Data-Driven Design & Development (Spring 2022)

Abstract

In this course, students learn how to work in data-driven design and development processes and what changes data analysis brings to the design and maintenance of games.

Description

Data analysis has become one of the driving forces of game design and development. This course teaches data analysis methods, and how they affect design and development. Students learn how user data is gathered, and how data analysis can be used in order to support the design and development of games and other interactive systems. They also learn how to develop, use, and analyse different game industry related metrics (e.g. customer metrics, community metrics, performance metrics, gameplay metrics) and how to use those to balance and optimise different aspects of games, informing the design process.

Formal prerequisites

The students need to have attended a introduction to programming course.

Intended learning outcomes

After the course, the student should be able to:

• Describe the impact of data analysis on game design, production, and marketing.
• Identify the stages of game design, production, and marketing in which analytics support decision making.
• Classify the key performance indicators relevant in each of the design, production and post production stages.
• Relate qualitative properties of the game experience with quantitative measures.
• Outline and apply relevant statistical measures for testing and evaluation
• Design tests and experiments, and evaluate their results in relation to the expected player experience.
• Construct effective visualisations from collected metrics that can inform game design, production, and marketing.
• Describe the challenges connected to the collection and analysis of large amount of in-game data.

Learning activities

Students are responsible for attending weekly lectures, participating in the group activities and engaging with the teaching staff for supervision and feedback.

Mandatory activities

There will be no mandatory activity during the course. The students will be provided with weekly workshop and they will work on a project throughout the course. A report on the project will be handed in at the end of the course as exam project. Alongside the oral examination, the report will contribute to the final grade.

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

Game Analytics -  Maximizing the Value of Player Data

Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa

Game Research Methods

Petri Lankoski, Staffan Björk

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 26%
• Lectures: 13%
• Exercises: 7%
• Assignments: 7%
• Project work, supervision included: 22%
• Exam with preparation: 25%

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