VERB1200 The Welfare State and Person Centered Care Course description

SYKKPRA50x, SYKPPRA50x and SKOMPPRA20 are fully overlapping.

The student must have been admitted to the programme.

On successful completion of the course, the student has the following learning outcomes classified as knowledge, skills and general competence:

Knowledge

The student

• can describe the most common causes of intellectual development disorder as well as the most common diagnoses and syndromes of this domain
• can explain overriding explanatory models in psychology
• can explain normal biological, cognitive and psychological development in relation to abnormal development and deviation from normal development
• can explain different theories and explanations of health, impairment and disability
• can explain the living conditions for people with intellectual disabilities and other disabilities
• can explain past and present welfare policy guidelines and their consequences for the practice of social educators
• can explain key legislation of relevance to welfare services and legislation that regulates services provided to disabled people
• can describe the scientific theories that forms the basis for the subject areas covered by the social education programme
• can explain scientific theories and methods and their importance to the practice of social educators
• can describe the basic features of quantitative and qualitative research methodology
• can explain evidence-based practice and the steps of evidence-based practice

Skills

The student

• can demonstrate skills that promote cooperation in a group

General competence

The student

• can discuss political goals, policy instruments and welfare schemes that regulate services for disabled people
• can discuss strategies for including disabled people

The teaching and learning methods include lectures, self-study and project work. Students are divided into project groups where they use the syllabus and articles to shed light on a given case. The project group's case work is discussed and supervised in seminars attended by several groups working on the same case. At the end of the course, the project groups present their project work to their fellow students.

The following required coursework must be approved before the student can take the exam:​

• project assignment in groups of six students
  • At least 80% participation in project group work for preparation and presentation of a mini project. The project groups are assigned cases at the start of the course.
  • 100% participation is required for presentation of the project to fellow students.

Individual written home examination over 48 hours, up to 2,000 words

All aids are permitted, as long as the rules for source referencing are complied with.

Much of the work in students´ scientific or practical work both during their studies and later in their professional life will require a good understanding of research methods and analysis tools and techniques. This course will provide the knowledge and skills necessary for planning and conducting engineering research, for processing data and for analysing results, with special focus upon the most common statistical techniques and data-driven computational techniques as some of the most common tools used in these areas.

No formal requirements over and above the admission requirements.

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

Knowledge:

The student has advanced knowledge of:

• research methods, especially in engineering research
• statistical and analytical techniques, including knowledge of the most common libraries and tools used in statistical analysis and visualisation of the results
• designing experiments, preparing data and interpreting analysis results
• how relevant statistical and computational techniques relate to each other and where they are used

Skills:

The student has:

• required skills in setting up sound experiments, hypotheses and research questions, and in finding and preparing relevant data
• required skills in identifying which statistical and analytical techniques are to be used and how and where they should be used
• hands-on experience with some of the most common computational techniques and libraries as well as related tools for statistical analysis
• hands-on experience with relevant tools for use in analyses

General competence:

The student:

• has broad overview of the computational tools and techniques used in analysis and engineering research, including statistical techniques and techniques related to data science and machine learning
• has an overview of the terminology related to statistical analysis and data science.
• is able to design experiments for successful engineering research, analyses and critical interpretation of results
• can extend his/her knowledge and skills in programming/scripting, analysing, managing and visualizing data