by that rounded rectangle at the top. There are inputs which are the parallelograms. They’re not represented here. So, in our example of a cheese toasty the inputs would actually be the ingredients and their quantities.

Then, there’s a process. So, an instruction or a command and these are represented in the flow chart by the rectangles. So, this is going to be the recipe or the method. There are also decisions which are the kind of triangles or the diamonds and there are outputs and they’re the parallelograms, in other words, our final product. And then there’s a clear end which is that rounded rectangle at the bottom right hand side of your screen.

The arrows actually connect the symbols. They show the direction of the flow of instruction. Now, algorithms can also have values that can be constant or variable. So, for example, the main content of a toasty could be cheese in our example or it could be baked beans. Therefore the main ingredient would be a value which is variable.

You could require one sandwich or you could require 20 sandwiches. So, therefore the number of sandwiches is another variable. As you can see the simple task of making a cheese toasty is actually quite a lengthy algorithm.

[Image shows the flow chart being removed from the slide and the cheese toasty can be seen again and then a microscope appears on the right of the cheese toasty]

Now algorithms actually are present in jobs. So, from the mundane such as admin process improvement to the exciting such as a career as an automation engineer. Now, automation engineers develop and apply the technological principles needed to control or monitor the manufacturing process.

They can be involved in developing and designing the concept, testing, installation, and monitoring of automation processes. These skills required depend upon the product that’s being manufactured, the process, and industry. However, generally they include identification of automation processes or opportunities, designing the automation equipment or the process, simulating and testing. So, this is where the computational thinking skill of modelling and simulation actually comes into practice. The testing of outcomes and looking at efficiency, and programming and fault finding.

[Image changes to show a new slide displaying an algorithm example with a list of possible answers under the text heading: Algorithms In The Classroom, A Computational Thinking Example]

The best way to understand the skill of algorithms is to actually complete an example. So, this is a Year 5/6 hard level question pulled from the Bebras Australia Computational Thinking Challenge. This question looks at robots following a set of instructions in unison. So, I’m going to quickly go through the example which is on your screen.

So, if we give the list, N,N,S,S,E, then robot A will pick up a cone, robot B will pick up a ring, and robot C will pick up a cone. So, the question asks, “Which set of instructions can be sent to the robots in order for all three to pick up a sphere, a cone, and a ring?”. I’ll get you to pause the video and jot down your answer.

For those that have figured out your answer, the answer is number two, N,E,E,S,E. So, how does that relate to the skill of algorithms? Well the best way to solve the problem is to follow the diagram and instructions. Here we followed a pre-written algorithm and checked for errors and accuracy. Now, number one is incorrect because two rings and no sphere is picked up. Number three is incorrect because two spheres and no ring is picked up and number four is incorrect because two cones and no sphere is picked up.

And how do algorithms in this example relate to the Digital Technologies Key Concepts? Well on the left hand side of your screen it relates to specification as we use descriptions and techniques to define and communicate a problem clearly.

Also algorithms followed a set of steps in order to come up with a solution. Implementation, so translating and programming, or automation of an algorithm. So, in this example it replicated a computer programme and robots working in parallel following an automated algorithm.

And Digital Systems, so this example was based on an algorithm in conjunction with a computer programme and a robot. So, software and hardware. This example also used the computational thinking skill of modelling and simulation as we tried to replicate the routes of the robots on the screen.

[Image changes to show a new slide displaying another algorithm type diagram below a text heading: Algorithms in the Classroom, Digital Technologies & Cross Curricular Examples]

So, now we’re going to look at a Digital Technologies Classroom example. As we addressed the Year 5/6 on the previous slide we’re going to continue with that year band. According to ACARA and the Australian Curriculum, a Year 5/6 student according to the Student Achievement Levels, a student that is at standard should be able to represent simple systems using flow charts based on procedures and decisions and use the symbolic language to create a working programme. So, for example, Kodu, Scratch and Yenka.

So, from this description we can actually see students designing and creating a game in Kodu lab fitting perfectly. Rather than writing text based code students actually move icons around the screen to create their games. In a Year 7/8 classroom an example could be students developing a simple app, documenting the entire process from ideation to development, UX, or user experience, and algorithm design, to testing and evaluation.

