Teacher Education 5.-10. grade Programme description

Programplanen bygger på forskrift om rammeplan for grunnskolelærarutdanninga for trinn 5-10, fastsett av Kunnskapsdepartementet 7. juni 2016.

Høgskolen i Oslo og Akershus (HiOA) utdannar dyktige lærarar til norsk skule. I løpet av grunnskulelærarutdanninga for trinn 5-10 skal studentane tileigne seg kunnskap, ferdigheiter og kompetanse som set dei i stand til å handsame heile mennesket gjennom undervisninga og læringa i faga. Studentane vil gjennom den integrerte, profesjonsnære, forskingsbaserte og utviklingsorienterte grunnskulelærarutdanninga utvikle solid kunnskap i fag, fagdidaktikk, pedagogikk, og solide ferdigheiter i å undervise i faga, leie ein klasse og fylle rolla som kontaktlærar.

Grunnskulelærarutdanning for trinn 5-10 er ei femårig integrert masterutdanning (300 studiepoeng) som kvalifiserer for tilsetting i undervisningsstillingar i grunnskulen på 5.-10. trinn. Utdanninga legg vekt på både fagleg spesialisering og fordjuping, samt breiddekunnskap i fag.

I alle fag i utdanninga blir det lagt på å utvikle profesjonsrelevant digital kompetanse og ferdigheiter i bruk av estetiske arbeidsformer og ein fleirfagleg og mangfaldig dimensjon vil vere eit gjennomgåande trekk. Dei grunnleggande ferdigheitene har ein sentral plass i alle fag utdanninga i, og studentane utviklar kompetansen innan forsking og utvikling (FoU) gradvis fram mot ei praksisretta masteroppgåve i utdanninga.

Blant anna i arbeidet med psykososialt læringsmiljø møter studentane studentar frå andre profesjonsutdanningar ved HiOA, som barnevernspedagogutdanninga, helsesøsterutdanninga og barnehagelærarutdanninga, for å lære om andre profesjonar og samtidig bli meir bevisst sin eigen. Gjennom slike møte opparbeider dei ein tverrprofesjonell kompetanse.

Applicants will choose the desired specialization track at the point of applying for the program. Admission to the program is based on two sets of requirements. The general admission requirements, which are the same regardless of track chosen, and the specialization track requirements. Please take special note of the individual requirements of each specialization track.

For each specialization track, there is also a list of recommended prior knowledge, which can be found in Section 5, under "Specialization Track Content". That list is not a formal admission requirement, but we encourage students to review it in order know what areas they might want to spend time preparing in before starting in order to avoid steep learning curves. The topics listed can be found as part of most university bachelor programs in technology and engineering. The student can use the list to inform any decision on elective courses in their bachelor studies that might best prepare them for their targeted specialization in this program.

General admission requirements

In order to qualify for an english-speaking program, the applicant must be able to document sufficient mastery of English. Please consult the current regulations at OsloMet for a complete overview: https://lovdata.no/dokument/SF/forskrift/2015-12-15-1681.

In addition to English proficiency, applicants must have completed a BSc or equivalent program with a grade average of C or better.

The master programme aims for a diverse group of students from many countries. Given the limited number of places, normally no more than three students from each country will be assigned to each specialization, with the exception of students from Norway.

Specialization track requirements

In order to be qualified for their desired track, the applicant must comply with at least ONE of the requirements for that track. Each requirement is a combination of Bachelor's degree from a specific field with possible conditions for ECTS within certain topics.

Universal Design of ICT requirements

• BSc in Computer Science, Computer Engineering or Informatics
• BSc in Information technology or equivalent program, which at least 80 ECTS within the field of computer science.

Cloud-based Services and Operations requirements

• BSc in Computer science, Computer Engineering or Informatics
• BSc in Information technology or other equivalent qualifications, which at least 80 ECTS within the field of computer science
• BSc in Electrical Engineering with at least 10 ECTS of programming

Data Science

• BSc in Computer Science, Computer Engineering or Informatics
• BSc in Mathematics or Applied Mathematics with at least 10 ECTS in programming
• BSc in Physics with at least 10 ECTS in programming
• BSc in Statistics with at least 10 ECTS in programming
• BSc in other engineering subjects with at least 10 ECTS in Mathematics courses, 10 ECTS in Statistics courses and 10 ECTS in programming courses

Mathematical Modelling and Scientific Computing requirements

• BSc in an engineering discipline with at least 30 ECTS (in total) withinMathematics, Statistics and/or Scientific Computing
• BSc in Computer Science or Informatics with at least 30 ECTS (in total) within Mathematics, Statistics and/or Scientific Computing
• BSc in Mathematics
• BSc in Statistics
• BSc in Physics

