- An individual created a link from the node "Causal diagram (for Path analysis)" and "Relation between Bayesian Networks and Causal Diagrams"
- Another individual later added an image to the node to help showcase this topic. This individual also created the child node "Causal Diagram Example" which showcased another example of a causal diagram. 
- A different individual created a child node to "Causal Diagram Example" called "Implication of Arrows" which describes the general method of how to read a causal diagram. 
- A different individual added the reference node "Use of Causal Diagrams to Inform the Design and Interpretation of Observational Studies: An Example from the Study of Heart and Renal Protection (SHARP)" and used this outside reference to help create nodes describing the different relations in causal diagrams.

Examples of Collaboration in Nodes Related to Causal Diagrams

- "Path Diagram for technology use on self-directed learning, student engagement and academic performance"
- Path coefficient (and regression coefficient)
- Regression
- "Technology use on self-directed learning, student engagement and academic performance"
- "The 3 Junctions in Causal Diagram"
- "Causal diagram (for Path analysis)"
- Mediator
- Confounder
- Collider

University of Michigan - Ann Arbor

In addition to the content from the two textbooks, students found outside references, and summarized the essence of what they learned with their peers on 1Cademy to clarify and expand on what the community learned from the course textbooks:

S1 found interest in studying the paper "Path Diagram for technology use on self-directed learning, student engagement and academic performance" that used a causal diagram for inferring the Average Causal Effect (ACE) of self-directed learning, technology use, and student engagement on academic performance. 

Using the nodes from ISLR (such as linear regression), and nodes from The Book of Why (such as mediator, collider, etc. ) the S1 helped their peers synthesize the material of two different sources to gain an understanding of how to interpret the content of the research paper with ease. This way, whenever a student had difficulty understanding one of the nodes summarized from this paper, they were able to look at its parent nodes and quickly study the prerequisite concepts.

Students found outside references, and summarized the essence of what they learned with their peers on 1Cademy

A path diagram to show the model involving technology use, student engagement, self-directed learning and academic performance.  Here, path analysis allowed researchers to combine causal theory and multiple regression to assess the direct and indirect effects between variables of interest. 

Path Diagram for technology use on self-directed learning, student engagement and academic performance

To understand one concept (Path diagrams in this case) you often need to have learned other concepts that are complimentary, derivative or encompassing of that concept to understand its context and meaning.

To gain this understanding someone might choose many different paths. Three (3) notable ones are:
1. Taking multiple courses that teach the different concepts
2. Read books on the different concepts
3. Search online the different concepts

Each of these ways to come to an understanding are less organized, more time consuming and less positively innovative than 1Cademy. 

Why are concepts (path diagrams) and papers easier to understand on 1Cademy? (2)

- Create the original nodes to learn the concepts
- Add new nodes that provide additional context and understanding to related concepts
- Add connections between topics to assist meaningful learning
- Make improvements on nodes to make the concepts clearer (easier to understand or better for understanding)
- Voted on the utility of each node 
- Include references to make it easy to refer to the original source
- (add more!)

Learn Before

Related

Learn After