According to ACARA and the Australian Curriculum this addresses the at standard achievement levels of “Represent a simple system using an algorithmic descriptor language” and “Represent models using coding”.

Now, what about some cross-curricular examples. So, in the Maths classroom, algebra, geometry, and long division. That requires students to follow a certain sequence when solving their problems. In an English classroom, students writing poetry, for example a Haiku, or writing a story or essay. In Home Economics students following or writing a recipe and in the Science classroom students following or writing up a scientific experiment.

[Image changes to show a new slide with text beneath the text heading: What is Evaluation And Why Is It Important? Evaluation, Determine effectiveness of a solution, generalise and apply to new problems]

So, let’s move on to evaluation. ACARA and the Digital Technologies Curriculum describes evaluation as the following, once a solution is designed evaluation is a process that allows us to make our, make sure our solution does the job it has been designed to do, that is does it efficiently and then allows us to think about how it could be improved. So evaluation tends to work hand in hand with algorithms. And once an algorithm is actually written it needs to be evaluated before a solution can actually be achieved.

So, this can actually be digital or an ICT solution but not always. So, why is evaluation so important? Well, evaluation allows us to consider the solution to a problem, make sure it meets the original design criteria, that it produces the correct solution, and make sure that it’s fit for purpose.

So, without evaluation we would not identify any potential faults and therefore we wouldn’t be able to solve the initial problem. Without evaluation of solutions and… or their algorithms we could not then generalise them and then apply them to new problems.

So, evaluation does require higher level thinking such as identifying assumptions, posing thoughtful questions, pursuing deeper understanding through reflection and perspective taking and making informed decisions in regard to, for preparation for action.

[Image changes to show a new slide displaying a slice of pizza beneath the text heading: Evaluation In The Everyday]

Evaluation in the everyday, so if evaluation is determining the effectiveness of solutions, generalising them and applying them to new problems, then let’s learn through an example so cooking, specifically pizza as you can see on your screen.

When making a pizza it’s necessary to know the following, what kind of pizza to make, what ingredients are needed and their quantity, what equipment is needed, how many people is the pizza for, where are you going to bake it, what temperature to bake it at and how long it actually needs to bake.

So, this is actually where the computational thinking skills of decomposition and abstraction. So, if you remember back to the BBC bite size cake example, that’s actually where these come into the process.

So, here we’ve basically come up with a recipe template. We now need to write our algorithm for making a pizza similar to what we did with the cheese toasty. Once that algorithm is written we need to evaluate the algorithm in order to obtain our solution. So, we do this by asking a few key questions. Is it easy to understand? Is it complete? Does it solve all aspects of the problem? Is it efficient? Can we incorporate loops if there is repetition of a certain process? So, this is where the computational thinking skill of pattern recognition comes into play. And does it meet the design criteria provided?

So, if the algorithm actually meets all of that criteria it’s likely to be successful so we can implement and create the solution. If we choose to test our algorithm and solution, this is where modelling and simulation, yes another computational thinking skill, actually comes into the process.

So, we finally have our solution but we actually haven’t finished the process of evaluation. The next step is to generalise it and apply it to new problems. So, we can do this by saying our recipe template and general algorithm for cooking can be applied to any cooking setting with a few little tweaks to the specifics so for example baking a cake.

So, in other words, if we follow the structure or the skeleton of this process we can actually apply it to any cooking scenario. That was lots of talking and lots of information. So, I hope from that description you can start to understand that evaluation is the culmination of all six computational thinking skills. So, here’s actually a quick recap in visual format.

[Image on the slide shows text appearing over the pizza picture: Decomposition, Abstraction, Algorithms, Pattern Recognition, Modelling and Simulation, Evaluation]

Number one, we have the problem. From here we use our decomposition and abstraction skills to actually break it down and make sense of it. Two, we write our algorithm. So, using the skills of both algorithms and pattern recognition to start bringing in looping. Three, we then test our algorithm through the skill of modelling and simulation. And four, we finally have our solution which we then evaluate for accuracy and efficiency.

Now, evaluation is actually an essential skill in all jobs.

