Table of contents

The course is connected to the following study programs

Teaching language

English

Learning outcomes

On successful completion of the course, the students should:
- have thorough understanding of the foundations and distinctions between science and engineering, focusing on the Computer Science domain;
- have advanced knowledge of the structure and approach of scientific investigations, including their limitations, within the Computer Science domain;
- be able to independently identify, formulate, evaluate, and address research problems using relevant methodologies;
- have the skills to analyze and critically evaluate various literature to identify state-of-the-art knowledge and gaps related to research problems;
- be able to apply theoretical models and utilize qualitative and quantitative research methods systematically for selected problems, and be able to prepare and present a research project plan;
- be able to assess research ethics, focusing on privacy and social responsibility, and effectively disseminate scientific content within the Computer Science domain using established formats.

Contents

The emphasis of the course is on theoretical and practical knowledge and skills related to scientific methodologies and methods, as well as scientific approaches, thinking and working methods and tools. The overall goal is to understand the research process from the formulation of a scientific problem to the dissemination of research project proposals to research results.

The main aspects of the course are:

  • Scientific methodologies (with focus on the Computer Science and Information and Communication Technology (ICT) disciplines)
  • Formulation of a problem space, hypotheses, and research questions
  • Literature search and analysis strategies
  • Selection and application of applicable Research Methodologies and Methods for the target problem
  • Development of qualitative and quantitative research designs
  • Dissemination techniques and formats for the effective communication of research content through presentations and documents

Teaching and learning methods

This course includes lectures, exercises, group work, and compulsory assignments.

Generally, lectures and exercises are offered on-site on campus (UiA, Grimstad). The workload for the average student is approximately 200 hours.
Details will be communicated via Canvas.

Examination requirements

Students must pass the compulsory assignments in order to take the examination. Information about compulsory assignments will be given in Canvas at the start of the course.

Examinations

Graded portfolio assessment.

Information about the content of the portfolio will be given in Canvas by the start of the semester. There will not be arranged a postponed exam for the portfolio.

Student evaluation

The person responsible for the course decides, in cooperation with the student representative(s), the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.

Offered as a free-standing course

Yes. Subject to availability or capacity.

Admission Requirement if given as a free-standing course

Admission requirements for the course are the same as for the master’s programme in AI and IoT.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course Reduction of Credits
ORG453 – Communication, Cooperation and Research Methods. 2.5
Last updated from FS (Felles studentsystem) Oct. 15, 2026 1:37:12 PM