Dropdown Exercises

Minimum version

In this type of exercise, the candidate has to select an element form a dropdown-list. Note that our package implements dropdowns as entry-objects because this is essential what dropwdowns are. Several dropdowns can be combined in a single exercise, but a combination with numeric and text entries (gaps) is not possible; mainly due to LMS limitations.

A template is automatically created when you initiate an rqti project through RStudio. Alternatively, it can be added by clicking on New file -> R Markdown -> From Template. The rqti templates start with rqti:. Here we look at the templates rqti: dropdown (simple) and rqti: dropdown (complex).

The minimum you need to provide is the type: dropdown (or the equivalent type: dd) in the yaml-section and some text, where at least one gap is formed as a dropdown-element, in a section called #question.Furthermore, when employing helper functions from the rqti package, it is essential to ensure its prior loading.

---
type: dropdown
knit: rqti::render_qtijs
---

```{r echo=F}
library(rqti)
```

# question

The philosophy of the rqti package is <<do one thing and do it well|one for all>>.

Under the hood, the rqti package uses `r dropdown(c("S4 OOP", "S3 OOP", "no OOP", "R6 OOP"))`.

# feedback

The package `rqti` is specialized for producing xml rqti files so "do one thing and do it well" is more appropriate. Under the hood we use S4 OOP.

Knitting via the Knit-Button to qtijs, this exercise renders as:

Alternatively, change the knit parameter to knit: render_opal (see API Opal) to upload to Opal directly, producing:

There are 2 ways to specify a dropdown-element in Rmd content:

Put the right answer inside << … >> (or the equivalent <gap> … </gap>). Example: <<element1|element2|element3>>
use the helper function ?dropdown (also see section helper dropdown)

By default, 1 point can be reached for each dropdown (specify points to your needs). The total number of points for completing a task is defined as the sum of points of all dropdowns.

Note that in this example, a feedback section was also provided. The feedback is optional, but usually it is a good idea to give some explanation for students. In dropdown exercises the feedback refers to the whole task, not to a specific dropdown. Group your feedback into appropriate sections, which can be opened/closed for better user experience (e.g. use <details> and <summary> html tags).

More control

If you want to have more fine-grained control, consider the RMD template rqti: dropdown (complex), which uses more yaml attributes and a more complex use of the helper function dropdown.

---
type: dd # type of exercise
knit: rqti::render_qtijs # if you do not want our preview renderer, remove this
identifier: dd001 # think twice about this id for later data analysis!
title: A meaningful title that can be displayed in the LMS
---

```{r echo=F}
library(rqti)
```

# question

The philosophy of the rqti package is <<do one thing and do it well|one for all>>.

Under the hood, the rqti package uses `r dropdown(c("no OOP" = "no OOP", "S4" = "S4 OOP", "S3" = "S3 OOP", "R6" = "R6 OOP"), solution_index = 2, points = 2, response_identifier = "OOP_task")`.

# feedback

The package `rqti` is specialized for producing xml rqti files so "do one thing and do it well" is more appropriate. Under the hood we use S4 OOP.

Which renders as:

yaml attributes

type

Has to be dropdown or dd.

identifier

This is the ID of the exercise, useful for later data analysis of results. The default is the file name. If you are doing extensive data analysis later on it makes sense to specify a meaningful identifier. In all other cases, the file name should be fine.

title

Title of the exercise. Can be displayed to students depending on the learning management system settings. Default is the file name.

Feedback

Feedback can be provided with the section

# feedback (general feedback, displayed every time, without conditions)
# feedback+ (only provided if student reaches all points)
# feedback- (only provided if student does not reach all points)

Helper function `dropdown`

This helper function is used to generate a formatted string describing a dropdown in Rmd content:

choices <- c("c1" = "S4 OOP", "c2" = "S3 OOP", "c3" = "no OOP", "c4" = "R6 OOP")
oop_task <- dropdown(choices = choices, solution = "S4 OOP",
                     response_identifier = "OOP_task")
oop_task
#> [1] "<gap>{choices: [S4 OOP,S3 OOP,no OOP,R6 OOP], solution_index: S4 OOP, points: 1.0, shuffle: yes, response_identifier: OOP_task, choices_identifiers: [c1,c2,c3,c4], type: InlineChoice}</gap>"

Argument list of dropdown:

choices

Elements of dropdown. If you use a named vector, the names will be used as identifiers. This is useful for later data analysis.

solution_index

The index of the correct choice as a numeric. Default is 1, meaning that you can simply put the correct element as the first one in the vector choices.

points

The number of points for the exercise. Default is 1.

shuffle

If TRUE, randomizes the order of the choices. Defaults to TRUE. Only in rare occasions it makes sense to have a strict order of choices (setting shuffle to FALSE).

response_identifier

This is the ID of the dropdown-element, useful for later data analysis of results. The default has the format “response_1”, “response_2”, …“response_n” for several dropdowns. If you are doing extensive data analysis later on, it makes sense to specify a more meaningful identifier.

Dropdown exercises are forced choice items, so are equivalent to single choice exercises. The advantage is that they can be placed in between other text and several of them can be used in a single exercise. Still, they suffer from the same problems as single choice exercises.

2024-05-08