MGFR6100 Fransk med didaktikk, modul 1 Emneplan

After completion of the course, the student will have acquired the following learning outcomes, defined as knowledge, skills and general competence.

Knowledge

The student

• can communicate in French and has oral and written skills corresponding to early B1 level of Common European Framework of Reference for Languages (CEFR)
• can deal with elements of the everyday life in French countries in an intercultural perspective
• can choose and use a French textbook and digital teaching resources in an action oriented approach

Skills

The student

• is capable of using French corresponding to B1 level: linguistic, sociolinguistic and pragmatic level
• is capable of understanding the elements of the everyday-life in France relevant to teach in an action-oriented approach, by using authentic materials including literature and arts.
• is capable of building and delivering a course in French dedicated to pupils from class 5 to 10 following an action oriented approach

General competence

The student

• is capable of integrating the basic skills in the French language subject for everyday life even in unexpected situations
• is capable of assessing his/her own teaching practice
• has developed own learning and communication strategies
• is capable of reflecting on own learning strategies
• has developed multilingual competence
• has developed intercultural competence

The teaching method will be based on a task approach, where a reflexive learning method will be the working method. The sessions in Caen, as an intensive and immersive session, will permit an appropriation of French as an elected language that the in-service students integrate so that they can be its emissary in their own class and country. Every module is seen as having two goals that work together: learning and teaching French.

Collaborative work in groups is encouraged, not only during the sessions in Oslo and in Caen, but also by e-learning method: peer-learning, exchange of good practices moderated and supervised by a teacher, collaborative and individual digital portfolio.

The organisation is as follows:

• one week compulsory intensive course in Caen in the summer: courses in the classrooms of the university, school outings and investigations in the city
• online period with supervision: online courses, tasks on the learning platform, feedback from peers and teachers, an online course delivered in synchronous time (frequency to be defined).
• two-day seminar at OsloMeet: classroom teaching.

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

80 % attendance and participation in face-to-face weeks and virtual sessions.

- One written assignment related to French culture and society (200 words +/- 10 %).

- One oral presentation related to French culture and society (5 minutes, online).

- One written assignment related to subject didactics (300 words +/- 10 %).

- One oral presentation related to French language (10 minutes, online).

The course requirements aim to enhance oral and written skills, integrating a task focused on vocabulary and grammatical structures. Further they aim to evaluate students’ ability to assess and select teaching and learning materials according to the age, the level, and the objectives of the target learners (pupils in classes 5-10).

Associate Professor Raju Shrestha

• 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.

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.

The exam will be assessed by two internal examiners. An external programme supervisor will be used in accordance with the Guidelines for Appointment and Use of Examiners at OsloMet.