$I$ compact interval, $g:I\rightarrow I,\;g(x)=ax^3+bx^2+cx+d$. Find stationary points, local and global maxima and minima of $g$ on $I$

This shows how to implement a recursive function in JavaScript with a nested function definition.

This uses an embedded Geogebra graph of a line $y=mx+c$ with random coefficients set by NUMBAS.

The student is asked to write a number with a certain property, or tick a box labelled "this is impossible" if it can't be done.

A custom marking algorithm on the gap-fill part first checks if the student ticked the box. If they did, their answer is marked correct if it really is impossible. If they didn't tick it, their number is checked against the required property.

The student is shown two number entry gaps on either side of a 'less than' sign. Their answer is marked correct if the first number is less than the second, using a custom marking algorithm.

This shows how to mark the gaps in a gap-fill part together, rather than independently.

A diagram showing the difference between lines, rays and segments in Eukleides.

Shows how to use the "random person" extension to pick random names for people in your questions.

This question shows how to load a GeoGebra applet from geogebra.org.

This question randomly generates a list of 100 items, each chosen from a list of strings.

It then computes the number of occurrences of each distinct item in the list. The statement shows the frequencies in a table.

This shows how to define a question variable whose value is a variable name with a few annotations added, so it's more convenient to use.

The question variable 'x' is defined to be the variable name vec:underline:x.

The student is given a value of $\cos(\theta)$ and has to find $\theta$.

Shows how to use subexpressions to represent randomly-chosen fractions of $\pi$ and surds, and have them displayed nicely.

Shows how to create a simplified JME subexpression, and substitute it into a string variable.

To prevent students from giving a trivial answer for a part which is used later in adaptive marking, you can consider it as invalid.

Part a of this question has a custom marking algorithm which marks an answer of zero as invalid. Any other answer is used in adaptive marking for part b.

Use the bareMatrices display flag to render a matrix without wrapping it in parentheses.

A demonstration of how to use the "variable list of choices" option for a "choose one from a list" part to shuffle only some of the choices, and always have the same "I don't know" choice at the end of the list.

This question uses a "formatted text template" variable to define a long passage of text which is shown to the student after they submit a part. A custom marking algorithm adds the text as a comment after the standard marking algorithm has finished.

This question uses the linear algebra extension to generate a system of linear equations which can be solved.

We want to produce an equation of the form $\mathrm{A}\mathbf{x} = \mathbf{y}$, where $\mathrm{A}$ and $\mathbf{y}$ are given, and $\mathbf{x}$ is to be found by the student.

First, we generate a linearly independent set of vectors to form $\mathrm{A}$, then freely pick the value of $\mathbf{x}$, and calculate the corresponding $\mathbf{y}$.

To generate $\mathrm{A}$, we generate more vectors we need, then pick a linearly independent subset of those using the subset_with_dimension function.

The gap-fill part in this question is only marked correct if both gaps are correct.

The feedback from the individual gaps is not shown.

A custom marking algorithm for a JME part estabishes whether the student's answer is equivalent to the expected answer, up to an arbitrary constant factor.

This question shows how to use the 'adviceDisplayed' signal to run some javascript when the question's advice is revealed to the student.

This question includes a JavaScript preamble which defines 'hbar' as a special variable name to be rendered in LaTeX as \hbar.

A pre-class test for week 2.

This question assesses the learner's ability to compute a confidence interval for a difference of two means, given two samples.

Randomized variables: sample raw data and confidence level. 

Adapted from section 7.3.1 in OpenIntro Statistics (4th ed).

Logarithmic differentiation question with customised feedback to catch some common errors.

Partial differentiation question with customised feedback to catch some common errors.

Product rule differentiation question with customised feedback to catch some common errors.