1 On The ADAPT Model
This book is patterned after the ADAPT model championed by North Carolina State University’s Data Science and AI Academy (DSA). This model stands for All-campus Data science and AI through Project based Teaching and learning. This model serves as a framework for the workflow of this book and it’s pedagogical ethos.
For a little context, all instructors at the Data Science and AI Academy are trained to teach using this model. The Academy’s data science courses are open to all students of any discipline and professional stage. Students include undergraduate, graduate university attendees as well as staff, faculty, and community members. With such a diverse student body, the DSA uses the ADAPT teaching model to create a consistent form of instruction across all courses. In a way, the teaching model threads all students together, weaving their experiences into a cohesive experience across any course that students take.
At its core, the model is designed to make computational skills accessible to everyone, regardless of their discipline. It emphasises student agency as much as possible within the confines of the unique course objectives. For example, students are encouraged to select their own dataset to use for their final project based on their interests. Instructors are guided to introduce their students to different datasets and techniques to appeal to student-interests.
In this spirit of this model, you’ll see that I use some fun data (e.g., Harry Potter, UK Grime music, etc.) to teach network principles and R skills. I find these appeal to a very broad student body. I also teach based on concepts more than explaining code. Understanding the ‘what’ and the ‘why’ behind social network analysis more than explaining every syntactical function, I find, facilitates learning for many. For students more interested in the nature of the code or the mathematics behind the measurements, I extend more resources on an individual basis. Therefore, this book, presented as a course, serves as a great introduction for students to social networks and instructors to the ADAPT teaching and learning approach.
1.1 ADAPT for Instructors
The teaching model is a living document that the Data Science and AI team, headed by Rachel Levy, are constantly working on. As previously mentioned, the students are at the core of this model as is the belief of the DSA that data science acumen can be taught to people from every disciplinary background. In fact, it behooves all to engage with data and become data literate (understanding how to interpret data) in a data-driven world!
I do not go into detail here but suffice it to say that the ADAPT model comprises three parts:
Project-based Learning
10 Common Learning Elements
Workforce Preparedness
This book was created from a course taught at the DSA under the direction of the ADAPT model. It’s goal is not to serve as an exemplar of the model, but rather to encourage instructors to engage with the “spirit” of the model. Consider teaching project-based courses that centres the interests of your students - maximising their agency. Further, consider teaching computational skills in such a way as to open its content to all interested parties.
Each of the units and modules in this book are designed with students in mind. I discuss each elements of the ADAPT model in the Unit introduction chapters. The purpose of this book is to enact these principles and enable other instructors wishing to teach social network analysis to do the same. All data used in this book is available using the “ADAPTSNA” R package. Alternatively, you may wish to use other data and just use this guide to inform your own syllabus and activities.
Finally, let me point you to the assignments in this course. You are welcome to use or to change these assignments as you wish. They are designed to build a student’s skills throughout the semester as they progress towards their final project. Courses taught using the ADAPT model do not have quizzes or tests, but rather scaffold multiple assignments throughout the semester aimed at building a capstone project at the end of the course. This scaffolding approach enables the student to build their progress continually and the assignments serve as milestones in their progress. The material in this book is designed to fill a 1 credit-hour course. If yours is a longer course, you may wish to alter the pacing of the semester adding or rearranging the content of this course. Regardless here is a little insight into the assignments I use and why I think these milestones are useful to the student’s success and for you to grade the student.
Assignment 1 (10 marks) acts as an orientation to network principles and parlance. This, no-tech/low-tech assignments sees students create an ego-network, their ego-network teaching them the vocabulary of network analysis.
Assignment 2 (10 marks) is the first milestone of their project. This no-tech assignment sees students present ideas for their final project encouraging them to present two separate ideas for datasets once they have either looked through the GitHub repository or discovered their own dataset. This serves as the beginning of a discussion between the instructor and student to guide their final project.
Assignment 3 (20 marks) is the second milestone of their project. This is designed to help students bring their data into R, describe it, and suggest any cleaning that they may need to do. By this point, they are encouraged to select their data that they are going to use.This is an opportunity for the instructor to give more in-depth feedback on the data they are using and suggest further transformations that they may need.
Assignment 4 (20 marks) is the third milestone of their project. This is an opportunity for instructors to provide guidance on producing useful visualisations.
The Final Project (40 marks) is the last milestone of their project. This an exploratory data analysis where students learn to use their exploratory visualisations and transformations to form and answer questions about their network.
1.2 ADAPT for Students
Students, the biggest thing you need to know is that you will be working on a project for the duration of the course. For more details see the Final Project Instructions.
Each unit and assignment is a milestone in your project development. For this project, you may select any social network data that appeals to you. I have collated quite a few from various sources available on the Github repo. Alternatively, you may wish to explore other network data (from places like the network repository, Kaggle or others).
Any data that you use, make sure you cite where you are using it from!
Pay attention to the introductions of each unit. There are learning objectives, project milestones, and tips for preparing to do this professionally.