Applied Artificial Intelligence requirements

• BSc in Computer science, Computer Engineering or Informatics
• BSc in Information technology or other equivalent qualifications, which include at least 80 ECTS within the field of computer science
• BSc in Electrical Engineering with at least 10 ECTS of programming
• BSc in Mathematics or Applied Mathematics with at least 10 ECTS of programming

Robotics and Control requirements

• BSc in Electrical Engineering
• BSc in Mechanical Engineering
• BSc in Chemical Engineering
• BSc in Physics
• BSc in Mathematics or Applied Mathematics
• BSc in Computer Science, Computer Engineering or Informatics And 10 ECTS programming and 25 ECTS mathematics and / or statistics.

Biomedical Engineering requirements

• BSc in Electrical Engineering
• BSc in Biomedical Engineering
• BSc in Mechanical Engineering
• BSc in Chemical Engineering
• BSc in Biotechnology Engineering
• BSc in Physics
• BSc in Computer Science, Computer Engineering or Informatics
• BSc in Statistics

On successful completion of their Master's degree, the candidate should have the following qualifications defined in knowledge, skills and general competence:

Knowledge

Upon successful completion of the program, the candidate:

1. has thorough knowledge of the professions within applied computer and information technology and their role in businesses, organizations and society
2. has a thorough knowledge of the processes and methodologies applied by professional practitioners within fields like information technology, scientific computing and electrical engineering or a combination of these traditional fields, both in public and private sector
3. has an advanced understanding of how technological advances in society are alloys of multiple disciplines, such as Mathematics, Computer Science, Electrical engineering and more
4. has a fundamental understanding of a secondary field within applied computer and information technology and its role in organizations and society
5. has thorough experience in interdisciplinary work and how it contributes to solving complex problems

Skills

Upon successful completion of the program, the candidate:

1. can contribute to innovation processes in applied computer and information technology by harnessing knowledge and skills from a research discipline, such as computer science, electrical engineering or mathematics, and directing them towards an interdisciplinary problem
2. can facilitate, nourish and cultivate interdisciplinary perspectives in projects
3. can design and implement technical solutions to challenges that represent modern and real-life scenarios
4. can translate abstract theoretical models or technical descriptions into working solutions and systems, relative to their area of focus
5. can analyze existing theories, methods and interpretations in their field and work independently on practical and theoretical problems
6. can use relevant methods for research, scholarly and development work within their field in an independent manner
7. can carry out independent research or development project within their field under supervision and in accordance with applicable norms for research ethics
8. can identify and communicate common facets and challenges within their field to professionals from other fields
9. can deploy, use and manage systems and technical tools that in complexity and scale represent enterprise scenarios
10. can independently update their knowledge as technology progresses to new areas within society
11. can apply knowledge to new areas within their academic field
12. can analyze academic problems within their area of research based on its methods, tradition and role in society

General Competence

Upon successful completion of the program, the candidate:

1. can appreciate why evaluating a technological challenge beyond the perspective of a single discipline is needed in the pursuit of a safe, inclusive and responsible technologically advanced society
2. can analyze relevant academic, professional and research ethical problems in applied computer and information technology
3. can apply his/her knowledge and skills in new areas in order to carry out advanced assignments in the realm of technology
4. can communicate extensive independent work and masters language and terminology of their own academic field or an interdisciplinary field
5. can communicate about academic and professional issues, analyses and conclusions in their field, both with specialists and the general public
6. can contribute to new thinking and innovation processes

ACIT is a combination of courses and a thesis project at the end. Students can choose between a short or a long thesis project, more on this below. The program is designed to first focus on a specialization before introducing training as a specialist in interdisciplinary work. Every specialization has three core courses, called specialization courses (SPEC). When following a specialization, the corresponding SPEC courses become mandatory courses.

In addition, there are two courses common for all specializations. These two courses focus on research methods and ethics and interdisciplinary innovation respectively. Finally, every student will take an alternative specialization course (ASPEC), which is one of the two first specialization course that belongs to a different specialization than their own. The student is free to choose what ASPEC course to take based on their individual interest, applicability for their research project or for a specific career profile as along the prerequisite knowledge requirements are met.

By requiring students to take a specialization topic outside their own specialization, they are not only given a wider scholarly perspective, but they will now interact with students from other fields as well as interacting with the teachers who are also researchers, increasing the chance that they will embark on a thesis project that combines the two fields and with supervisors from each field.

