Ultimate A-Level Computer Science Podcast

🎧 A-Level Computer Science revision for OCR & AQA students.
⭐ If this podcast helps your revision, leaving a quick rating really helps other students find it.

In this episode, we break down Iterative Testing in the OCR A-Level Computer Science NEA (H446).

You’ll learn:

What iterative testing means and why it’s important

How to test your solution at each stage of development

How to present test data, expected results, and actual outcomes

How to identify issues and refine your solution

What examiners are really looking for in testing evidence

Common mistakes that cost marks

Iterative testing is essential for demonstrating that your solution works correctly and improves over time.

🎯 If you’re working on your NEA, make sure to also listen to the Design and Iterative Development episodes to understand how testing fits into the full development process.

This episode provides an overview of graph traversal algorithms, specifically depth-first search (DFS) and breadth-first search (BFS). It explains how to trace and describe typical applications for each algorithm, including illustrations of their operational steps using a sample graph. The text further details the data structures employed by each algorithm—a stack for DFS and a queue for BFS—and examines their complexity. Finally, it briefly discusses the application of these concepts to tree traversals, highlighting similarities and differences.

This episode provides an overview of merge sort and quick sort algorithms, crucial topics for A Level Computer Science. It begins by explaining the core steps of merge sort, including dividing a list into sublists and then merging them back into a single sorted list, illustrating this with a step-by-step example. The document then calculates the time complexity of merge sort as O(n log2n). Subsequently, it introduces quick sort as another "Divide and Conquer" algorithm, detailing its process of selecting a pivot, partitioning the list, and recursively sorting sublists, also with a visual demonstration. The text concludes by discussing the efficiency of quick sort, noting its best-case time complexity of O(n log n) and a worst-case scenario of O(n^2), particularly when the pivot selection leads to highly unbalanced partitions.

This repisode, provides an overview of sorting algorithms, specifically bubble sort and insertion sort. It highlights the importance of choosing an efficient sorting algorithm due to the potentially large number of items to be sorted. The document explains the mechanics of both bubble sort and insertion sort, offering pseudo-code algorithms and illustrating their processes. Crucially, it analyzes the time complexity of both algorithms, determining that both have a Big-O time complexity of O(n²), although insertion sort is generally faster in practice. The material also touches upon scenarios where simpler sorts, despite being less efficient, might be adequate for small datasets.

🎧 A-Level Computer Science revision for OCR & AQA students.
⭐ If this podcast helps your revision, leaving a quick rating really helps other students find it.

n this episode, we break down the Iterative Development section of the OCR A-Level Computer Science NEA (H446).

You’ll learn:

What iterative development actually means in the NEA

How to structure your development into clear iterations

How to show testing, refinement, and progression

What examiners are really looking for

Common mistakes that cost marks

This builds directly on the Analysis & Success Criteria section, helping you move from planning into development.
🎯 If you’re working on your NEA, make sure to also listen to the previous episode on Analysis & Success Criteria to fully understand how the sections link together.