BLH3010 Practice and Study Placement at Institutions Outside Norway Course description

Praksis- og studieopphold ved en institusjon utenfor Norge finner sted i sjette semester, og varer i tre måneder fra medio januar. Praksis- og studieoppholdet vil som hovedregel innebære en lengre praksisperiode ved minst to ulike barnehager,;et kortere studieopphold ved en barnehagelærerutdanningsinstitusjon i samme land/region, samt én uke til å reise i vertslandet/regionen. Før utreise deltar studenten i faglige forberedelser, og under oppholdet vil det også være faglige arbeidsoppgaver i tilknytning til praksisoppholdet. Et slikt praksis- og studieopphold byr på muligheter for økt bevisstgjøring og handlingskompetanse i forhold til interkulturell kommunikasjon, etisk refleksjon og flerkulturell pedagogikk. Oppholdet bidrar også til personlig og faglig bevisstgjøring om egne verdier og eget pedagogisk ståsted i forhold til syn på barn, lek, læring og voksen-barn relasjoner.

Emnet erstatter kunnskapsområdet;Ledelse, samarbeid og utvikling (15 studiepoeng).

Målgruppe

Studenter i barnehagelærerutdanningen som skal ha et praksisopphold utenfor Norge som del av sitt bachelorprogram.

No formal requirements over and above the admission requirements.

On successful completion of the course, students should have the following learning outcomes defined in terms of knowledge, skills and general competence.

Knowledge

The student:

• is knowledgeable about supervised, unsupervised, reinforcement learning
• has a good understanding of the principles of state-of-the-art deep neural networks such as CNN, RNN, GAN, RL.
• has a good understanding of both theoretical and practical know how required to use machine and deep learning methods effectively

Skills

The student:

• develop practical skills necessary to build, train, and deploy machine learning and deep learning models
• is able to analyze machine learning methods in regard to their performance and effectiveness
• is able to use existing deep learning networks, improve and/or customize them to apply to new problems

General competence

The student:

• has both theoretical and practical understanding of machine and deep learning methods
• can discuss relevance, strength and limitations of machine learning and deep learning in solving real world problems
• is able to work on relevant research projects

This course is divided into two parts. The first part with focus on covering the particular scripting language used in this class, such as its syntax, use and some extra libraries. The first part will also cover the practice of using a version control system as the means to store the code-base. During this part, students will meet for weekly lectures and lab-sessions where they work on exercises.

The second part will focus on the students completing a programming project. The project can be chosen from a portfolio of available problems. The student will work individually on the project and submit a final code-base that also includes documentation. During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

The assessment will be based on:

• A group project: implementation and report (about 7000 words).
• 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 applying 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 for the project report.

No aids are permitted for the oral exam.

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Associate Professor Raju Shresta

Bachelor level knowledge of the following topics is helpful for understanding some of the concepts in this course:

• linear algebra
• vector calculus
• basic statistics and probability.

Some experience with programming, especially with Python, and any machine learning frameworks such as Keras, TensorFlow, and scikit-learn will be beneficial.