[Image on the slide shows the text disappearing and a picture of a world globe appearing next to the slice of pizza]

However, we’re going to focus on one that’s quite prominent and that’s a career as a GIS Analyst. Now GIS Analysts routinely deal with large sets of data to process and analyse and put it into user focussed displays such as graphics, maps and charts. Now, this work requires extensive knowledge and experience with GIS, so Geographic Information Systems, technology principles, as well as proficiency with computers, including knowing how to code using Python, HTML, and also the use of Microsoft Office software.

So, how does that job relate to evaluation? Well, we’re actually going to look at it in terms of an algorithm. So, what are the inputs for a GIS Analyst? Well, the data from different sources, such as surveying, GPS, or drones.

What’s the process of a GIS Analyst? Well, that’s the definition of the problem, the criteria, then building the data sets, and then the analysis of those data sets. And then the output of a GIS Analyst is what’s actually given to the client. So, the maps, the charts and the directions. Evaluation occurs at the process and the output stages. So, techniques can be applied to different settings and different industries. Generalisation happens when an algorithm created is actually applied to similar data sets.

[Image changes to show a new slide displaying a classroom computational thinking problem and diagram and on the left the Digital Technologies Key Concepts below the text heading: Evaluation in the Classroom, A Computational Thinking Example]

Once again, we’re going to look at another example to consolidate our learning. So, this is a question from the Bebras Australia Computational Thinking Challenge. It’s a Year 9/10 medium level question.

So, in this question we’re looking at finding an item with asking a limited number of questions. So, the question asks, “How many questions needs to be asked to be sure to have found the item?”. I will get you to pause the video, read through the question and jot down your answer.

OK, for those who have got an answer, the answer is actually three. So, how does this question relate to the skill of evaluation? Well, in order to get the correct answer we actually used a tree search. So, we took the problem, we broke it down into parts, we then followed a set of steps in order to figure out that the least amount of questions to ask was actually three, and then we tested this theory and formed our solution.

How does this example relate to the Digital Technologies Key Concepts? On the left hand side of your screen you can see it relates to digital systems as this question sees us looking at tree searching and using nodes, which were the caves, and branches. Also, interactions, so data and the process such as the tree search, and impacts, looked at sustainability empowerment met through digital technologies. We also used algorithms and followed a set of steps to come up with the answer.
 
[Image changes to show a new slide displaying a photograph of the inside of a computer beneath text heading: Evaluation In The Classroom, Digital Technologies & Cross Curricular Examples]

Moving into the Digital Technologies classroom, as we addressed the Year 9/10 on the previous slide, we’re going to stick with that year band. So, according to ACARA and the Australian Curriculum, a Year 9/10 student according to the Curriculum Achievement Levels, a student that is at standard should be able to recognise modules common to different systems and apply existing modules to new simple systems.

So, an example would be getting students to develop a cyber security strategy. So, here students would also develop a user guide regarding data networks, security and the evaluation of networks, security features and measures and then making recommendations for future improvements.

In a Year 5/6 classroom students could write a programme using Scratch or Blockly that moves a robot from one place in a maze to another. So, a constraint could be that it is to be a small programme. So, one with limited steps, say four steps. So, this is where loops are introduced. According to ACARA and the Australian Curriculum this addresses the at standard achievement level of “Describe and extract elements common to different problems”.

So, some cross-curricular examples could include in the English classroom, looking at debating or writing a persuasive essay, using a PNI or SWOT analysis. And in the Maths and Science classroom, when developing or applying mathematic and scientific formula for solving problems, evaluation actually takes place when the testing and applying to the new question sets.

[Image changes to show a new slide displaying information about the Bebras Australia Thinking Challenge and text and text heading appears: Where To From Here? Bebras Australia Website, https://www.bebras.edu.au/, Digital Careers, Bebras Australia Computational Thinking Challenge, 2019 Dates, 4th – 15th March 2019, 26th August – 6th September 2019]

So, how do we keep our students engaged with computational thinking?

[Image shows a red arrow pointing to the Teacher Registration area on the slide]

You can actually jump on to the bebras.edu.au website and sign up for your students to actually complete next year’s Computational Thinking Challenge. So, if you just head up to Teacher Registration and you can register yourself and your students to complete the challenge.

[Image shows another red arrow appearing and pointing at text: Bebras 365]

You can actually access Bebras 365 through this same web address.