In summary, the core structure for all students is:

• 30 ECTS Specialization courses (SPEC)
• 20 ECTS Common courses
• 10 ECTS Alternative specialization course (ASPEC)
• 60 ECTS Long Thesis

or

• 30 ECTS Elective courses (EC) and 30 ECTs Short Thesis

The table below illustrates the program structure for a student selecting a long thesis. The two common courses are placed in the first and second semester. The specialization courses are also within that timeframe but the master thesis project is divided into three phases where the first phase is in the second semester. This structure allows for the project to mature over three semesters instead of two. It also enables the student to pick an alternative specialization course that would supplement the thesis project after familiarizing more thoroughly with the project and its scope.

Sem 1: Common course - Specialization course - Specialization course

Sem 2: Common course - Specialization course - Master's Thesis

Sem 3: Alternative specialization cource - Master's Thesis - Master's Thesis

Sem 4: Master's Thesis - Master's Thesis - Master's Thesis

The structure for a short thesis is shown in the next table. The placement of the common and specialization courses is the same as before, but there is more space in the second and third semester to take additional elective courses. The thesis project takes place in the final semester.

Sem 1: Common course - Specialization course - Specialization course

Sem 2: Common course - Specialization course - Elective course

Sem 3: Alternative specialization cource - Elective course - Elective course

Sem 4: Master's Thesis - Master's Thesis - Master's Thesis

Common course content

ACIT has two common courses which are mandatory for all students in the program. The first common course, Research methods and Ethics introduces the student to scientific writing, finding and understanding research papers and the ethical standards that follow a researcher and professional. The ability to communicate effectively is an important asset of any researcher, as research is not done in a vacuum. We need to communicate our challenges and findings to others, be that fellow researchers, politicians or in the general public domain. In each case, the format has to be adapted to the audience, so todays researcher must master a wider range of communication than before. Finding, reading and understanding scientific literature can be a cumbersome process. Our students will learn techniques to find, sort and organize the literature they seek in order to get the most out of it.

Each scientist relies on a set of methodologies, that define the rules and methods for their design, development, data gathering and analysis. These methods can vary based on the particular field of the researcher and ideally every researcher should know every method from every field. Instead, however, one must focus on the most common methods used in their domain. This course offers a broad perspective on the range of methods available but will offer more specialized topics to each student based on their field.

The second common course, Interdisciplinary Innovation: using diversity to solve complex problems, is an important course for the student to get training in how to be an expert among other experts from other fields. In this course, students will work together in diverse groups to address or solve a challenge given to them from our own researchers or outside partners. We focus the ability to interact with other team members and to build on each respective knowledge and skills. Students will be trained in design and innovation processes as well as practice to present and communicate their solution to others.

Specialization track content

Each specialization track offers in-depth knowledge into a field that has both academic and industrial applications. Below are descriptions of the content one will find in each track.

Together with the description, we list the specialization courses for the track and also provide a list of Recommended Prior Knowledge. This list is not a formal requirement for admission to the program, but should help the student understand what to expect and also enable them to study up on the topics beforehand in order to avoid steep learning curves. They can also use this information to select courses that further enhance their knowledge in those areas.

Representatives from the program committee can provide a list of literature and digital resources that can be used for self-study.

Universal Design of ICT

Specialization courses (SPEC)

• ACIT4910 - User Diversity and ICT barriers (1st Semester)
• ACIT4920 - Universal Design of Interactive Systems (1st Semester)
• ACIT4930 - Interaction Styles and Technologies for Accessibility (2nd Semester)

This specialization focuses on identifying disabling ICT barriers and developing universally designed ICT solutions that can be used by as many people as possible, including people with disabilities, so that all citizens can take an active part in social activities, education and employment. This combines an understanding of diversity among users, situations and equipment, human-computer interaction, assistive technologies, and methods for universal design of ICT solutions, as well as knowledge of relevant national and international legislation, guidelines and standards.

With an emerging e-society, it is becoming essential that all electronic information and services are accessible for all, regardless of the device, the situation, or the abilities of the user. In Norway and many other countries, providing ICT solutions accessible for as many people as possible is becoming a legal requirement. This poses great challenges for competent ICT professionals and society's ICT infrastructure and services. This specialization aims to meet the growing need of society for knowledge and expertise in universal design of ICT solutions such as web and mobile applications, e-services, e-commerce and self-service machines.

