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neurologyYou need to know

• Abstraction reduces a problem by removing unnecessary detail and retaining the information needed to create a solution.
• Decomposition breaks a complex problem into smaller, manageable sub-problems that can be solved and tested separately.
• Algorithmic thinking involves defining a logical, ordered sequence of steps that solves a problem.
• Computational thinking uses abstraction, decomposition and algorithmic thinking together to define and refine problems.
• An input is data supplied to a system, a process is an operation performed on the data, and an output is the resulting information or action.
• A structure diagram shows how a problem is decomposed into smaller sub-problems and how those sub-problems are linked.
• An algorithm is a precise, ordered and finite sequence of instructions for solving a problem.
• Algorithms can be represented using pseudocode, flowcharts, an exam reference language or a high-level programming language.
• In a flowchart, a terminal represents the start or end, a parallelogram represents input or output, a rectangle represents a process, a diamond represents a decision, and a predefined-process symbol represents a subprogram.
• Flow lines in a flowchart use arrows to show the order in which instructions are executed.
• Selection chooses which instructions to execute according to whether a condition is true or false, while iteration repeats instructions while or until a condition is met or for a specified number of times.
• Nesting occurs when one selection or iteration structure is placed inside another selection or iteration structure.
• A syntax error occurs when code breaks the grammatical rules of a programming language and usually prevents the program from running or translating successfully.
• A logic error occurs when a program runs but produces an incorrect or unintended result because the algorithm or implementation is flawed.
• A trace table records how variables, conditions and outputs change as each instruction or iteration of an algorithm is executed.
• A linear search checks items one at a time until the target is found or every item has been checked, and it does not require the data to be sorted.
• A binary search requires sorted data and repeatedly compares the target with the middle item before discarding the half that cannot contain the target.
• A bubble sort repeatedly compares adjacent items and swaps them when they are in the wrong order until a complete pass requires no swaps.
• An insertion sort builds a sorted section by taking each new item and inserting it into its correct position within that section.
• A merge sort repeatedly divides a data set into smaller sections, then merges the sections in order to produce a sorted data set.

• Apply abstraction to a problem by identifying essential information and excluding details that do not affect the solution.
• Decompose a problem into clearly defined sub-problems and represent their relationships using a structure diagram.
• Identify the required inputs, processes and outputs for a given computational problem.
• Create, interpret, complete and refine algorithms using pseudocode, flowcharts or a high-level programming language.
• Use the correct flowchart symbols and directional flow lines to represent inputs, outputs, processes, decisions, subprograms and terminals.
• Construct nested selection and iteration so that the control structures execute in the intended order.
• Identify syntax and logic errors in algorithms or code, explain their effects and suggest precise corrections.
• Create and complete a trace table by recording variable values, condition results and outputs in the correct execution order.
• Apply linear search, binary search, bubble sort, insertion sort and merge sort to data sets, showing each comparison, division, insertion, swap or merge as appropriate.
• Identify a standard searching or sorting algorithm from its code, pseudocode or exam reference language and justify the identification using its key steps.