Learning and Study Skills (5 ECTS)

Objective

The student
• is able to work in digital learning environments and use the most common computer software and tools
• is able to apply basic scientific writing and professional presentation skills in learning assignments
• knows how to obtain knowledge in their learning environment
• is able to plan their studies, time management and career path
• identifies the factors affecting their well-being and knows how to use Metropolia’s student well-being services
• understands sustainable development as part of health care and social services.

In addition, the student
• has basic knowledge of information security and data protection
• knows the best practices of information security in higher education
• is able to handle data and information appropriately in a higher education environment, for example in the context of writing a thesis
• identifies threats to information security and in the cyber world at a basic level of knowledge
• understands the basic principles of the operation of large language models
• is aware of the limitations of artificial intelligence (AI)
• is able to train their own AI model and critically evaluate its performance
• is familiar with different models of AI.

Content

• Metropolia’s digital learning environments and the use of cloud services, templates and basic software
• Data protection and information security
• Basics of scientific writing and professional presentation skills
• Study skills and basics of information search
• Code of conduct and regulations governing studies
• Introduction to the student’s field of study
• Planning and support for the studies and career path
• Basics of sustainable development in health care and social services
• Basics of information security
• Basics of artificial intelligence

Location and time

The course will take place in the 1rd period in autumn 2025

Materials

The learning material is available on Moodle
The literature to be used for the learning task will be specified in the project work.

Teaching methods

Learning tasks
Group work and project work
Studies orienting to studies and studies
2 days internship at laboratory

Employer connections

During the course, students are oriented to the work of a biomedical laboratory scientist.

Exam schedules

Learning assignments must be returned by the agreed deadline.

International connections

N/A
The course includes international information.

Completion alternatives

N/A

Student workload

The workload of the course is 5 credits, which corresponds to 135 hours of student work.
Scope of the modules:

Computing and digital skills 1.5 credits
Communication and writing 1.5 credits
Degree component 2 credits

Content scheduling

The course starts with Digistart.
During the course, students will work in small groups and write a learning assignment. The group will agree on the timetable together. Attendance at tutorials is compulsory and tutorial schedules can be found in the course timetable.
The due date and instructions for the assignment can be found in Moodle.

Further information

The written learning task is accompanied by group work. A “project note” is kept of the group work, which is attached to the learning task. Participation in the group work is compulsory... If the student does not participate in the group work, his/her performance will not be assessed.

If there are challenges in group work, these will be raised in the first instance between group members and if there is no desired change in the work, the group should contact the teacher supervising the qualification.

The course orientation will cover the entire course content and learning tasks.

Students confirm their place on the course by being present at the start of the lesson or by other means agreed with the teacher, for example by returning the first learning assignment. If the student is unable to attend the start of the course due to illness, he/she may also confirm his/her place by informing the teacher in advance.

The assignments must be returned by the deadline. The course will not be assessed if the assignments are returned after the deadline. In this case, the student must register for the next semester.

Assignments and MATERIALS prepared by the teacher are works of authorship under the Copyright Act (404/61), to which the teacher holds the copyright. The assignments AND MATERIALS may not be used for any purpose other than the teacher's own studies. Their public distribution is only allowed with the permission of the teacher.

The use of artificial intelligence in the course is subject to specific instructions. Students must familiarise themselves with the Metropolia AI guidelines and follow them in their coursework.

In case the student has received a decision for individual support, which can affect the completion of the study unit, the student should be in touch with the responsible teacher of the study unit directly after the ending of the enrolment period.

Evaluation scale

Hyväksytty/Hylätty

Assessment criteria, satisfactory (1)

Assessment: Pass/Fail
For assessment criteria, see the attachment.
The assessment criteria for beginners are applied.

Assessment criteria, good (3)

Assessment: Pass/Fail
For assessment criteria, see the attachment.
The assessment criteria for beginners are applied.

Assessment criteria, excellent (5)

Assessment: Pass/Fail
For assessment criteria, see the attachment.
The assessment criteria for beginners are applied.

Assessment criteria, approved/failed

Assessment: Pass/Fail
For assessment criteria, see the attachment.
The assessment criteria for beginners are applied.

Assessment methods and criteria

Assessment is based on returned learning tasks.
The assessment is based on the competence assessment framework, level beginner (Metropolia University of Applied Sciences Rehabilitation and Diagnostics Curriculum autumn 2025)

Further information

Käytetään arviointikehikon tasoa aloittelija.

Further information

Käytetään arviointikehikon tasoa aloittelija.