This article summarizes the authoring patterns currently covered by the package and its bundled examples.
The package exports the following main question types:
clozeschoicemchoicenumstringFor cloze exercises, the gap subtypes num,
string, schoice, and mchoice are
mapped to the corresponding ILIAS gap elements.
For new cloze exercises, the recommended style is to use
exams::add_cloze() together with
format_metainfo(). This keeps the question text and the
cloze metadata synchronized.
library(exams)
n <- sample(20:30, 1)
mean_x <- sample(seq(70, 80, by = 0.5), 1)
choices <- c("t-test", "chi-squared test", "Wilcoxon signed-rank test")
# Inside the exercise body:
# How many observations are in the sample? `r add_cloze(n)`
# What is the sample mean? `r add_cloze(mean_x, tolerance = 0.1, digits = 1)`
# Which method is appropriate? `r add_cloze(choices[1], choices, type = "schoice")`
#
# Meta-information:
# exclozetype: `r format_metainfo("type")`
# exsolution: `r format_metainfo("solution")`
# extol: `r format_metainfo("tolerance")`See the bundled file stats_cloze.Rmd for a full,
self-contained example.
Single-choice and multiple-choice questions follow the standard
R/exams pattern with answerlist() in the
question and a logical answer key in the solution block.
If you want imports to arrive with explicit scoring in ILIAS, include
expoints in the exercise metadata.
Meta-information
================
extype: schoice
exsolution: 100
exname: P-value interpretation
exshuffle: TRUE
expoints: 1For cloze exercises with several gaps, you can provide one point value per gap.