Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

## Idea

Our colleagues over at the economics department became interested in using R/exams for
generating large-scale exams in their introductory economics courses. However, they face the challenge that
so far they had been writing static exercises and modified them by hand if they wanted to reuse them in a
different exam in another semester. To let R/exams do this job it is illustrated how a static arithmetic
exercise can be turned into a dynamic exercise template either in `num`
format with a numeric solution or into `schoice` format with a single-choice solution.
The idea for the exercise is a very basic price elasticity of demand task:

 Consider the following inverse demand function: $p\left(x\right)=a–b·x$ for the price $p$ given the demanded quantity $x$ . What is the price elastiticy of demand at a price of $p$ ?

The natural candidates for “parameterizing” this exercise are the
price

$p$

and the parameters

$a$

and

$b$

of the inverse demand function.
Based on these the solution is simply:

 First, we obtain the demand function by inverting the inverse demand function: $x=D\left(p\right)=\left(a–p\right)/b$ . Then, at $p$ the price elasticity of demand is $\frac{D‘\left(p\right)}{D\left(p\right)}p=\frac{–1/b}{x}p.$

## Overview

Below various incarnations of this exercise are provided in both R/Markdown `Rmd` and R/LaTeX `Rnw` format.
The following table gives a brief overview of all available versions along with a short description of the idea behind it.
More detailed explanations are provided in the subsequent sections.

# Exercise templates Dynamic? Type Description
1 elasticity1.Rmd
elasticity1.Rnw
No `num` Fixed parameters and numeric solution.
2 elasticity2.Rmd
elasticity2.Rnw
No `schoice` As in #1 but with single-choice solution (five answer alternatives).
3 elasticity3.Rmd
elasticity3.Rnw
Yes `num` Randomly drawn parameters with dynamic computation of correct solution, based on #1.
4 elasticity4.Rmd
elasticity4.Rnw
Yes `schoice` Randomly drawn parameters (as in #3) with dynamically-generated single-choice solution (as in #2), computed by `num_to_schoice()`.
5 elasticity5.Rmd
elasticity5.Rnw
Yes `schoice` As in #4 but with the last alternative: None of the above.

## Static numeric

The starting point is a completely static exercise as it had been used in a previous
introductory economics exam. The parameters had been set
to

$p=5$

,

$a=50$

,
and

$b=0.5$

.
This implies that

$x=90$

,

$–0.111111$

.

The corresponding R/exams templates simply hard-code these numbers into the question/solution
and wrap everything into R/Markdown (elasticity1.Rmd)
or R/LaTeX (elasticity1.Rnw).
Note that LaTeX is used in either case for the mathematical notation. In case you are unfamiliar
with the R/exams format, please check out the First Steps tutorial.

The meta-information simply sets `extype` to `num`, supplies the `exsolution` with a precision of three digits,
and allows an `extol` tolerance of 0.01. To see what the result looks like download the files linked above
and run `exams2html()` and/or `exams2pdf()`. (The examples below always use the R/Markdown version but the
R/LaTeX version can be used in exactly the same way.)

```library("exams")
exams2html("elasticity1.Rmd")
exams2pdf("elasticity1.Rmd")
```

## Static single-choice

Single-choice versions of exercises are often desired for use in written exams
because they can be conveniently scanned and automatically evaluated. Thus, we need to come up
with a number of incorrect alternative solutions (or “distractors”). If desired, these could include
typical wrong solutions or a None of the others alternative.

The R/exams templates
elasticity2.Rmd and
elasticity2.Rnw are
essentially copies of the static numeric exercise above but:

1. Question/solution now contain an answerlist (with five alternatives).
2. The `extype` has been changed to `schoice`.
3. The `exsolution` now contains a binary coding of the correct solution.
4. Furthermore, to obtain some basic randomization we have turned on shuffling by setting `exshuffle`
to `TRUE`. (Subsampling more than five alternatives would also be possible and would add some further randomization.)

As above `exams2html()` and/or `exams2pdf()` can be used to display the exercise interactively in R/exams.

