Adaptive Media Systems


Content, progress and pedagogy of the module


Adaptive media systems can use machine learning/artificial intelligence techniques to model users and/or their interactions with the system so as to tailor the experience specifically to the user(-profile), or otherwise enhance the performance/efficacy of the system. This adaptivity can be a real-time process based on here-and-now data, e.g., slowing system progress down for a user who is deemed to be temporarily overloaded, or it can be part of a long term strategic process of harvesting and mining user interaction data to enhance system efficacy.

The objective of the module is to provide students with competences in synthesizing and evaluating methods for creating such adaptive media systems. By applying machine learning to harvested data, or by other explicit means of letting measurements/data control changes to the media experience (adaptation).

The module furthermore requires students to work according to a scientific method, and to report results in scientific forms, such as papers and posters.

Learning objectives


Students who complete the module will obtain:

  • knowledge of common data types and collection thereof
  • understanding of machine learning methods and their applicability in the context of adaptive media systems
  • understanding of fundamental scientific methodology and hypothesis-driven research


Students who complete the module will be able to: 

  • apply scientific computing skills (e.g., Python, R) for collection, preprocessing and curation of user and usage data
  • analyze the applicability and affordance of machine learning techniques, or other means of enabling adaptivity, in the context of adaptive media systems
  • apply scientific methodology and techniques, including state-of-the-art review, hypothesis generation, and critical reporting in paper/poster format

With respect to Problem-Based Learning students will be able to:

  • produce a project report according to norms of the area, take into consideration relevant literature, apply correct terminology and convey the research-based foundation, problem and results of the project orally and in writing in a coherent manner, including the relationship between the problem formulation, the project’s realization and its conclusions
  • evaluate and select relevant literature, scientific methods and models and other tools for application in the project work, and evaluate the project’s problem area in a relevant scientific context


Students who complete the module will be able to:

  • synthesize and evaluate adaptive media systems based on techniques such as machine learning, user behavioral data, etc.
  • apply scientific methodology towards research in adaptive media systems, and the documentation/communication thereof

With respect to Problem-Based Learning students will be able to:

  • plan, execute and manage complex research and/or development tasks, and assume a professional responsibility for carrying out, potentially cross-disciplinary, collaborations
  • assume responsibility for own scientific development

Type of instruction

Academically supervised student-governed problem oriented project work



Name of examAdaptive Media Systems
Type of exam
Oral exam based on a project
Oral exam based on a scientific paper written in English and a media-technological product, an AV-production illustrating and summarizing the project, a poster in English, and edited worksheets/portfolio documenting project details.
Permitted aids
With certain aids:
See semester description
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleAdaptive mediesystemer
Module codeMSNMEDM1221
Module typeProject
Duration1 semester
Language of instructionEnglish
Location of the lectureCampus Aalborg, Campus Copenhagen
Responsible for the module


Study BoardStudy Board of Media Technology
DepartmentDepartment of Architecture, Design and Media Technology
FacultyThe Technical Faculty of IT and Design