Computer Science

Understanding computers

Skills

Computer system components

• I can distinguish between hardware and software components in computer systems (Y8)
• I can identify and categorise input, output and storage devices for different scenarios (Y8)
• I can draw block diagrams showing the main components of a computer system (Y8)
• I can explain the relationships between different computer components (Y8)

Central processing unit and memory

• I can distinguish between main memory (RAM) and permanent storage devices (Y8)
• I can explain the three stages of the fetch-decode-execute cycle (Y8)
• I can understand how processor speed affects computation time (Y8)
• I can interpret processor speeds measured in Hz, kHz, MHz and GHz (Y8)

Binary representation and data

• I can explain why computers use binary to represent all data (Y8)
• I can convert integers to binary numbers and binary numbers to integers (Y8)
• I can define and use storage units including bit, byte, KB, MB and GB (Y8)
• I can perform simple binary addition calculations (Y8)
• I can identify binary numbers as odd or even using pattern recognition (Y8)

Character representation and ASCII

• I can explain how text characters are represented using ASCII code (Y8)
• I can convert between ASCII codes and characters using lookup tables (Y8)
• I can understand the relationship between binary representation and character encoding (Y8)

Storage technologies

• I can compare the typical capacities, strengths and weaknesses of different storage devices (Y8)
• I can name and distinguish between three types of optical storage device (Y8)
• I can describe how data is stored on optical discs using pits and lands (Y8)
• I can explain how laser technology enables data reading on optical media (Y8)

Technology evolution and convergence

• I can trace the history and development of communication technologies (Y8)
• I can understand how modern devices combine multiple technologies through convergence (Y8)
• I can discuss Moore's Law and its impact on technological advancement (Y8)
• I can evaluate different applications of emerging technologies (Y8)

• Understand the fundamental structure of computer systems including input, processing, output and storage components
• Recognise the difference between physical hardware components and software applications
• Know that RAM provides temporary storage for currently running programs and data
• Understand that the CPU executes instructions through a continuous fetch-decode-execute cycle
• Recognise how processor speed measured in hertz relates to computational performance
• Understand that binary (base 2) is the fundamental number system used by all digital computers
• Know the hierarchy of storage units from bits through to gigabytes and beyond
• Apply techniques for converting between binary and decimal number systems
• Understand that ASCII provides a standard method for representing text characters in binary
• Compare magnetic, optical and solid-state storage technologies and their applications
• Know that optical discs store data as patterns of pits and lands read by laser light
• Understand how communication technologies have evolved from simple signals to complex digital systems
• Recognise that device convergence has combined many separate technologies into single devices
• Understand Moore's Law as an observation about the pace of technological advancement
• Evaluate the potential applications and implications of emerging technologies including robotics, AI and 3D printing

This unit provides students with essential foundational knowledge about computer systems and digital representation, building systematically from hardware components through to data encoding and storage technologies. Building on the digital literacy skills developed in Y7.1 Using Computers Safely, Effectively and Responsibly and the file management concepts from Y7.2 Spreadsheet Modelling, students now develop a deeper technical understanding of how computer systems function at a fundamental level.

The unit begins with concrete, observable components that students can identify and categorise, establishing clear mental models of how computer systems are structured. Students learn to distinguish between hardware and software, building on their practical experience of using different devices and applications. The progression from identifying individual components to understanding their relationships through block diagrams develops systems thinking skills that are essential for computational understanding. This foundation directly prepares students for more advanced topics in subsequent units, particularly Y8.2 Experience AI where understanding of processing and data representation becomes crucial.

The central focus on the CPU and memory systems introduces students to the fundamental concepts of how computers process information. The fetch-decode-execute cycle provides a concrete model for understanding computational processes, while the exploration of processor speeds connects abstract concepts to observable performance differences. The distinction between RAM and permanent storage addresses common misconceptions about computer memory and establishes clear understanding of data persistence. These concepts are essential preparation for programming units later in Y8 and Y9, where students will need to understand how their code is executed and how data is managed.

The comprehensive coverage of binary representation and number systems develops crucial computational thinking skills while addressing key National Curriculum requirements. Students learn not just how to convert between number systems, but why binary is fundamental to digital computing. The progression from basic conversion through to binary arithmetic and ASCII representation demonstrates the practical applications of these abstract concepts. This mathematical foundation supports learning across multiple units, including the logical thinking required for programming and the data representation concepts essential for graphics and multimedia work.

The exploration of storage technologies provides both historical context and practical understanding of data persistence and capacity. Students develop appreciation for the rapid evolution of storage capacity and speed, while learning the physical principles underlying different storage methods. The detailed examination of optical storage introduces students to the relationship between physical properties and digital representation, reinforcing binary concepts through real-world applications. This knowledge supports informed decision-making about technology choices and prepares students for advanced topics in data management and system design.

The concluding focus on technological convergence and emerging technologies encourages students to think critically about the pace of change and future possibilities in computing. By examining how separate technologies have combined into single devices, students develop understanding of how innovation builds upon existing foundations. The introduction to Moore's Law provides a framework for understanding technological progress, while exploration of emerging technologies encourages forward-thinking and creative problem-solving. This broader perspective prepares students to be adaptable technology users and potential innovators, supporting their development as digitally literate citizens who can navigate an rapidly evolving technological landscape.