## Dynamic numeric

Next, the static exercise from above is made dynamic by drawing the parameters from a suitable
data-generating process. In this case, the following works well:

```## p = a - b * x
p <- sample(5:15, 1)
fctr <- sample(c(2, 4, 5, 10), 1)
x <- sample(5:15, 1) * fctr
b <- sample(1:5, 1) / fctr
a <- p + b * x

## elasticity
sol <- -1/b * p/x
```

Note that in order to obtain “nice” numbers a common scaling factor `fctr` is used for both `x` and `b`.
Also, while the examinees are presented with parameters `a` and `b` and have to compute `x`,
the data-generating process actually draws `x` and `b` and computes `a` from that. Again, this makes it
easier to obtain “nice” numbers.

The R/exams templates
elasticity3.Rmd and
elasticity3.Rnw include
the data-generating process above as a code chunk either in R/Markdown or R/LaTeX format.
The parameters are then inserted into question/solution/metainformation using ``r a`` (R/Markdown)
or `\Sexpr{a}` (R/LaTeX). Sometimes the `fmt()` function is used for formatting the parameters
with a desired number of digits. (See `?fmt` for more details.)

As before `exams2html()` and/or `exams2pdf()` can be used to display the random draws from
the exercise templates. For checking that the meta-information is included correctly, it is often

```exams_metainfo(exams2html("elasticity3.Rmd"))
```

Furthermore, some tweaking is usually required when calibrating the parameter ranges in the
data-generating process. The `stresstest_exercise()` function draws a large number of random replications
and thus can help to spot errors that occur or to find solutions that are “extreme” (e.g., much larger or smaller than usual). To clean up your
global environment and run the function use something like this:

```rm(list = ls())
s <- stresstest_exercise("elasticity3.Rmd", n = 200)
plot(s)
plot(s, type = "solution")
```

The latter command plots the correct solution against the (scalar) parameters that were
generated in the exercise. This might show patterns for which parameters the solution becomes
too large or too small etc. See `?stresstest_exercise` for further features/details.

## Dynamic single-choice

To go from the dynamic numeric exercise to a dynamic single-choice exercise we need to
extend the data-generating process to also produce a number of wrong alternatives.
The function `num_to_schoice()` helps with this by providing different sampling
mechanisms. It allows to set a range in which the alternatives have to be, a minimum
distance between all alternatives, possibly include typical wrong solutions, etc.
It also shuffles the resulting alternatives and tries to make sure that the correct
solution is not a certain order statistic (e.g., almost always the largest or
smallest alternative).

The R/exams templates
elasticity4.Rmd and
elasticity4.Rnw first
wrap the data-generating process into a while loop with `while(sol > -0.11)`. This makes sure
that there is enough “space” for four wrong alternatives between -0.11 and 0, using a minimum
distance of 0.017. Subsequently, the four wrong alternatives are generated by:

```## single-choice incl. typical errors
err <- c(1/sol, sol/p, p/sol)
err <- err[(err > -5) & (err < -0.2) & abs(err - sol) > 0.01]
rng <- c(min(1.5 * sol, -1), -0.01)
sc <- num_to_schoice(sol, wrong = err, range = rng,
delta = 0.017, method = "delta", digits = 3)
```

This suggests a number of typical wrong solutions `err` (provided that they are not too small
or too large) and makes sure that the range `rng` is large enough. With these arguments
`num_to_schoice()` is run, see `?num_to_schoice` for the details. The resulting `sc` list
contains suitable `\$questions` and `\$solutions` that can be easily embedded in an `answerlist()`
and in the meta-information. Sometimes it is useful to wrap `num_to_schoice()` into another
`while()` loop to make sure a valid result is found. See the deriv2
template for illustraion.

As before `exams2html()` and/or `exams2pdf()` can be used to display the random draws from
this exercise template. And some stress-testing could be carried out by:

```rm(list = ls())
s <- stresstest_exercise("elasticity4.Rmd", n = 200)
plot(s)
plot(s, type = "ordering")
```

As a final variation we could include None of the above as the last alternative. This is
very easy because we can simply replace the fifth element of the question list with the corresponding
string:

```sc\$questions[5] <- "None of the above."
```

No matter whether this was actually the correct alternative or one of the incorrect alternatives, the
corresponding solution will stay the same. The R/exams templates
elasticity5.Rmd and
elasticity5.Rnw incorporate
this additional line of code in the data-generating process.