evaluate the design principles of the tutorials project

[avallecam]

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 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 documentation.
• Show the outbreak analytics pipeline approach connecting common policy questions with analysis tasks, data inputs and outputs.
• Order the content to promote motivation: first the content that requires the less time to master and most useful once mastered. Aligned with the datasciencebox design principles.
• Facilitate the material maintainability. It should be cheaper to update than to replace it.
• Use the lesson folder structure from The Carpentries workbench, designed accordingly to their design principles.
• 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, appliedepi, graphnet, rstudio, stackoverflow, 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.