EPN-V2

MEST4312 Research Theory and Method 1 Course description

Course name in Norwegian
Vitenskapsteori og forskningsmetode 1
Study programme
Master of Aesthetic Practices in Society
Weight
10.0 ECTS
Year of study
2020/2021
Course history

Introduction

This course provides an overview of relevant philosophy of science perspectives in the aesthetic field as well as different scientific methods. Particular emphasis is placed on elucidating research methods of relevance to the distinctive nature of the aesthetic field and research in this field. This is, in turn, put into a philosophy of science perspective. The course will present basic concepts and methodology perspectives that are key to assessing and carrying out research and development work in the aesthetic field.

Required preliminary courses

No requirements over and above the admission requirements.

Learning outcomes

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student

  • has thorough knowledge of the field's scientific theories and methods and the distinctive nature of the art subjects as a research area
  • is capable of analysing and reflecting on academic questions with a basis in research method perspectives and scientific traditions relating to the academic field’s history, characteristics and place in society
  • has in-depth knowledge of different forms of knowledge acquisition and source criticism in scientific publishing.

Skills

The student is capable of

  • analysing and using philosophy of science and research methods of relevance to the aesthetic field in an independent manner
  • remaining critical to different perspectives in the fields of philosophy of science and research methods, and applying this knowledge to structure and formulate academic lines of reasoning
  • remaining critical to different information sources as the basis for the research process
  • assessing different ethical perspectives relating to the research process

Competence

The student

  • is capable of working independently in his/her own field and masters the field's forms of expression
  • is capable of communicating with various target groups about professional issues and angles of discussion in the aesthetic fields

Teaching and learning methods

Undervisningen vil veksle mellom forelesninger av faglærere og gjesteforelesere. Studentene deltar aktivt i undervisningen og i gruppearbeid. Masterstudiet er basert på individuelle studier med deltagelse i grupper, forelesninger og seminar. Det legges vekt på både teoretisk og praktisk arbeid som studieform.

Course requirements

This course will present complex systems (cellular automata, networks, and agent-based) modelling and programming through state-of-the-art artificial intelligence methods that take inspiration from biology (sub-symbolic and bio-inspired AI methods), such as evolutionary algorithms, neuro-evolution, artificial development, swarm intelligence, evolutionary and swarm robotics.

During this course, students will get both theoretical and practical experience within complex systems and bio-inspired/sub-symbolic AI methods.

Assessment

The following coursework is compulsory and must be approved before the student can take the exam:

  • Minimum 80% attendance at compulsory teaching activities and seminars

Assessment

Individual written home exam in research theory and method over two weeks.

Scope: 3,000-4,500 words.

The exam grade can be appealed.

Permitted exam materials and equipment

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills, and general competence.

Knowledge

The student:

  • has advanced knowledge in sub-symbolic and bio-inspired AI methods.
  • has a clear understanding of key concepts in AI such as emergence, adaptation, evolution.
  • has a clear understanding of complex systems modelling and analysis.

Skills

The student:

  • can program complex systems using bio-inspired AI methods.
  • can design and implement evolutionary and swarm robotic systems.
  • can model complex systems using evolutionary AI models.

General Competence

The student:

  • has theoretical and practical understanding of biologically-inspired AI methods, evolutionary robotics, complex systems methods.
  • can understand and discuss relevance, strength and limitations of biologically inspired and complex systems.
  • is able to work in relevant research projects.

Grading scale

The course consists of lectures and seminars on techniques and methods, as well as a project to be carried out in individually / groups (2-3 students). The project will be chosen from a portfolio of available problems. The students will work individually or in groups and will submit the code and a project report.

Practical training

Lab sessions.

Examiners

None.