Outline of hackweek train-the-trainer module content (#49)

* delete files

* update with new sections

* new references

* initial commit

* draft learning objectives and possible references

* initial commit

* update

* fix wording

* fix typo

* add badge

* add more refs

* adjust wording

* Update _toc.yml

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Co-authored-by: Scott Henderson <scottyh@uw.edu>

docs/_toc.yml

@@ -8,12 +8,15 @@ parts:
   chapters:
     - file: tutorials/index  
       sections: 
-        - file: tutorials/tutorial-resources
-        - file: tutorials/tutorials
+        - file: tutorials/tutorial-design
+        - file: tutorials/tutorial-delivery
 - caption: Project Design
   chapters:
   - file: projects/index
-- caption: Accessible Technology
+    sections:
+      - file: projects/project-design.md
+      - file: projects/project-implementation.md
+- caption: Technology
   chapters:
     - file: technology/index  
       sections:

docs/culture/index.md

@@ -1,3 +1,23 @@
 # Introduction
 
-We strive to create a sense of belonging by meeting participants where they are and embracing the idea that each of us is on a lifelong journey of learning.
+*Paragraph providing brief overview of this module*
+
+## Learning Objectives
+
+After completing this module, hackweek supporters will:
+
+* understand the importance of psychological safety to support creativity and open science
+* have practical experience applying tools that support a healthy learning culture
+* identify specific ways we can maximize for inclusivity within existing hackweek structures 
+
+## Possible References?
+
+Characteristics of open science collaborative culture {cite:p}`thibault_open_2023`
+
+Needed systemic changes to support a culture of open science {cite:p}`gentemann_opening_2022`
+
+Disclosure (vulnerability?) increases effectiveness of interracial mentoring relationships
+{cite:p}`leitner_reducing_2018`
+
+Trends in peer learning (do we need a separate helper section?)
+{cite:p}`topping_trends_2005`

docs/overview.md

@@ -2,9 +2,7 @@
 
 Data permeates nearly every corner of the research landscape. Whether you are a social scientist exploring the impacts of zoning policies on urban demographics, or a physical scientist predicting the effects of climate change on agricultural productivity, you will almost certainly need to develop skills in accessing, processing, visualizing and performing statistical analysis on datasets. 
 
-New technologies have enabled us to have access to ever-increasing volumes of data. At first we usually think, the more data the better! But very quickly we run into challenges. Let's take a high resolution imagery of earth from space as an example: depending on the sensor resolution and study region, such files could be as large as several hundred gigabytes. Very quickly you might find yourself investing time trying to subset the data, work with it in the cloud, or find someone else who has a bigger hard drive than yours to download the data. 
-
-Even for the most technically savvy of us, keeping up with the dizzying expansion of tools, data formats and technologies is nearly impossible. 
+New technologies have enabled us to have access to ever-increasing volumes of data. At first we usually think, the more data the better! But very quickly we run into challenges. Often a dataset will be too large for us to download and manage it on our local laptop, and we find ourselves trying to subset the data, work with it in the cloud, or find someone else who has a bigger hard drive than yours to download the data. Other times there may be new approaches such as machine learning that would advance our science, but we do not have access to example workflows for applying the tools to our datasets. Even for the most technically savvy of us, keeping up with the dizzying expansion of tools, data formats and technologies is nearly impossible. 
 
 So, how do we learn these tools? If we're already enrolled in a formal education system, we may be lucky enough to find good courses that keep pace with the shifting data science landscape. If not, we typically try to learn from what is on the internet, or we seek out a training workshop. 
 

docs/projects/index.md

@@ -1 +1,22 @@
-# Introduction
+# Introduction
+
+*Paragraph providing brief overview of this module*
+
+## Learning Objectives
+
+After completing this module, project leads will:
+
+* understand the various roles that projects can play in supporting hackweek objectives 
+* create a description for a project idea that is accessible for hackweek participants
+* practice facilitating small group team dynamics to ensure participants feel like they are contributing
+* be familiar with our suite of project [learning technologies](../technology/index.md)
+
+## Possible References?
+
+Evidence that project-based learning is a viable approach {cite:p}`almulla_effectiveness_2020`.
+
+Mentorship at a hackathon should be more than just occasional support. It should include taking ownership of the project, setting achievable goals, building social ties, and receiving learning-oriented support {cite:p}`nolte_how_2020`.
+
+Evidence that minimal guidance and lack of structure does not lead to positive project outcomes {cite:p}`kirschner_why_2006`.
+
+## What to expect as a project lead

docs/projects/project-design.md

@@ -0,0 +1,3 @@
+# Project Design
+
+*Note content [here](https://book-template.hackweek.io/projects/index.html) that we may wish to draw on/reproduce/reshape for project leads.*

docs/projects/project-implementation.md

@@ -0,0 +1,3 @@
+# Project Implementation
+
+*Note content [here](https://book-template.hackweek.io/projects/index.html) that we may wish to draw on/reproduce/reshape for project leads.*