The specialization track objectives for Universal Design of ICT are:

• The student will acquire advanced knowledge of universal design and specialist knowledge of ICT, and learn how to analyze problems and solutions based on the history, traditions, characteristics and societal context of universal design and ICT
• The student will gain skills in evaluating usability and accessibility of existing ICT systems and develop ICT solutions that are accessible and usable for as many people as possible
• The student will understand how universally designed ICT solutions can positively affect a person's opportunities for actively taking part in a digitized society and can communicate this to both specialists and the general public

In this specialization, the first two courses focus on the fundamentals of universal design of ICT. In the course "ACIT4910 - User Diversity and ICT barriers", topics covered include differences in user requirements due to diversity among users, situations and devices, as well as how to identify disabling barriers. National and international guidelines, regulations and legislation relevant to universal design of ICT are also covered. In the course "ACIT4920 - Universal Design of Interactive Systems", topics covered include the design of cost-effective prototypes, how to involve and communicate with users in the design process, and evaluating prototypes through user testing with diverse users.

In the second semester, the course "ACIT4930 - Interaction Styles and Technologies for Accessibility" focuses on technology and methods within human-computer interaction and available computer systems, including topics such as multimodal user interfaces and issues in interactions related to context, such as accessibility in public spaces, mobility problems, and the user's affective state.

The following electives are suggested: “ACIT4090 Globalization of Technology”, "ACIT4080 Intelligent User Interfaces" and "ACIT4045 Projects in Human Computer Interaction". This specialization can be also be supplemented with other specialization courses from other tracks such as Agile Service Management and Developer Operations or Problem solving with scripting as well as ACIT4025 Seminar in Cyber Security and privacy.

Recommended, but not required, prior knowledge:

• Human-computer interaction and interaction design
• Inclusive design
• Universal design
• User experience design
• User-centred design

Cloud-based Services and Operations

Specialization courses (SPEC)

• ACIT4410 - Agile Service Delivery and Developer Operations (1st Semester)
• ACIT4420 - Problem solving with scripting (1st Semester)
• ACIT4430 - Infrastructure Services and Operations (2nd Semester)

Today, the cloud is an essential platform for services that need to display automated and agile features. Software engineering is not enough, as that focuses much on the process of designing and developing software. What is needed in addition is a thorough technical foundation as well where the entire platform stack is covered.

This specialization focuses on the process of developing, deploying and managing large-scale services. This combines an understanding of how modern development teams work, how parts of the development process can be automated in order to achieve higher efficiency and finally how a service can be supported by an operations infrastructure in order to make it robust and flexible enough to scale to a world audience. A practical focus will be found in all three specialization courses, aiming to deliver technical competence as well as an birds-eye view of how the IT industry and academia meets the demand of a digitized society.

The specialization track objectives for Cloud-based Services and Operations are:

• The student will learn the role large-scale cloud-based services play in a digitized society
• The student will gain technical skills and unique knowledge to become a valuable member of software engineering or operations teams
• The student will understand the current challenges of cloud-based operations and can discuss them

In this specialization, the first two courses focus on how to package and deploy services in cloud-based environments as well as how to develop scripts for automating that process. This builds naturally on general IT programming, web-development and software engineering topics commonly found in bachelor programs. The course "ACIT4410 - Agile Service Delivery and Developer Operations" is a unique introduction into the most modern ways services are deployed and managed, covering topics such as containers, IaaS, PaaS, scaling and Site Reliability Engineering (SRE). In the course "ACIT4420 - Problem solving with scripting" the students can put their new knowledge into projects that allow them to build sophisticated frameworks for automated service management.

In the second semester, the course "ACIT4430 - Infrastructure Services and Operations" focuses on how to build a wider scaffolding of robustness around a service by providing features such as monitoring, configuration management, centralized logging and backup. This course aims to enhance the knowledge from the previous semester with a deeper understanding on how to make a service function well over its entire lifecycle as well as provide a better understanding of how operations teams work to achieve it.

This specialization can be supplemented with more core infrastructure courses, such as Enterprise networking and security. Many other specialization courses from other tracks will also work favourably with these topics as internet-based services and agile software delivery are a relevant element of most of the IT industry.

Recommended, but not required, prior knowledge

• Basic operating systems concepts
• Networking
• Web Programming
• Basic Linux/Mac OS command-line
• Version Control Systems, like Git

Data Science

Specialization courses (SPEC)

• ACIT4510 - Statistical Learning (1st Semester)
• ACIT4620 - Computational Intelligence (1st Semester)
• ACIT4530 - Data Mining at Scale (2nd Semester)

In Data Science you will find elements of Big Data, Statistics and Machine Learning. With the vast amount of data available to us from all forms of electronic devices and systems, the challenge remains to extract knowledge and wisdom from it. Examples of current challenges where data analysis is needed are: self-driving vehicles that share information and learn from each other, climate data from across the globe, financial transactions from millions of bank customers or genomic datasets from gene banks. As technology continues to spread into every nook of our lives, new data about us is generated. Even though valuable insight can be found, the need to protect the data and understand the ethical ramifications of its use becomes ever more important.

