evaluate the design principles of the tutorials project

At the beginning of the project, we defined the design principles below. After developments in tutorials-early, tutorials-middle, and tutorials-late, we can contrast and evaluate the consistency between the planning and the developed materials in an evaluation phase of the project

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## What design principles we follow for these lessons?

This section aims to capture the decisions about why a material is the way it is.

- A [Tutorial documentation](https://documentation.divio.com/tutorials/) format
- Easy to consume in a self-paced manner,
- Be self-explanatory,
- Show common mistakes and misconceptions, and
- Write assessment exercises with diagnostic power for those common misconceptions.
- Add links to related [Explanation](https://documentation.divio.com/explanation/) documentation.
- Show the outbreak analytics pipeline approach connecting common policy questions with analysis tasks, data inputs and outputs.
- Order the content to [promote motivation](https://teachtogether.tech/en/index.html#s:motivation-authentic): first the content that requires the less time to master and most useful once mastered. Aligned with the [datasciencebox design principles](https://datasciencebox.org/01-design-principles.html).
- Facilitate the [material maintainability](https://teachtogether.tech/en/index.html#s:process-maintainability). It should be cheaper to update than to replace it.
- Use the lesson folder structure from [The Carpentries workbench](https://github.com/carpentries/workbench-template-rmd), designed accordingly to their [design principles](https://carpentries.org/blog/2020/08/lesson-template-design/#design-principles-for-the-next-iteration-of-the-carpentries-lesson-template).
- Facilitate a multimodal experience:
- Create visuals to explain related concepts. Vision gathers the most information in the short term memory
- Create slides or other teacher document (e.g. visual qmd files) to facilitate it's reuse by other instructors for in-person workshops or online trainings.
- Create interactive videos to create a sense of presence.
- Add an interactive chatbox for effective one-to-one timely feedback.
- Use callout blocks for complementary info and refer to existing materials from the epidemiology and data science training community: [reconlearn](https://www.reconlearn.org/), [appliedepi](https://community.appliedepi.org/), [graphnet](https://thegraphcourses.org/), [rstudio](https://posit.cloud/learn/primers), [stackoverflow](https://stackoverflow.com/search?q=epidemiology), github issues and discussions.

## What is not included in this material?

Topics that are out of the scope of these lessons include:

- How to use Git and GitHub to contribute in Open science projects.

- How to create a reproducible analysis project.

- How to build R packages for data analysis tasks.