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Assessment modes let instructors decide whether a lesson should generate coding practice, conceptual questions, or both.
library(tutorizeR)
work_dir <- file.path(tempdir(), "tutorizeR-assessment")
source_file <- file.path(work_dir, "lesson.qmd")
tutorize(source_file, output_dir = work_dir, assessment = "code")
tutorize(source_file, output_dir = work_dir, assessment = "mcq")
tutorize(source_file, output_dir = work_dir, assessment = "both")code: generate exercise and solution scaffolds.mcq: generate MCQ blocks only.both: generate code exercises, solutions, and
MCQs.question: "Which variable identifies the academic program?"
answers:
- text: "program"
correct: true
- text: "quiz_score"
correct: falseIn a source lesson, this YAML would be placed inside a
tutorizeR-mcq fenced block.
library(tutorizeR)
example_dir <- system.file("examples", "example_course_module", package = "tutorizeR")
work_dir <- file.path(tempdir(), "tutorizeR-assessment")
dir.create(work_dir, recursive = TRUE, showWarnings = FALSE)
file.copy(file.path(example_dir, "lesson-source.qmd"), work_dir, overwrite = TRUE)
file.copy(file.path(example_dir, "student_activity.csv"), work_dir, overwrite = TRUE)
qb <- load_question_bank(file.path(example_dir, "question-bank"))
report <- tutorize(
input = file.path(work_dir, "lesson-source.qmd"),
output_dir = work_dir,
assessment = "both",
question_bank = qb,
mcq_source = "mixed",
overwrite = TRUE,
verbose = FALSE
)
print(report$mcq)Generated MCQs are scaffolds. Instructors should verify that questions are aligned with learning objectives and that distractors are plausible. The package does not measure whether an MCQ improves learning.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.