Navigating Complexity: Mapping, Visualisation and Decision-making (Autumn 2020)

Abstract

The course will teach students to analyse complexity within an empirical case that explores a current topic or controversy within the field of science, technology, and innovation.

Description

The complex challenges of digitalisation often demand the professional use of both qualitative methods and data-exploration to diagnose issues and to act upon them.

In Navigating Complexity, students will be introduced to a range of conceptual and technical tools for generating and visualizing data and analysing complexity. Throughout the course students will experiment with different techniques for generating data and visualising complexity. Based on case work, students will be learn to reflect on how visualisations work as simplifications and can inform decision-making.

Students will learn a variety of qualitative approaches to quantitative data, focusing on inductive, exploratory inquiry. After the course, students will be capable of dealing with, communicating, and acting constructively in situations faced by complex challenges without straightforward solutions.

The course will cover topics such as complexity thinking, storytelling with data, data and digital methods, situational analysis, problematisation, data politics and technical tools for data visualisation and exploration.

Formal prerequisites

Please note that this course is only available for students enrolled in the programme Digital Innovation & Management. Moreover the student must always meet the admission requirements of the IT University.

Intended learning outcomes

After the course, the student should be able to:

• Develop research questions that allow for exploratory and inductive inquiry into an empirical case through an iterative process of data collection, analysis, and storytelling.
• Apply selected methods and conceptual tools to analyze complexity in an empirical case.
• Interpret the data and the visualizations generated using the technical and conceptual tools provided in the course.
• Reflect upon the decisions made in the research process relating the data and the visualizations to the development of the research question or focus.
• Discuss the relationship between chosen methods, theories, data, and their implications for the findings in a concise manner.

Learning activities

14 weeks of teaching consisting of lectures, exercises and supervision.

Each week students have 4 hours of lecture and 4 hours of exercises.

During exercises students will be divided into groups of approximately 40 working with TAs to practically engage with the exercises and topics of the week.

Mandatory activities

There are 10 mandatory activities in the course (nine written and one oral). These must be completed before participation in the final group report submission and oral exam.

The written activities are the submission of individual weekly reflections on the course literature (one per week for a total of nine out of eleven). These reflections allow the students to familiarise themselves with the course curriculum and prepare them for the oral exam.

The oral mandatory activity is a presentation of group work that takes place toward the end of the semester, but before exam submission.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 20%
• Lectures: 20%
• Exercises: 20%
• Assignments: 5%
• Project work, supervision included: 25%
• Exam with preparation: 2%
• Other: 8%

Ordinary exam

Exam type:
D: Submission of written work with following oral, Internal (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.

Time and date