Exam Writing Format & Strategy

Exam Rules Recap

Time management:

• 2025: ~20 marks in 55 min = ~2.75 min per mark
• 2024: ~30 marks in 55 min = ~1.83 min per mark
• Rule of thumb: 1 mark ≈ 2-3 minutes. If a question is worth 2 marks, spend ~5 minutes max.

The 5-Minute Reading Period Strategy

During the 5-minute reading time (no writing allowed):

1. Scan ALL questions — count marks, identify topics
2. Identify the calculation question (CNN dimensions) — mentally plan the steps
3. Identify the diagnosis question (bias/variance from curves) — start forming your answer
4. Plan your time allocation — more time on high-mark questions
5. Identify what you need from your cheat sheet — locate formulas you'll need

Answer Formatting Rules

Rule 1: Lead with the Answer

💡 中文思维习惯："先铺垫再给结论"。英文学术写作相反：先给结论，再解释原因。这是中国学生最需要改变的习惯。

❌ "There are many factors to consider. First, we need to think about..."
✅ "No, training for 2000 epochs will not help because the model is already overfitting."

Rule 2: Be Concise — Quality Over Quantity

💡 不要写"废话"凑字数。老师明确说了 "quality over quantity"。用3句精确的话比写半页模糊的要好。

❌ (half page of vague general knowledge)
✅ "This is an overfitting scenario (train 95%, val 60%). L2 regularisation will help 
    because it penalises large weights, promoting a simpler model that generalises better."

Rule 3: Link to the Specific Scenario

💡 不要只写"正则化能防止过拟合"这种教科书式的回答。必须引用题目给的具体数字（如 train=95%, val=60%）来支撑你的判断。

❌ "Regularisation helps prevent overfitting." (too generic)
✅ "Since the training accuracy (95%) is much higher than validation (60%), 
    indicating overfitting, L2 regularisation is likely to help by constraining 
    model complexity." (linked to given numbers)

Rule 4: Show Calculation Steps

💡 计算题一定要写出公式和代入过程，不能只写最终答案。即使算错了，步骤正确也能拿部分分。

❌ "The output is 16x16x10"
✅ "Conv output: ((50 + 2×0 - 5) / 3) + 1 = (45/3) + 1 = 15 + 1 = 16
    Output: [16, 16, 10] (10 from number of filters)"

Rule 5: For "What Do You Think?" — Go Beyond Numbers

💡 "你怎么看"这类题不能只算数字，要解释数字背后的含义。模型到底在做什么？为什么会这样？

❌ "Accuracy is 60% and recall is 100%."
✅ "The accuracy is 60% and recall is 100%. This means the model predicts almost 
    everything as positive — it catches all actual positives (perfect recall) but 
    at the cost of many false positives (precision only 56%). The model appears 
    to be performing well at detecting positives, but is actually just classifying 
    nearly everything as positive."

Answer Templates by Question Type

Type: "Evaluate This Suggestion" (2 marks each)

[YES/NO — 1 mark]
[Reasoning connected to scenario — 1 mark]

Template:

[Yes/No], [suggestion] is [likely/unlikely] to improve the validation accuracy. 
The model is currently [overfitting/underfitting] (training accuracy [X]%, 
validation accuracy [Y]%). [Suggestion] [mechanism: e.g., "penalises large weights" / 
"adds more training data"] which [helps/does not help] with [overfitting/underfitting] 
because [specific reason].

Type: "Calculate + Interpret" (Confusion Matrix)

Step 1: State formulas
Step 2: Plug in numbers
Step 3: Give result
Step 4: Interpret (what does the model actually DO?)
Step 5: Explain WHY (class imbalance? threshold too low?)

Type: "Explain Concept" (2-4 marks)

Paragraph 1: WHAT it is (definition)
Paragraph 2: HOW it works (mechanism)
Paragraph 3: WHY it matters (benefit/purpose)

Type: CNN Calculation (show workings)

For each layer:
  Input:   [H, W, C]
  Formula: ((H + 2p - f) / s) + 1 = ...
  Output:  [H', W', C']

Final: Flatten = H' × W' × C' = [number]

Time Allocation Guide

For a 20-mark exam (2025 format):

For a 30-mark exam (2024 format):

Last-Minute Reminders

1. Read the scenario carefully — the numbers matter (train acc, val acc, missing values count)
2. Diagnose BEFORE prescribing — always state overfitting/underfitting first
3. Show your work on calculations — partial marks are possible
4. Cross out wrong work — the exam says "cross out work you don't want assessed"
5. Write clearly — illegible answers get 0 marks
6. Use overflow pages if needed, but note which question on the original page

中国学生考试写作常见问题（Common Issues for Chinese Students）

问题 1：先写"背景"再给"结论"

❌ 中文习惯："首先，正则化是一种技术……它的作用是……所以我认为……"
✅ 英文习惯："Yes, L2 regularisation will help. This is because..."

→ 英文考试要求：第一句话就给结论，然后解释原因。

问题 2：过度使用 "can"

❌ "Regularisation can help to improve the model."
✅ "Regularisation is likely to improve validation accuracy by constraining model complexity."

→ "can" 太弱。用 "is likely to" 或 "will" 更果断、更学术。

问题 3：缺少因果连接

❌ "The model is overfitting. We should use dropout."（两句话之间没有逻辑连接）
✅ "The model is overfitting, as evidenced by the gap between training and validation accuracy. Therefore, applying dropout is likely to help by reducing co-adaptation."

→ 用 "as evidenced by"、"therefore"、"because" 把句子连起来。

问题 4：用中文直译

❌ "The model learned too good on the training data"
✅ "The model fits the training data too closely"

❌ "The performance is not good enough"
✅ "The model fails to generalise to unseen data"

❌ "We can use a more big model"  
✅ "Increasing the model size would help"

问题 5：不敢下判断

❌ "Maybe this suggestion could possibly help..."
✅ "Yes, this suggestion is likely to improve validation accuracy."

→ 考试要明确 YES/NO。模糊的回答拿不到分。