Being a Data Scientist means having practical skills in order to set up and use advanced Big Data databases, next it requires competence in statistics in order to know what methods of analysis are most applicable. Finally, it requires the ability to automate the analysis to be turned into a tool that can be used by others on future, similar datasets.

The specialization track objectives for the Data Science track are:

• The student will learn to utilize statistical methods on large data sets in practice
• The student will get practical experience on state-of-the-art BigData systems
• The student will get theoretical background in the algorithms and techniques used in Data Science
• The student will program their own analysis tools based on the methods they have learned to be used on large data sets

The two specialization courses for this track, "ACIT4510 - Statistical Learning" and "ACIT4530 - Data Mining at Scale", provide a foundation of multiple methods of structuring and analyzing datasets. This involves topics ranging from statistics, machine learning and pattern mining as well as becoming familiar with platforms that can be used to store, organize and run computations on the data. The specialization course "ACIT4620 - Computational Intelligence", will be shared with the Applied Articifial Intelligence specialization and allow students to integrate AI concepts.

Students from this track will find interesting connections to all the other specialization tracks. Courses from Applied Artificial Intelligence and Mathematical Modelling and Scientific Computing will let the student go deeper on the learning and analysis aspects. Courses from «Cloud-based services and operations» will be interesting for students who want to focus more on the technical aspects and management of BigData architectures. Other specialization tracks will offer relevant use cases for Data Science, such as health data from Biomedical Engineering, sensor data from Robotics and Control.

Recommended, but not required, prior knowledge

• Database systems
• Basic statistics like probability theory and common tests
• Basic programming
• Linear algebra
• Algorithms and data structures

Mathematical Modelling and Scientific Computing

Specialization courses (SPEC)

• ACIT4310 - Applied and Computational Mathematics (1st Semester)
• ACIT4321 - Quantum Information Technology (1st Semester)
• ACIT4330 - Mathematical Analysis (2nd Semester)

Mathematical models are widespread in science and engineering and, as we spend much of our time on the internet, even surround us in everyday life. Whenever we want to create a representation of the real world that allows us to investigate and simulate it, we reach for mathematics as a language. Today, mathematicians work alongside engineers and scientists to address major challenges in our world, such as understanding and predicting the effect of climate change or describing both atomic building blocks and the vast limits of the universe. Mathematicians work in the computer game industry to provide models of how physics affects artifacts or how economies within games can be balanced. The most powerful supercomputers in the world are designed for that purpose only: run vast mathematical computations. Common for these examples is that the expert both has a solid mathematical skillset to utilize on their context but also the ability to translate the relevant models into technical solutions. Programming mathematical models into executable tools for analysts is a central ingredient in this specialization.

The mathematical modelling and scientific computing specialization prepares students for developing and treating models in their own projects and for jobs in research, industry and IT where mathematical modelling and simulation is essential. The typical student will know her way around theoretical mathematics and use this theory to implement mathematical and computational methods on a computer, run numerical simulations and interpret simulation results in terms of the problem at hand. Thus, students in this specialization should become proficient in all parts of the modelling process.

Students in the Mathematical Modelling and Scientific Computing Specialization track will:

• build a substantial portfolio of analytical techniques and computational methods and be able to implement these methods for scientific computing
• gain insight into how mathematical models are built and be aware of strengths and limitations of mathematical modelling
• learn how to apply theory and interpret model results in the context of science and engineering

The introductory course in the Mathematical Modelling and Scientific Computing specialization ("ACIT4310 - Applied and Computational Mathematics") focuses on the model concept, why we need models, and how we apply various mathematical and computational methods to analyze and simulate models. The course "ACIT4321 - Quantum Information Technology" will introduce students to key concepts within classical information theory the fundamentals of quantum phenomena, and be trained to create their own quantum algorithms, simulate quantum systems, and implement the corresponding programs on classical and quantum computers. Prior knowledge in quantum physics is not required. In the second semester, the course "ACIT4330 - Mathematical Analysis" provides a deeper understanding of mathematical concepts and gives the theoretical background of many of the results used in the first courses.

