Programplaner og emneplaner - Student
IPHFORK2-09H International Public Health Emneplan
- Engelsk emnenavn
- International Public Health
- Studieprogram
-
Bachelorstudium i ergoterapiInternational Public Health
- Omfang
- 18.0 stp.
- Studieår
- 2020/2021
- Emnehistorikk
-
Innledning
Studiet består av følgende fire hovedtema, med ett tema for hver samling.
Ressurser i personlig kunnskap (samling 1)
- Om voksnes mangfoldige forutsetninger for læring
- Biografisk læring
- Læringsstiler (studievaner, persepsjon)
- Funksjonell leseferdighet
- Akademisk skriving
- Om læremidler og andre læringsressurser for bruk i egen virksomhet
Voksenlæreren på kulturelt komplekse læringsarenaer (samling 2)
- Å undervise voksne
- Voksne lærende i det flerkulturelle Norge
- Fleksibel læring
- Endringer i teknologiske kunnskapsmiljøer
- Formell og uformell kompetanse
- Minoritet og majoritet i lærende virksomheter
- Epistemiske kulturer (læringskultur)
- Samhandling i krysskulturelt læringsarbeid (fordommer, anerkjennelse)
Transnasjonalt kunnskapsliv (samling 3)
- Mobilitet, migrasjon og utdanning
- Internasjonale studenter
- Internasjonalisering i eget læringsmiljø
- Medbrakt og ervervet kompetanse - realkompetanse
- Kulturforskjeller i motivasjon - kultursjokk
- Endring - første- og andre ordens læring
- Transformativ læring
- Dannelse og myndige medborgere
Kultursensitiv pedagogikk (samling 4)
- Bindingskrefter i livslang læring
- Aktivt lyttende veiledning
- Krysskulturell veiledning og formidling
- Metodisk kulturrelativisme
- Vide kontekster for kunnskap
- Negative skoleerfaringer og utdanningsrelaterte skader
- Vurdering av læringsutbytte
- Bærekraftig kunnskap
Forkunnskapskrav
The course consists of lectures, group consultations, presentation seminars, and project work. In the seminars, students will read papers, present, and also actively participate in other presentations. This will facilitate research-oriented education in the field. Research projects will be aimed at cultivating the students towards good future researchers.
Læringsutbytte
The following required coursework must be approved before the student can take the exam:
- Two oral presentations (one on a given topic, one on the topic of own choice)
- Participate as a prepared opponent/discussant in two presentations from other students
There is mandatory attendance in obligatory consultation meetings and a minimum of 80% mandatory attendance in the lectures.
Students who do not meet this requirement will not be allowed to sit the exam.
Arbeids- og undervisningsformer
Exam in two parts:
- A group project: implementation and report (about 7000 words). A group of 2-3 students will be formed during the course. Each group member receives an individual grade based on their contribution to the project.
- Individual oral exam (about 30 minutes).
Each of them carries 50% weight in the final grade. The oral examination cannot be appealed.
Both exams must be passed in order to pass the course.
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.
Arbeidskrav og obligatoriske aktiviteter
All aids are permitted for the project report, provided the rules for plagiarism and source referencing are complied with.
No aids are permitted for the oral exam.
Vurdering og eksamen
Grade scale A-F.
Hjelpemidler ved eksamen
Two internal examiners. External examiner is used periodically.
Vurderingsuttrykk
Associate Professor Raju Shrestha
Sensorordning
- Bachelor level knowledge in linear algebra, vector calculus, and basic statistics, and probability is important for understanding some of the concepts in this course.
- Knowledge and skills in programming, particularly Python, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.
Opptakskrav
This course covers principles of machine learning and deep learning methods and best practices in solving problems effectively. Most of the problems are related and applicable in various areas such as computer vision, surveillance, assistive technology, medical imaging, etc. Therefore, the course intends to provide case studies and examples of ML and DL in solving various problems. Students can explore the tremendous potential of modern AI, ML, and DL methods and techniques in solving problems in different application domains through project work.