Human Factors and Resilience Engineering disciplines address issues related to the interaction of humans and technology in complex systems such as performance, workload, teamwork, decision making, resilience, command and control, situational awareness, stress, mental models, and training. In this course the aim is for students to be able to account for theories and models within the disciplines of Human Factors and Resilience Engineering that can be used to describe, understand, and analyze human factors in complex systems, and to spply methods to analyze human factors in complex systems.
Main field of studyCognitive Science
Course typeSingle subject and programme course
Course coordinatorErik Prytz
Director of studies or equivalentJalal Maleki
Available for exchange studentsYes
|Course offered for||Semester||Weeks||Language||Campus||VOF|
|Single subject course (Half-time, Day-time)||Autumn 2020||v202045-202102||English||Linköping|
|F7MKS||Master Programme in Cognitive Science||3 (Autumn 2020)||v202045-202102||Swedish||Linköping||v|
|F7MKS||Master Programme in Cognitive Science||1 (Autumn 2020)||v202045-202102||Swedish||Linköping||v|
Main field of studyCognitive Science
Course levelSecond cycle
Course offered for
- Master Programme in Cognitive Science
Bachelor's degree in the field of cognitive science, or Bachelor's degree 180 in main field of computer science or equivalent and approved 30hp courses in one or more of the subjects: psychology, linguistics, philosophy, neuroscience, anthropology or equivalent, or Bachelor's degree in one of the main areas of Psychology or Neuroscience and approved 30hp courses in computer science or equivalent.
Intended learning outcomes
After completion of the course, the student should at an advanced level be able to:
- account for theories and models within the disciplines of Human Factors and Resilience Engineering
- apply methods to analyze human factors in complex systems
- identify, delimit, and analyze a man-machine system from a Human Factors or Resilience Engineering perspective
The following topics are studied in the course:
- Central theories and models within the Human Factors and Resilience Engineering disciplines that can be used to describe, understand, and analyze human factors in complex systems
- Central concepts related to those disciplines
- Methods to analyze and describe complex systems and the role of humans within such systems
- Current research within the Human Factors discipline.
Teaching and working methods
Teaching methods are lectures, practical exercises, and seminars. The student is expected to read and study independently, either individually or in groups.
The course is examined through active participation during seminars, completion of practical exercises, and an individual project that includes both written and oral components.
Detailed information can be found in the study guidelines.
If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it. If the coordinator has instead recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.
Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.
Students who have passed an examination may not retake it in order to improve their grades.
GradesThree-grade scale, U, G, VG
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.
The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.
DepartmentInstitutionen för datavetenskap
|UPG1||Written presentation||U, G, VG||3 credits|
|OBL1||Mandatory seminars||U, G||2 credits|
|LAB1||Laboratory work||U, G||1 credits|
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