There is a range of relevant specialization courses from other tracks that can be suitable for Mathematical Modelling and Scientific Computing students. Mathematically oriented courses include "ACIT4610 - Evolutionary AI and robotics" and "ACIT4530 - Data Mining at Scale: Algorithms and Systems" from the artificial intelligence and data science tracks, respectively, and the course "ACIT4710 - Digital Signal and Image Processing Analysis" from the biomedical engineering track. The course "ACIT4410 - Agile Service Delivery and Developer Operations" is a useful supplement if packaging the analysis into tools and products is of interest. Students who wish to improve their programming skills could benefit from taking the course "ACIT4420 - Problem-solving with scripting" (several tracks).

Recommended, but not required, prior knowledge

• Basics of numerical methods
• Basic mathematical analysis
• Basic physics
• Basic programming

Applied Artificial Intelligence

Specialization courses (SPEC)

• ACIT4610 - Evolutionary artificial intelligence and robotics (1st Semester)
• ACIT4620 - Computational Intelligence (1st Semester)
• ACIT4630 - Advanced machine learning and deep learning (2nd Semester)

This specialization focuses on the understanding, the development, and the application of artificial intelligence methods and tools to solve a variety of real world problems. Artificial intelligence will revolutionize the way people live and work. This specialization gives you the advantage to work with cutting edge technologies and acquire the skill required in the present and in the future. During your studies, you will learn state-of-the-art algorithms and tools within artificial intelligence, such as deep learning, reinforcement learning, as well as evolutionary and biologically inspired algorithms, swarm intelligence, and other methods used in research and in the industry. You will not only learn the methods and theory, but you will also focus on practical projects that will give you the necessary experience and expertise to apply the methods to solve problems in different domains.

The specialization track objectives for Applied Artificial Intelligence are:

• The students will learn the foundation and inspiration of modern artificial intelligence methods and tools
• The students will gain practical experience and technical skills in applying artificial intelligence to solve problems in different areas
• The students will be able to understand the challenges and implications of applied artificial intelligence, and the impact AI can have on society, work, and daily life.

ACIT4620 - Computational Intelligence will provide prerequisite/foundational materials to support other two more advanced courses on "deep learning" and "evolutionary AI". In this way, the students will have a complete, holistic and coherent understanding of the AI field.

The course in "ACIT4610 - Evolutionary artificial intelligence and robotics" will provide the basis for modelling and analyzing complex systems, programming and controlling them with biologically inspired artificial intelligence, swarm intelligence, and evolutionary robotics. This course will provide insight into creating autonomous machines and systems that can adapt, evolve, and learn over time.

In the second semester, the course "ACIT4630 - Advanced machine learning and deep learning" focuses on how to use advanced AI algorithms that allow computers and machines to learn through deep learning and reinforcement learning. Such state-of-the-art techniques are currently used to perform difficult cognitive tasks at a level that is often superior to humans, such as pattern recognition and diagnosis in medical images, self-driving cars, or natural language understanding. During this course, the students will apply machine learning to solve problems in domains of their interest.

This specialization can be supplemented with the "ACIT4040 - Applied artificial intelligence project" where students in teams will develop a complete artificial intelligence system from scratch, or with the course on "ACIT4030 - Machine learning for images and 3d data" which will focus on AI applications in the domain of graphics and computer vision. Several other specialization courses from other tracks can complete the program with relevant skills within data science, mathematics or robotics.

Recommended, but not required, prior knowledge

• Programming (e.g. Python)
• Bachelor level knowledge of linear algebra
• Bachelor level knowledge of vector calculus
• Basic statistics and probability

Robotics and Control

Specialization courses (SPEC)

• ACIT4810 - Advanced Methods in Modelling, Simulation, and Control (1st Semester)
• ACIT4820 - Applied Robotics and Autonomous Systems (1st Semester)
• ACIT4830 - Special Robotics and Control Subject (2nd Semester)

The specialization in Robotics and Control focuses on understanding the technologies and methodologies behind modern robots, drones, advanced industrial process control and autonomous systems in general. Robotics is becoming increasingly important for home, industrial, transport and medical applications. The deployment of autonomous self-guiding vehicles, including autonomous ships and drones, is expected to grow massively in the coming years with the need for highly skilled professionals. The specialization combines traditional robotics and control systems with novel computing technologies, such as artificial intelligence and machine learning. These skills are extremely relevant for current and future companies working on product development, smart manufacturing technologies, and Industry 4.0.

During the study, you will obtain knowledge in key topics such as dynamic systems, control theory, sensory feedback and information processing, electromechanical design, and real time software development.

