7.0 Module Overview: Algorithm Design and Problem-Solving

Use this module to build one skill at a time, then apply it in a different situation. Read each worked example, explain its steps and attempt the exercises without copying the example. The mixed review is for retrieval now and after a delay.

Lessons

• Program Development Life Cycle (Analysis, Design, Coding, Testing)
• System Decomposition & Structure Charts
• Cambridge Pseudocode Standards & Identifier Tables
• Flowchart Symbols & Decision Logic
• Totalling, Counting & Sentinel Input Loops
• Finding Maximum, Minimum & Calculating Average
• Mean and Empty Data
• Linear Search Algorithm on 1D Arrays
• Bubble Sort Algorithm with Swap Optimization
• Data Validation Checks (Range, Length, Type, Presence)
• Data Verification Methods (Double Entry & Visual Check)
• Designing Test Data (Normal, Boundary, Extreme, Erroneous)
• Trace Tables & Algorithm Dry-Run Execution
• Error Detection, Debugging & Algorithm Correction
• Mixed Practice: Algorithms
• Independent Scenario: Monitoring Readings

Study and feedback

Reading theory is open. Interactive access depends on your account and module access; the AI Tutor requires eligible All-Access membership. Structured checks provide feedback on specific answers. Written explanations and diagrams need comparison with the worked guidance and, where possible, a teacher's feedback. Passing a check is not a prediction of an exam grade.

Programming before algorithms

Beginners should study module 8 through arrays before attempting module 7's search/sort programs. The module navigation places programming before algorithm practice; syllabus numbers remain available for reference. Revisit subroutines, files and the independent scenarios once their prerequisites are secure.