There are thousands of resource pages and tools that can be helpful to learning new digital methods. In this course, we emphasize the dialogue with the support team to find the best available tools.

Terms of Service, data handling and GDPR

When working with subject specific digital methods, you might run into issues concerning sensitive data or copyright, and we highly recommend that you contact technical support for assistance. Do this as early as possible, so that you can plan your project, and make sure that your data will be handled correctly. The technical support can also help you with issues such as understanding terms of service and checking if data from specific webpages are okay to use. If you are ever in doubt about any of this, please always ask the technical support staff.

Key topics and inspiration

Below, you will find a number of resources introducing key course topics that you may need in order to develop and implement digital elements into your teaching. Ranging from an understanding of how to work with data, skills within statistics and text analysis, to introductions of agent-based modelling and machine learning, the resources below are meant for inspiration and further exploration. These are just some of the methods and topics that are supported within the project and is meant for a starting point for further dialogue about the possibilities within specific subjects and fields.

Data collection and management

Web technology and scraping

Understanding web technology, the structure of web pages and the laws and ethics concerning data on the web can allow for collection of large amounts of interesting qualitative or quantitative data.

Data collection and web scraping by Kristian Gade Kjelmann

Data care

Handling and caring for your data will make later analysis much smoother and should be an integral step in every process that deals with large data sets.

An introduction to data care by Mathieu Jacomy

Data analysis

Image analysis

"Teaching" the computer to recognize certain shapes and motives opens up novel ways of working with and exploring large data sets of image data.

Image analysis by Ross Deans Kristensen-McLachlan

Text analysis

When analysing textual data with quantitative and computational methods, it becomes possible to explore very large collections of text, and through distant reading, recognize patterns that could otherwise be too large-scale to notice.

Introduction to text analysis by Lene Offersgaard

Machine learning

By utilizing machine learning, the computational power can be used to do exploratory data analysis and locate patterns that otherwise would stay hidden from the researchers and students.

Introduction to Machine Learning and Artificial Intelligence in Education by Ross Deans Kristensen-McLachlan

Agent-based modelling

Agent-based modelling can be used to simulate and visualise complex environments and can then be used for both exploratory data analysis and as a powerful teaching tool.

Introduction to Agent-based modelling in education by Arthur Hjorth

Data presentation

Seeing patterns in data

With knowledge of statistics and ways to visualise data, it becomes easier to see and explain patterns within the large data sets collected.

Introduction to Relationships in data (statistics) by Janet Rafner

Telling stories with data

When patterns emerge from data it becomes invaluable to visualise the findings in an easy to understand way.

Introduction to telling stories with data by Janet Rafner

Introduction to Digital Humanities and Social Sciences with Mads Rosendahl Thomsen

GDPR guides

Find the specific guidelines for each of the Danish universities here: