📚 Year 8 SQA Computing: Interdisciplinary Mixed Question Practice | Year 8 SQA 计算机：跨学科综合题型训练
Interdisciplinary mixed questions in computing help you connect coding, data handling and logical thinking with subjects like Maths, Science, Geography, Music and Art. By solving real-world challenges across different disciplines, you learn to decompose problems, recognise patterns and design algorithms that have genuine meaning. This article presents ten engaging practice tasks, each blending computing with another school subject, accompanied by solution approaches and key vocabulary.
跨学科综合题型训练将编程、数据处理和逻辑思维与数学、科学、地理、音乐、艺术等学科联系起来。通过解决不同领域的实际问题，你将在有意义的情境中学会问题分解、模式识别和算法设计。本文提供十道引人入胜的练习，每道题都将计算与另一门学科融合，并附有解题思路和关键术语。
1. What is Interdisciplinary Computational Thinking? | 什么是跨学科计算思维？
Interdisciplinary computational thinking means using core computing skills — abstraction, decomposition, pattern recognition and algorithm design — to investigate questions from other subjects. Instead of learning programming in isolation, you might model a science experiment, sort geographical data, encrypt historical messages or generate musical patterns.
跨学科计算思维意味着运用抽象、分解、模式识别和算法设计等核心计算技能来探究其他学科的问题。编程不再是孤立地学习，你可以模拟科学实验、排序地理数据、加密历史信息或生成音乐模式。
When you approach a cross-curricular challenge, you first identify the key data and the required output. Next, you break the task into smaller steps and look for sequences, selections or repetitions. Finally, you translate those steps into pseudocode or actual code. This practice sharpens both your computing knowledge and your understanding of the other subject.
当你面对跨学科挑战时，首先要明确关键数据和所需输出。然后，把任务分解为更小的步骤，寻找顺序、选择或循环结构。最后，将这些步骤转化为伪代码或实际代码。这样的练习既能加深你对计算的理解，也能巩固你在其他学科的知识。
2. Maths & Programming: Fibonacci Sequence Generator | 数学与编程：斐波那契数列生成器
The Fibonacci sequence appears in nature, art and mathematics. Each term is the sum of the two previous terms: 1, 1, 2, 3, 5, 8, 13, ... Your task is to write a program that displays the first 15 Fibonacci numbers.
斐波那契数列出现在自然界、艺术和数学中。每一项都是前两项之和：1, 1, 2, 3, 5, 8, 13, ... 你的任务是编写一个程序，显示前15个斐波那契数。
Solution approach: Initialise two variables a = 0 and b = 1. Use a counted loop that runs 15 times. Inside the loop, output a, then update the pair: next_term = a + b, a = b, b = next_term. This algorithm closely follows the mathematical recurrence Fₙ = Fₙ₋₁ + Fₙ₋₂.
解题思路：初始化两个变量a = 0和b = 1。使用一个运行15次的计数循环。在循环内部，输出a，然后更新变量组：next_term = a + b，a = b，b = next_term。该算法精确对应数学递推关系 Fₙ = Fₙ₋₁ + Fₙ₋₂。
a = 0
b = 1
for i = 1 to 15
print a
next_term = a + b
a = b
b = next_term
next i
This task reinforces variables, loops and the connection between algebraic formulas and iterative code. It also offers an opportunity to discuss data types and integer overflow when numbers grow large.
该任务强化了变量、循环以及代数公式与迭代代码之间的联系。同时，当数字变得很大时，还可以讨论数据类型和整数溢出问题。
3. Science Simulation: Bacterial Growth Model | 科学模拟：细菌生长模型
In Biology, you study that a single bacterium can divide every 20 minutes. Starting with 1 cell, how many bacteria would exist after 24 hours? Write a program that simulates this exponential growth step by step.
在生物学中，你学习到单个细菌每20分钟分裂一次。从1个细胞开始，24小时后会有多少细菌？编写一个程序，逐步模拟这一指数增长过程。
Analysis: 24 hours equals 72 twenty-minute intervals (3 × 24). Use a variable population = 1 and a loop that repeats 72 times. Each iteration doubles the population: population = population × 2. The final result is 2⁷², a number with 22 digits.
分析：24小时等于72个20分钟间隔 (3 × 24)。使用变量population = 1和一个重复72次的循环。每次迭代使种群翻倍：population = population × 2。最终结果是2⁷²，一个22位数的数字。
population = 1
for hour = 1 to 24
for minute_20 = 1 to 3
population = population * 2
next minute_20
next hour
output population
You could extend the simulation by plotting a growth curve using asterisks or by introducing a carrying capacity limit. This cross-curricular task combines biological concepts with loops and exponential arithmetic.
你可以扩展该模拟，用星号绘制增长曲线或引入环境承载量限制。这道跨学科题目将生物学概念与循环和指数运算结合在一起。
4. Geography & Data Handling: Sorting World Populations | 地理与数据处理：对世界人口排序
Imagine you have collected population data for seven continents (in millions) and need to display the list from largest to smallest. The raw data is shown in the table below.
假设你收集了七大洲的人口数据（百万），需要按从大到小排序并显示。原始数据如下表所示。
| Continent | Population (millions) |
|---|---|
| Asia | 4641 |
| Africa | 1340 |
