Students learn how to use Gephi to conduct network analyses through various activities.
Network analysis often uncovers connections that aren’t immediately visible. It’s interesting to use during teaching, because it can show us who or what is most important in a network, whether we’re talking individuals, characters in a literary text, hyperlinks between websites, tweets, and so on.
Students gain knowledge of network data and network analysis.
Students install and use Gephi to analyse and visualise network data.
Students collect and produce their own network dataset.
Students learn about two specific types of network data relevant to media studies, namely hyperlinks and tweets.
Week 1 | |
Preparation | Compulsory readings prior to class. |
Lecture | Introduction to network theory. What is it and how is it useful? |
Classroom instruction | Focus on network analysis: How can the theory be translated into an analytical method? Introduce Gephi as a tool for network analysis via a code-along session, where the teacher shows how to set it up and use the different functions. Students create a dataset based on information about everyone in the class, e.g. their names, place of birth and pets. The dataset is later used for students to practice network analysis on. |
Teaching by student instructors | Student instructors initiate exercises that build on what was learned during classroom instruction. |
Week 2 | |
Preparation | Compulsory readings prior to class. |
Lecture | Lecture on web- and hyperlinks and review of specific analyses from existing literature. |
Classroom instruction | Students are taught how to extract links from websites using the program Screaming Frog, and then how to use these links to visualise networks. The link data is cleaned and prepared for network analysis. Students then get to experiment with network analysis of hyperlinks. |
Teaching by student instructors | Student instructors initiate exercises that build on what was learned during classroom instruction. |
Week 3 | |
Preparation | Compulsory readings prior to class. |
Lecture | Lecture focusing on examples from existing literature, among other things. |
Classroom instruction | The teacher demonstrates how to source tweets (either from current tweets or from existing databases). Students then experiment with network analyses of the tweets. (Access to Twitter has since been restricted. Use YouTube instead) |
Teaching by student instructors | Student instructors initiate exercises that build on what was learned during classroom instruction. |
Students gain insight into network analysis and how it can be used. This shows them how to use analytical methods to discover otherwise unseen relationships between websites.
Students gain the ability to form an overview of a dataset with no background knowledge of it.
Use the students’ own data for the first exercise on visualising networks. This clearly illustrates a) that data is created, b) that data can be created in many different ways and c) what a data network is. Students will likely remember the exercise better, because their own data is used.
Align expectations with the students. Make it clear what the learning objectives of the course are. This can also give you an idea of the students’ current level.
Gephi is a very extensive program (often referred to as the Photoshop of network analysis), so it is recommended to start with a thorough introduction to the core concepts of network theory before you even open Gephi. It also won’t be possible to cover all of the functions in Gephi. Even a simple introduction should take two lessons.
Please contact the editors at AU Educate if you have any questions about the content of the platform or if you need consultation on your teaching from one of the many skilled professionals at the Centre for Educational Development.
