High-Performance Open-Source Archive
Many instructors prepare lessons in Quarto because it supports
narrative, executable code, figures, tables, and reproducible
publishing. tutorizeR keeps that authoring workflow intact
while producing an interactive tutorial for students.
A source .qmd lesson can contain narrative text, setup
code, examples, and MCQ blocks:
---
title: "Weekly study patterns"
---
## Learning goal
Students will summarize study time and relate it to quiz performance.
```r
library(tidyverse)
activity <- readr::read_csv("student_activity.csv")
```
```r
activity |>
group_by(program) |>
summarise(mean_hours = mean(study_hours), .groups = "drop")
```
```yaml
question: "Which summary is most appropriate for comparing programs?"
answers:
- text: "Mean study hours by program"
correct: true
- text: "The first row of the data"
correct: false
```
For browser-executable Quarto output, target
quarto-live:
library(tutorizeR)
work_dir <- file.path(tempdir(), "quarto-lesson")
output_dir <- file.path(work_dir, "generated")
report <- tutorize(
input = file.path(work_dir, "lesson-source.qmd"),
output_dir = output_dir,
format = "quarto-live",
assessment = "code",
overwrite = TRUE
)
print(report)The quarto-live workflow requires the Quarto live
extension in the teaching project. That dependency is intentionally not
vendored by tutorizeR.
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