Navigating Complexity: Mapping, Visualisation and Decision-making (Autumn 2024)

Abstract

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

Description

The complex challenges of managing digitalisation and innovation processes often demand the professional use of qualitative data-driven approaches to diagnose and act upon issues.

In Navigating Complexity, students will be introduced to a range of conceptual and technical tools for analysing complexity by experimenting with different techniques for generating and visualising different kinds of data. Based on project work, students will learn to reflect on how methods and visualisations work as simplifications and can inform decision-making.

Students will learn various approaches to collecting and analysing digital and non-digital data, focusing on inductive, exploratory inquiry. After the course, students will be capable of dealing with, communicating, and acting constructively in situations facing complex challenges without straightforward solutions. Beyond the specific set of methods, the goal of the course is to enable students to use and combine them in the face of complexity.

The course will cover topics such as, but not limited to, complexity thinking, digital methods, problematisation, data politics, discourse analysis and positionality.

Formal prerequisites

Please note that this course is targeted at 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 and anaysis.
• Apply selected methods and conceptual tools to analyse complexity in an empirical case.
• Interpret the data and any visualisations generated using the technical and conceptual tools provided in the course.
• Discuss the relationship between chosen methods, theories, and data and their implications for the findings concisely
• Reflect on how their project work and findings enable them to navigate the complexity of their case.

Learning activities

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

Each week, students have 4 hours of lectures and 4 hours of exercises. Lectures will likely be divided into 2-hour sessions: one auditorium lecture and one online interactive lecture. The auditorium lecture introduces the theme of the week's content and is a place for theoretical and conceptual conversation with students, surrounding the weekly readings.  The online interactive lecture takes the form of 'Navcom Radio', a 2-hour live program discussing the themes and theories of Navigating Complexity that can be put into practice in conversation with students in the studio or calling in.

During the TA-led exercise sessions, students will be divided into groups to engage with the week's topics and experiment with the technical and conceptual tools offered.

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, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.

reexam

Exam type:
D: Submission of written work with following oral, External (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

Ordinary Exam - submission Fri, 20 Dec 2024, 08:00 - 14:00
Ordinary Exam Mon, 13 Jan 2025, 09:00 - 21:00
Ordinary Exam Tue, 14 Jan 2025, 09:00 - 21:00
Ordinary Exam Wed, 15 Jan 2025, 09:00 - 21:00
Ordinary Exam Thu, 16 Jan 2025, 09:00 - 21:00
Ordinary Exam Fri, 17 Jan 2025, 09:00 - 21:00
Ordinary Exam Mon, 20 Jan 2025, 09:00 - 21:00
Ordinary Exam Tue, 21 Jan 2025, 09:00 - 21:00
Ordinary Exam Wed, 22 Jan 2025, 09:00 - 21:00
Ordinary Exam Thu, 23 Jan 2025, 09:00 - 21:00
Ordinary Exam Fri, 24 Jan 2025, 09:00 - 21:00