A Level CompSci

(a) The Arithmetic and Logic Unit; ALU, Control Unit and Registers (Program Counter; PC, Accumulator; ACC, Memory Address Register; MAR, Memory Data Register; MDR, Current Instruction Register; CIR). Buses: data, address and control: how this relates to assembly language programs. (b) The Fetch-Decode-Execute Cycle; including its effects on registers. (c) The factors affecting the performance of the CPU: clock speed, number of cores, cache. (d) The use of pipelining in a processor to improve

(a) The differences between and uses of CISC and RISC processors. (b) GPUs and their uses (including those not related to graphics). (c) Multicore and Parallel systems

(a) How different input, output and storage devices can be applied to the solution of different problems. (b) The uses of magnetic, flash and optical storage devices. (c) RAM and ROM. (d) Virtual storage

(a) The nature of applications, justifying suitable applications for a specific purpose. (b) Utilities. (c) Open source vs closed source. (d) Translators:Interpreters, compilers and assemblers. (e) Stages of compilation (lexical analysis, syntax analysis, code generation and optimisation). (f) Linkers and loaders and use of libraries

(a) Understand the waterfall lifecycle, agile methodologies, extreme programming, the spiral model and rapid application development. (b) The relative merits and drawbacks of different methodologies and when they might be used. (c) Writing and following algorithms.

(a) Need for and characteristics of a variety of programming paradigms. (b) Procedural languages. (c) Assembly language (including following and writing simple programs with the Little Man Computer instruction set). See appendix 5d. (d) Modes of addressing memory (immediate, direct, indirect and indexed). (e) Object-oriented languages (see appendix 5d for pseudocode style) with an understanding of classes, objects, methods, attributes, inheritance, encapsulation and polymorphism.

(a) Lossy vs Lossless compression. (b) Run length encoding and dictionary coding for lossless compression. (c) Symmetric and asymmetric encryption. (d) Different uses of hashing.

(a) Lossy vs Lossless compression. (b) Run length encoding and dictionary coding for lossless compression. (c) Symmetric and asymmetric encryption. (d) Different uses of hashing.

(a) Characteristics of networks and the importance of protocols and standards. (b) The internet structure: • The TCP/IP Stack. • DNS • Protocol layering. • LANs and WANs. • Packet and circuit switching. (c) Network security and threats, use of firewalls, proxies and encryption. (d) Network hardware. (e) Client-server and peer to peer.

(a) HTML, CSS and JavaScript. See appendix 5d. (b) Search engine indexing. (c) PageRank algorithm. (d) Server and client side processing.

(a) Arrays (of up to 3 dimensions), records, lists, tuples. (b) The following structures to store data: linked-list, graph (directed and undirected), stack, queue, tree, binary search tree, hash table. (c) How to create, traverse, add data to and remove data from the data structures mentioned above. (NB this can be either using arrays and procedural programming or an object-oriented approach).

(a) Define problems using Boolean logic. See appendix 5d. (b) Manipulate Boolean expressions, including the use of Karnaugh maps to simplify Boolean expressions. (c) Use the following rules to derive or simplify statements in Boolean algebra: De Morgan’s Laws, distribution, association, commutation, double negation. (d) Using logic gate diagrams and truth tables. See appendix 5d. (e) The logic associated with D type flip flops, half and full adders.

(a) The Data Protection Act 1998. (b) The Computer Misuse Act 1990. (c) The Copyright Design and Patents Act 1988. (d) The Regulation of Investigatory Powers Act 2000.

The individual moral, social, ethical and cultural opportunities and risks of digital technology: • Computers in the workforce. • Automated decision making. • Artificial intelligence. • Environmental effects. • Censorship and the Internet. • Monitor behaviour. • Analyse personal information. • Piracy and offensive communications. • Layout, colour paradigms and character sets.

(a) The nature of abstraction. (b) The need for abstraction. (c) The differences between an abstraction and reality. (d) Devise an abstract model for a variety of situations.

(a) Identify the inputs and outputs for a given situation. (b) Determine the preconditions for devising a solution to a problem. (c) The nature, benefits and drawbacks of caching. (d) The need for reusable program components.

(a) Identify the components of a problem. (b) Identify the components of a solution to a problem. (c) Determine the order of the steps needed to solve a problem. (d) Identify sub-procedures necessary to solve a problem.

(a) Identify the points in a solution where a decision has to be taken. (b) Determine the logical conditions that affect the outcome of a decision. (c) Determine how decisions affect flow through a program.

(a) Determine the parts of a problem that can be tackled at the same time. (b) Outline the benefits and trade offs that might result from concurrent processing in a particular situation.

(a) Programming constructs: sequence, iteration, branching. (b) Recursion, how it can be used and compares to an iterative approach. (c) Global and local variables. (d) Modularity, functions and procedures, parameter passing by value and by reference. (e) Use of an IDE to develop/debug a program. (f) Use of object oriented techniques.

(a) Features that make a problem solvable by computational methods. (b) Problem recognition. (c) Problem decomposition. (d) Use of divide and conquer. (e) Use of abstraction. (f) Learners should apply their knowledge of: • backtracking • data mining • heuristics • performance modelling • pipelining • visualisation to solve problems.

(a) Analysis and design of algorithms for a given situation. (b) The suitability of different algorithms for a given task and data set, in terms of execution time and space. (c) Measures and methods to determine the efficiency of different algorithms, Big O notation (constant, linear, polynomial, exponential and logarithmic complexity). (d) Comparison of the complexity of algorithms. (e) Algorithms for the main data structures, (stacks, queues, trees, linked lists, depth-first (post-order) and