Experience AI

Skills

AI concepts and classification

• I can distinguish between rule-based and data-driven approaches to problem solving (Y8)
• I can define artificial intelligence and machine learning in computing contexts (Y8)
• I can identify examples of AI applications in everyday life (Y8)
• I can explain how classification works using supervised learning (Y8)

Data and machine learning

• I can describe the role of training data in creating machine learning models (Y8)
• I can explain the relationship between data quality and model accuracy (Y8)
• I can identify different types of machine learning: supervised, unsupervised, and reinforcement learning (Y8)
• I can use machine learning tools to create and test simple classification models (Y8)

Bias and ethics in AI

• I can identify how bias can appear in AI systems and their outputs (Y8)
• I can distinguish between data bias and societal bias in machine learning contexts (Y8)
• I can evaluate the potential benefits and risks of AI applications (Y8)
• I can suggest strategies for reducing bias in AI systems (Y8) 

Model creation and evaluation

• I can follow the stages of the AI project lifecycle to develop solutions (Y8)
• I can create simple machine learning models using classification techniques (Y8)
• I can test machine learning models and calculate their accuracy (Y8)
• I can explain the importance of confidence scores in AI predictions (Y8)

Decision trees and explainability

• I can describe how decision trees work as machine learning models (Y8)
• I can create decision trees using training data and understand their structure (Y8)
• I can explain why decision trees are considered explainable AI (Y8)
• I can use decision tree terminology including nodes, features, and leaf nodes (Y8)

AI applications and career awareness

• I can create model cards to document and explain machine learning models (Y8)
• I can identify career opportunities in AI and machine learning fields (Y8)
• I can evaluate the appropriateness of AI solutions for different problems (Y8)
• I can discuss the societal implications of AI development and deployment (Y8)

• Understand that artificial intelligence uses data-driven approaches rather than rule-based programming
• Recognise that machine learning is a subset of AI that creates models from data
• Know the three main types of machine learning: supervised, unsupervised, and reinforcement learning
• Understand that classification is a key application of supervised learning
• Recognise that training data must be labelled by humans for supervised learning to work
• Understand that bias in AI systems can arise from biased training data or societal inequalities
• Know that decision trees are explainable AI models that use features to make classifications
• Understand the AI project lifecycle from problem definition through to model evaluation
• Recognise that machine learning models make predictions with associated confidence scores
• Know that different AI applications require different types of models and approaches
• Understand that model cards document important information about AI systems
• Recognise the wide range of career opportunities in AI development and application
• Understand that AI systems can have both beneficial and harmful societal impacts
• Know that accuracy and bias are key considerations when evaluating AI systems
• Recognise that AI applications are increasingly used across multiple industries and contexts

This unit provides learners with comprehensive foundational knowledge about artificial intelligence and machine learning, building systematically from basic concepts through to hands-on model creation and ethical considerations. Building on the technical understanding developed in Y8.1 Understanding Computers, where learners learned about computer systems and data representation, this unit now introduces them to how computers can learn from data rather than simply following programmed instructions. The unit also extends the AI awareness introduced in Y7.5 AI & Machine Learning, providing deeper technical understanding and practical experience with machine learning tools and concepts.

The unit begins with fundamental distinctions between rule-based and data-driven approaches to problem-solving, helping learners understand why AI represents a significant departure from traditional programming. Through the engaging "intelligent piece of paper" activity, learners discover that following algorithmic rules does not constitute true intelligence, setting the stage for understanding how machine learning systems can adapt and learn from data. This conceptual foundation is essential for helping learners move beyond anthropomorphic views of AI towards a more nuanced understanding of artificial intelligence as a field of computer science. The progression from simple rule-following to complex pattern recognition in data prepares learners for the more sophisticated programming concepts they will encounter in Y8.6 Introduction to Python and later units.

The comprehensive exploration of machine learning types and applications demonstrates the breadth and practical relevance of AI technologies in modern society. Learners learn about supervised, unsupervised, and reinforcement learning through accessible examples and video content, while gaining hands-on experience with classification tasks using real-world scenarios. The focus on classification as a key machine learning application provides concrete understanding of how AI systems make predictions and decisions. This practical experience with machine learning tools and concepts directly supports the computational thinking skills that will be essential for Y9.4 Computational Thinking & Logic, while also preparing learners for the more advanced programming challenges they will face in Y9.3 (Re)Introduction to Python and Y9.5 Python Next Steps.

The hands-on experience with creating and evaluating machine learning models provides learners with practical skills in the AI development lifecycle. Using tools like Machine Learning for Kids, learners experience the complete process from problem definition through data preparation, model training, and evaluation. They learn to calculate accuracy, understand confidence scores, and critically assess model performance. This practical experience with the iterative nature of machine learning development reinforces the problem-solving methodologies introduced in Y7.2 Spreadsheet Modelling and provides essential preparation for the programming projects they will undertake in later units. The emphasis on testing and evaluation also connects to the systematic approaches to quality assurance that learners will encounter in their programming work.

The exploration of decision trees as explainable AI models introduces learners to the important concept of algorithmic transparency and accountability. Learners learn to create, interpret, and evaluate decision trees, understanding how these models make classifications and why they are considered more explainable than other AI approaches. This focus on explainability introduces crucial concepts about algorithmic accountability that are increasingly important in AI development. The systematic approach to creating decision trees from training data reinforces logical thinking skills and provides a bridge between the abstract concepts of machine learning and the concrete structures learners will encounter in programming. This foundation in structured decision-making processes directly supports the logical reasoning skills that will be essential for Y9.4 Computational Thinking & Logic and the algorithmic thinking required for advanced programming units.

The unit concludes with career exploration and model documentation activities that help learners understand the professional applications of AI knowledge and the importance of responsible AI development. Through creating model cards and exploring AI careers, learners gain insight into the diverse roles available in the growing AI industry while understanding the professional standards and ethical considerations that guide responsible AI development. This career awareness component helps learners connect their learning to potential future pathways while emphasising the importance of interdisciplinary collaboration in AI development. The focus on documentation and communication skills also supports the technical communication abilities that will be essential for project work in later units, particularly Y8.4 HTML and Website Development and the collaborative programming projects in Y9 units.