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Future research (5 cr)

Code: MU00DT24-3015

General information


Enrollment
07.04.2025 - 21.04.2025
Registration for introductions has not started yet.
Timing
01.09.2025 - 31.12.2025
The implementation has not yet started.
Number of ECTS credits allocated
5 cr
Local portion
5 cr
RDI portion
2 cr
Mode of delivery
Contact learning
Unit
Department of Culture
Campus
Kouvola Campus
Teaching languages
Finnish
Seats
20 - 40
Degree programmes
Degree Programme in Bioproduct Design
Degree Programme in Graphic Design
Degree Programme in Fashion and Costume Design
Degree Programme in Interior Architecture and Furniture Design
Teachers
Laura Hakanen
KY Opettaja
Teacher in charge
Laura Hakanen
Groups
SKKV23SP
Interior architecture and furniture design, full-time studies
Course
MU00DT24

Realization has 15 reservations. Total duration of reservations is 56 h 0 min.

Time Topic Location
Tue 02.09.2025 time 10:00 - 14:30
(4 h 30 min)
Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 09.09.2025 time 10:00 - 14:30
(4 h 30 min)
Tulevaisuuden ennakointi MU00DT24-3015
149 Byod-/teorialuokka (32+1), päärakennus
Thu 18.09.2025 time 10:00 - 14:30
(4 h 30 min)
Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Thu 25.09.2025 time 10:00 - 14:45
(4 h 45 min)
Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Thu 02.10.2025 time 10:00 - 14:30
(4 h 30 min)
Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Thu 09.10.2025 time 10:00 - 14:15
(4 h 15 min)
Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Thu 16.10.2025 time 10:15 - 15:15
(5 h 0 min)
Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 28.10.2025 time 13:15 - 16:15
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 04.11.2025 time 09:00 - 12:00
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 04.11.2025 time 13:00 - 16:00
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 25.11.2025 time 13:15 - 16:15
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 02.12.2025 time 09:00 - 12:00
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 02.12.2025 time 13:00 - 16:00
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 16.12.2025 time 09:00 - 12:00
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Tue 16.12.2025 time 13:00 - 16:00
(3 h 0 min)
* Tulevaisuuden ennakointi MU00DT24-3015
243 DataLAB/Byod-luokka (24+1), päärakennus
Changes to reservations may be possible.

Objective

You outline potential, probable and desirable futures, which you begin to implement in your design process.
You will be familiar with the different future research methods, which you learn to apply in the developing of your own work and your trade.

Content

What methods are available to forecast the future?
What kind of potential, probable and desirable futures do you see in your field?
How do you start implementing them in your own profession?

Course material

The study materials are provided by the teacher in class or via the Learn platform.

Study forms and methods

• The course is carried out by way of contact teaching on Kouvola's campus according to a weekly schedule. Remote participation is not possible. As a rule, scheduled contact lessons are not recorded nor duplicated by online lectures.
• Students make progress by attending scheduled lessons and completing tasks and assignments individually or in groups.
• The teacher provides guidance during and after contact lessons and via the Learn platform.
• The assessed assignments are completed independently/in groups/in pairs.
• Feedback is provided by different methods, in writing and/or orally. Also, peer feedback can be used.
• The course consists of two parts which are separately assessed: common 3 credits and sustans 2 credits. More detailed information available below in section “Course part description”.

RDI and work-related cooperation

The assignments are based on the needs of operators in the field.
Assignments can be completed with reference to tasks given by one’s employer or in connection to developing a product.

Timing of exams and assignments

There is no exam in this course.

Learning assignment return schedules are published on the Learn platform at the start of the study period. All learning assignments must be completed in accordance with the schedule found on the Learn platform in order to obtain an acceptable performance from the course. Assignments returned late are automatically assessed as rejected.

Student workload

• 1 ECTS credits equals 27 hours of work by the student.
• In a 5-credit course, the student's total maximum workload is approximately 135 hours including lessons, group work and independent study.

Further information

Students who have been accepted for the study course will be transferred to the Learn platform automatically at the start of the study course.

Evaluation scale

1-5

Assessment methods and criteria

The student’s performance is assessed with reference to the learning objectives and assessment criteria of the course. The completion of the course requires all tasks to be completed with a passing grade.

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