Computer Science

Assessment

A-level: Content and skills for Paper 1 An on-screen examination that tests a candidate’s ability to program, as well as the theoretical knowledge of Computer Science from the following topics: Fundamentals of programming Fundamentals of data structures Fundamentals of algorithms theory of computation Focuses on the designing, writing and testing of programs Available in a wide range of programming languages (C#, Java, Pascal/Delphi, Python (2, 3), VB.Net) A-level: Structure of Paper 1 Prior to the exam, candidates issued with Preliminary Material, the Skeleton Program, and, where appropriate, test data for use in the exam This material will be made available in centres in the autumn term of the second year so that centres have time to prepare students for the exam without it interfering with NEA Students answer series of short questions and write/adapt/extend programs in an Electronic Answer Document provided by AQA Based on the current model that has been successfully used by AQA at AS for several years Includes synoptic and extended response questions A-level: Content and skills for Paper 2 Tests a candidate’s ability to answer questions on the following topics: Fundamentals of data representation Fundamentals of computer systems Fundamentals of computer organisation and architecture Consequences of uses of computing Fundamentals of communication and networking Fundamentals of databases Big Data Fundamentals of functional programming A-level: Structure of Paper 2 Compulsory short answer and extended answer questions Includes synoptic and extended response questions A-level: Example NEA tasks A simulation, e.g. of a business or scientific nature, or an investigation of a well-known problem such as the game of life A solution to data processing problem for an organisation, e.g. membership systems The solution of an optimisation problem, e.g. production of a rota, shortest-path problems, route finding A control system, operated using a device such as an Arduino board A website with dynamic content, driven by a database back-end An app for a mobile phone or tablet Investigating machine learning algorithms Investigating an area of data science using, for example, Twitter feed data or online public data sets