[Image changes to show a slide displaying information about Bebras 365 and with previous Challenge questions from previous years]

So, if you hop on to Bebras 365 you’ll actually see the next screen. So, what is 365? Basically, it’s where we compile all of the previous year’s Challenge questions. So, we haven’t deleted anything, we’ve just put them into a useable source so you can use them as a resource in your classroom.

So, Bebras 365 actually allows you, as I said, to have all of those questions available without the need for a log in or to sign in. So, as a teacher you can pick and choose the type of questions that your students may either be struggling with, so for example, if they don’t understand evaluation, then you can go through all of those year’s Challenge questions and find the evaluation questions and actually get your students to answer those ones. Or, you could use Bebras 365 as preparation for the actual physical Bebras Australia Computational Thinking Challenge.

You can do this by getting them to sit the questions. They become familiar with how the questions are worded, what they look like, and how to answer them. When you and your students are working your way through Bebras 365, there’s just a couple of things you need to keep in mind. So, the questions, unlike that in the Challenge, they’re actually not timed or graded. So, they can have as much time to sit on that question as they need to and they actually won’t get a result at the end of it. So, if they do ten questions they won’t get a result out of ten. It will just be you can continue doing questions, getting them wrong or right as you go along, and they won’t be, there won’t be a score at the end of it.

So, as I said, “Correct” and “Incorrect” will actually display on the page when the students are working through the questions. So, they can kind of work through their solution until they get it correct. The only thing it won’t display is an explanation around why the question is either correct or incorrect. Now, to have a bit more of a valuable learning experience for students, you as the teacher can actually go and download that year’s Solutions Guide.

So, for example if we were working out of the 2016 Challenge, we’re doing a couple of questions out of that, all I would need to do as the teacher is to down the, download the 2016 Solutions Guide and there’ll be an explanation of all of the answers aligned with all of the questions and it shows you how the computational thinking skills are actually aligned for that question as well.

[Image changes to show a photograph looking down on a male student looking at a book and a female student working on a laptop and sitting back to back beneath the text heading: Where To From Here? Events and Programs, https://www.digitalcareers.edu.au/events/]

If you jump on to the Digital Careers website, here you’ll be able to see an events calendar. So, anything that we’ve got running, any webinars that we have happening and any programmes and activities that we actually support. You can also access information on the other CSIRO Education and Outreach Programmes that you can implement into your classroom.

[Image changes to show a new slide displaying the CSIRO logo, text heading and text: Questions and Thank You, Education & Outreach, Allira Crowe, Digital Careers – CSIRO Education & Outreach, digitalcareers@csiro.au, www.csiro.au]

I hope that you’ve found this session helpful and that it’s assisted with demystifying the skills of algorithm and evaluation. If you do have any questions after viewing this, please feel free to send us an email at the address you see on your screen. Thank you for joining me.

[Music plays and the CSIRO logo and text appears: CSIRO Australia’s innovation catalyst]

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Computational thinking skills

CSIRO Digital Careers believes that every Australian requires foundational computational thinking skills to thrive in the future world of work. Here we present six key computational thinking skills that are aligned to the Australian Curriculum Digital Technologies.

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14 MAY 2019

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How well do you know your computational thinking skills?

According to ACARA, what is the Decomposition computational thinking skill?
According to ACARA what is the Pattern Recognition computational thinking skill?
According to ACARA what is the Abstraction computational thinking skill?
According to ACARA what is the Algorithms computational thinking skill?
According to ACARA, what is the Evaluation computational thinking skill?
According to ACARA, what is the Modelling and Simulation computational thinking skill?

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Digital Translator

OOP

OOP stands for Object-Oriented Programming. It is a programming language that supports the object oriented programming paradigm. In object oriented programming, objects represent a combination of data (the attributes of an object) and actions that can be performed on or with those data (the methods of the object). An example might be a declaration of a 'car', which has attributes that describe its physical nature (such as the number of doors, its colour, the size of the engine) and the actions it can perform (such as accelerating, braking and turning).

The valid attributes and methods of an object are defined by its class, and these attributes and methods can be inherited from the definition of another class. Examples of OOP languages include C++, Eiffel, Java, Python and Scala.