Recap
- A data abstraction gives a collection of data a name without showing how it is represented.
- A list is an ordered sequence of elements; each element has an index. In the exam notation the first index is 1.
[v1, v2, v3]creates a list;[]creates an empty list. A list is one value, so a variable can hold it.- A list can mix types, for example
["Aroha", 15, true]. - An index less than 1 or greater than the length gives an error and the program terminates.
- Data abstraction manages complexity: one name for many values, code that adapts when data grows, easier to develop and maintain.
Key relationships
| Idea | Notation or rule | Example |
|---|---|---|
| Create a list | aList ← [value1, value2, ...] |
scores ← [12, 15, 9] |
| Read an element | aList[i] |
scores[2] is 15 |
| Last index | LENGTH(aList) |
scores[LENGTH(scores)] is the last element |
| Empty list | aList ← [] |
LENGTH([]) is 0 |
| Invalid index | Below 1 or above LENGTH |
Error; program terminates |
The four-part explanation
When asked how an abstraction manages complexity:
- Name it (the list
playlist). - Say what it holds (all the song titles).
- Point to where it is used (the loop that plays each song).
- Say what the program would need without it (a variable and a line of code per song) and why that is worse.
Mistakes to avoid
- Counting from 0 in exam questions.
- Using
LENGTH(aList) + 1as an index. The last valid index isLENGTH(aList). - Writing “it makes the code easier” with nothing specific.
- Forgetting that elements in one list can have different types.
- Treating a list as several variables rather than one value.
Worked mini-example
colours ← ["red", "green", "blue"]
DISPLAY(LENGTH(colours))
DISPLAY(colours[2])
DISPLAY(colours[LENGTH(colours)])Output: 3 green blue
LENGTH gives 3; colours[2] is the second element, green; colours[LENGTH(colours)] is colours[3], blue. colours[4] would end the program with an error.
Quick self-check
- What index does the first element have?
- What does
LENGTH([5, 6, 7, 8])evaluate to? - Give two ways data abstraction helps manage complexity.
Answers
-
-
- Any two: fewer names to track; code does not change when the amount of data changes; easier to develop and maintain; related data treated as one value.
Where marks are won and lost
Two kinds of question dominate. The first gives a list and asks what an index expression evaluates to, or whether the program will stop with an error. The second asks you to explain how a list or other abstraction manages complexity. The explanation earns marks only when it is specific: say what the list holds, where the program uses it, and what would be harder without it. In the Create task, the written response about your list asks for exactly this reasoning, applied to your own code.
Back to the full study guide for worked examples and explanations.