The specialization track objectives for Robotics and Control are:

• The student will learn the theories, technologies, and methodologies used in modern robotics and control systems
• The student will gain hands-on practical experience and technical skills in implementing robotics and control methods to solve real-life problems
• The student will understand the different aspects needed to develop robotic and intelligent systems, and to use them to create innovative solutions and solve societal challenges.

In this specialization, the first two courses focus on the fundamentals of modern robotics and control systems. The course "ACIT4810 - Advanced Methods in Modelling, Simulation, and Control" provides the mathematical foundations to understand, analyze, and implement modern control systems. This includes data driven dynamic modelling, multivariable and predictive control algorithms, and the combination of traditional control theory and AI-based methods. The course "ACIT4820 - Applied Robotics and Autonomous Systems" provides a hands-on overview of common theories and methods used in the design of robotic and autonomous systems. This includes state estimation, navigation, motion planning, computer vision, and implementation using Robot Operating System (ROS).

The specialization course aims at providing an arena where students can learn about specific technologies and methods that are relevant for their master project. Suggestion for these themes can be varied from special applications within robotics and control theory, Applied AI methods and Machine Learning, IoT (sensor/ actuator) systems for both autonomous vehicles and distributed systems, embedded systems, and industrial process control to name a few.

The course "ACIT4020 - Aerial Robotics" provides a hands-on overview of common theories and methods used in the design of autonomous and remotely piloted aerial robotic systems.

There is a range of relevant specialization courses from other tracks that can be suitable for Robotics and Control students. For instance, "ACIT4310 - Applied and Computational Mathematics", "ACIT4420 - Problem solving with scripting", "ACIT4630 - Advanced Machine Learning and Deep Learning", "ACIT4040 - Applied Artificial Intelligence Project", "ACIT4015 - Internet of Things", "ACIT4510 - Evolutionary AI and Robotics", "ACIT4720 - Medical Sensors and Actuators", "ACIT4710 - Digital Signal and Image Processing Analysis", "ACIT4030 - Machine Learning for images and 3D data", and "ACIT4080 - Intelligent User Interfaces".

Recommended, but not required, prior knowledge

• Basic knowledge on Electronics
• Basic knowledge on Control Systems and mathematical modeling
• Basic knowledge in Calculus, Statistics, and Linear algebra
• Basic knowledge on programming

Biomedical Engineering

Specialization courses (SPEC)

• ACIT4720 - Medical sensors and actuators (1st or 3rd Semester)
• ACIT4710 - Digital Signal and Image Processing Analysis (1st or 3rd Semester)
• ACIT4730 - Special biomedical engineering subject (2nd Semester)

Biomedical engineering studies ways to improve the diagnostics, therapy, care, rehabilitation and life quality by researching and developing diagnostic and therapeutic devices, equipment, implants, medical imaging systems as well as pharmaceuticals. This specialization in particular involves the hardware and software design of devices and systems used to measure biological signals and activities. This ranges from developing sensors that can capture a biological signal of interest, to applying methods of amplifying and filtering the signal so that it can be further studied, to dealing with sources of interference that can corrupt a signal, to building a complete instrumentation system such as an x-ray machine or a heart monitoring system.

The objectives for this specialization in Biomedical Engineering can defined as following:

• The student will acquire advanced knowledge in hardware and software design and learn how to analyse different problems related to biology and medicine and implement those solutions in a cross disciplinary field.
• The student will gain skills in evaluating existing instrumentations and systems that are applied in the laboratories and clinics, and develop specific solutions that are ideally innovative and practically anchored.
• The student will understand how different hardware and software approaches are applied in a field where the challenges are created by the diversity and complexity of living systems, which require creative, knowledgeable, and imaginative solutions.

In this specialization, the first two courses focus on the fundamentals of the instrumentation, sensors, and measurement's techniques. The course "ACIT4720 - Medical sensors and actuators" focuses basics of measurements techniques with examples of different sensory schemes that are applied in biomedical applications. In the course "ACIT4710 - Digital Signal and Image Processing", topics covered include different signal processing and sampling approaches are discussed. Reconstruction algorithms for medical imaging will be included, as well as post-processing algorithms for augmented interpretation of the images.

The course "ACIT4730 - Special biomedical engineering subject" focuses on specific technology and methods that the candidate may be involved specifically through the master project.

Suggestion for these themes can be varied from special applications within diagnostic and prognosis. Other suggested themes can be embedded systems, multivariate analysis techniques, design of optical fibres, mechatronics systems, and design of lab on chip or CD with focus on biomarkers.

