IB Maths AI HL Further Differentiation Paper 1 & 2 ~6 min read

Second Order Derivatives

Differentiate once and you get the gradient. Differentiate the gradient and you get the second derivative — the rate of change of the rate of change. It tells you how the gradient itself is shifting, which is exactly what you need to classify stationary points and read a curve’s concavity in the topics ahead.

📘 What you need to know

What the notation means

The first derivative is the gradient; the second is the gradient’s gradient. Watch where the superscript 2’s go — they sit differently in the two parts of the fraction.

First and second derivative notation dydx = f(x)  (first)  ·  ydx² = f(x)  (second) ✗ notation to recognise, not a booklet formula

🧠 “d-two-y over d-x-squared”

You differentiate twice (so d²) with respect to x twice (so x²). The 2 sits up top with the d, but down below with the x — that asymmetry is the thing to memorise.

DerivativeWhat it measures
f(x)the value of the function
f(x) (first)the gradient — the rate of change of f
f(x) (second)the rate of change of the gradient
Why it’s useful: the second derivative lets you test for local minimum and maximum points, decide the nature of stationary points, determine the concavity of a curve, and help sketch the graph of the derivative — all coming up in the next two topics.

Finding a second derivative

🧭 Recipe — second derivative

  1. Rewrite roots and fractions as negative/fractional powers of x.
  2. Differentiate once to get f(x) — applying chain/product/quotient rules as needed.
  3. Differentiate again to get f(x), working carefully term by term.
  4. Evaluate or simplify if asked — e.g. tidy surds by rationalising the denominator.

🤔 Why are negative powers so error-prone here?

Each differentiation drops the power by 1, so a term like x−1/2 becomes x−3/2, then x−5/2 — the exponents get more negative and the coefficients pick up extra factors each time. Two passes means twice the chance to slip a sign or mishandle the fraction, so the safest approach is one clean term at a time.

Worked examples

Throughout, f(x) = 4 − √x + 3x.

WE 1

Rewrite f(x) as powers of x

Convert the roots before differentiating.

√x = x^(1/2), 3√x = 3x^(−1/2) f(x) = 4 − x^(1/2) + 3x^(−1/2)
WE 2

Find f(x)

Differentiate once, term by term.

4 → 0 −x^(1/2) → −½x^(−1/2) 3x^(−1/2) → 3·(−½)x^(−3/2) = −32x^(−3/2) f′(x) = −½x^(−1/2) − 32x^(−3/2)
WE 3

Find f(x)

Differentiate f(x) again. Watch the negatives.

−½x^(−1/2) → −½·(−½)x^(−3/2) = ¼x^(−3/2) 32x^(−3/2) → −32·(−32)x^(−5/2) = 94x^(−5/2) f″(x) = ¼x^(−3/2) + 94x^(−5/2)
WE 4

Evaluate f(3) in the form ab

Write as surds, substitute x = 3, then rationalise.

f″(x) = 14x√x + 94x²√x f″(3) = 112√3 + 936√3 = 1236√3 = 13√3 rationalise: 13√3 × √3√3 = √39 f″(3) = 19√3
WE 5

Find f(x) for f(x) = 2x3 − 5x2 + 4x − 1

A polynomial — straightforward double differentiation.

f′(x) = 6x² − 10x + 4 f″(x) = 12x − 10 f″(x) = 12x − 10

💡 Top tips

⚠ Common mistakes

Next up — Stationary Points. With both first and second derivatives in hand, you can now do more than just find stationary points (where f(x) = 0) — you can classify them. The next topic uses the sign of f(x) to decide instantly whether a turning point is a local minimum, a local maximum, or something subtler.

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