ACIT4930 Interaction Styles and Technologies for Accessibility Course description

The student must have been admitted to the Master’s Degree Programme in Health Sciences - Specialisation in Public Health Nursing.

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

Knowledge

The student

• can critically assess the public health nurse’s function, areas of responsibility and tasks in relation to school children, adolescents and their caregivers at different levels
• can analyse children and adolescents’ physical, mental and social development and health and the environments in which they grow up
• can analyse and discuss knowledge about guidance and communication aimed at relevant users
• can critically assess topics related to the extended authorisation to prescribe contraceptives

Skills

The student

• can apply relevant methods of health promoting and preventive work at different levels to children and adolescents aged 6-20 years and their caregivers
• can analyse and assess children and adolescents’ development and needs and implement evidence-based measures related to their health and quality of life
• can analyse and assess the need for measures and interdisciplinary cooperation, and if necessary provide referrals
• can assess and give grounds for different ways of providing guidance and educational methods when interacting with children, adolescents and their caregivers
• can order and administer contraceptives

General competence

• can analyse and assess evidence-based methods in the school health service and the youth health centres
• can analyse and apply ethical principles to reflect on dilemmas in professional practice
• can apply a culturally sensitive approach to children, adolescents and their caregivers

The course will use varied, student-active work methods. Work and teaching methods used in the course include lectures, oral and written assignments with presentations (individually and in groups), seminars, skills training and self-study. The student will receive one hour of supervision during the first week of the project examination.

- This course is organized as a series of lectures and seminars where students present and discuss with opponents research articles that cover core concepts and topics in the literature.

- Students work alone or in groups on two projects under supervision.

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

• Two oral presentations of research articles.
• Participation in the discussion of at least two research article presentations.

• One written group project report (2000-3000 words) completed in group of up to 2 students related to practical implementation of HCI. This part of the examination counts 35 % of the final grade.
• One written group project report (3000-5000 words) completed in groups of up to 4 students, focused on theoretical aspects of the selected project topic. This part of the examination counts 35 % of the final grade.
• Individual oral examination (20 minutes for each candidate). The oral examination counts 30% of the final grade.

All exams must be passed in order to pass the course.

The oral examination cannot be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for registering for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

All aids are permitted, provided the rules for plagiarism and source referencing are complied with.

For the oral exam, students will not have access to computers or other aids.

All examinations are assessed by an internal and an external examiner.

This course will present the state of the art in algorithms for machine learning for the 3D environment. We will cover topics related to deep learning for 3D data such as point clouds, multi-view images, and shape graphs. The course covers applications like classification, segmentation, shape retrieval and scene representation.

No formal requirements over and above the admission requirements.