Critical Data Analysis (Spring 2023)

Abstract

This course focuses on the analysis and production of data. Students gain an overview of data-driven design work and critical thinking about the use of data, and the challenges and opportunities that data brings to the design of interactive systems. They will be introduced to technical concepts in data production and data analysis, and reflect on these from an ethical, social and cultural perspective. Doing so, the course bridges technical and human perspectives in data-driven design.

Description

Data analysis has become one of the driving forces of digital design. This course teaches data analysis methods and tools, and how they affect the design of interactive systems. Students learn how to produce and analyse data, to reflect on the role of data in design  and how data in a productive way can support the design of interactive systems. The course will introduce students to all aspects of data-driven design cycles, including collecting data, analysing data, and creating data-informed designs or re-designs, while being aware of the ethical, legal and cultural challenges in the production and use of data.

NB: This course is not available to students enrolling in the course Data-Driven Design and Development

Formal prerequisites

This course is not available to students enrolling in the course Data-Driven Design and Development

Intended learning outcomes

After the course, the student should be able to:

• Formulate an appropriate research question to effectively address and evaluate a design, development or business idea
• List possible data sources to evaluate the user experience in a given context
• Compare, evaluate and apply appropriate operationalisation of a given construct
• Outline and apply relevant statistical measures for testing, evaluation and communication of data
• Describe the technical, ethical and legal challenges connected to the collection and analysis of user data
• Hypothesise what data will show and critically evaluate these hypotheses based on actual findings in their data analys

Learning activities

• short in-class excersises related to the topic of day
  
• group work with either own or pre-selected data samples
  
• practising data collection methods and tools in exercise sessions
  

Mandatory activities

There will be two mandatory activities during the semester:

• Design a research study to address a hypothetical business problem related to an app/game/service
• Conduct the previously designed research study and report the results

The pedagogical function of the mandatory activities is to provide the students with assignments where they practice the given ILO’s. The students will have the possibility to see the practical activities associated with the whole build, measure, learn cycle. Throughout the course, the students will receive formative feedback from teacher/TA/peer building up to and after each mandatory activity.

If the students fail to hand in or get a “not-approved” they will receive a second attempt to be submitted latest by the end of week 22

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

There will no basic book(s) for this course. We will instead use up-to-date relevant research articles in the field and select book chapters which will be made available on LearnIT when the course starts.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 20%
• Lectures: 20%
• Exercises: 20%
• Assignments: 20%
• Exam with preparation: 20%

Ordinary exam

Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B22: Oral exam with no time for preparation.

reexam

Exam type:
B: Oral exam, External (7-point scale)
Exam variation:
B22: Oral exam with no time for preparation.

Time and date