In addition, the following elective courses are suggested: "ACIT4015 - Internet of Things", "ACIT4030 - Machine Learning for images and 3D data", "ACIT4080 - Intelligent User Interfaces", "ACIT4035 Rehabilitation and assistive devices" from the Department of Information Technology at OsloMet.

This specialization can be supplemented with many other specialization courses from other tracks such as Robotics, ACIT4040 - Applied AI projects, ACIT4630 - Advanced Machine Learning and Deep Learning, ACIT4530 - Data Mining at Scale: Algorithms and Systems and ACIT4320 - Quantum Information Technology. Students who wish to improve their programming skills could benefit from taking the course "ACIT4420 - Problem-solving with scripting" and "Scripting for automation". In addition, subjects "Biomechanics", and "Cellular physiology" from faculty of health sciences could be complementary in this specialization.

Recommended, but not required, prior knowledge:

• Anatomy and physiology
• Electronics
• Biomedical equipement
• Electrical safety
• Basic programming

Short and long thesis

The thesis project is the keystone of the program for every candidate. Here, they will embark on an individual research project that is unique to them, their interest and specialization. The program offers two options for the thesis project: a short and a long thesis. So-called external projects, where a company or organization is a stakeholder in the project are also possible under the right circumstances. In all cases, the project proposal will go through a quality assurance process and the student will be assigned a local supervisor.

The short thesis project is 30 ECTs and will be in the final semester of the program. The topics for these projects can be initiated by students or be selected from a list of available projects offered from the faculty. Students are generally recommended to select a short thesis if they prefer to increase their breadth with more elective courses and find it more suitable for them to focus on the thesis in a single semester.

During the short thesis project, the student will give a mid-term presentation to report on their progress and solicit feedback from a larger group than their immediate partners and supervisors. This will also provide assurance that all students are on track.

The long thesis is 60 ECTs and therefore half of the entire time spent in the program. In addition, the project work is divided over more than two semesters allowing for a maximum time to reflect upon the work. In the final semester, all the time is focused on the thesis project, just like the short thesis. Long thesis projects go deeper into a single topic and should only be embarked if there is ongoing research in that specific topic at any of the research groups involved in the program in order to secure adequate supervision and quality. One will expect the candidate to become a participant of one of the research groups associated with the program and see their project as a part of a larger research effort at OsloMet. Students can therefore not propose their own long thesis project unless it is in collaboration with a faculty member and research group.

A long thesis is recommended for students who enjoy working independently for longer periods and who target an academic career later. Even though there is no formal difference with a long and short thesis with regard to qualifying for a PhD scholarship, the long thesis project has additional requirements with regard to writing a scientific paper as part of their project. A potential publication would normally be an advantage when applying for a PhD scholarship.

The long thesis project is divided into three phases, which are organized as separate courses. This structure ensures that there are concrete deliverables during the entire thesis project and not just at the end. During the first phase, students are to complete a literature survey as well as develop a suitable problem statement and approach for the project. In the second phase, much of the data gathering and development will take place, which requires the student to showcase their results and preliminary analysis. In the final phase, most of the writing takes place and the student will deliver a final thesis along with a research paper. For more details, please consult the course descriptions for these phases further down.

Elective courses

ACIT offers several elective courses that have their origin in one of the specialization tracks, but can be of interest to all students. The elective courses can create interesting and complementing combinations of knowledge and skills for the individual student based on their particular interest. For many, they may give a necessary depth in a topic that they want to focus on in the thesis project later.

Elective courses are in principal only available for students who elect a short thesis structure rather than a long, since in the long thesis most of the time will be spent on the project. In broad terms, the elective courses offer more breadth with three extra courses while long thesis offers more depth into a single topic and it is up to the individual student to elect their most suited plan. Students do not have to decide on the elective courses they want to take at the beginning of the programme but can wait until their interests mature.

Not all elective courses are available at any time. Whether an elective course is run depends on the overall student interest and semester. Students are not guaranteed that an elective course will be offered if the number og assigned students is low. The faculty will work together with the students to collect interest in the specific courses in good time for the students to make adequate choices.

Students can in principle use courses from other MSc programs at the Faculty of Technology, Art and Design or external institutions as elective courses provided they have a relevance to ACITs overall profile. This will allow students to explore other professional perspectives that normally interact extensively with the design, development and use of technology. The student has to investigate possible courses and apply the program council for approval in good time, generally in the middle of the preceding semester. Whether the student is granted access to the course depends on the availability at the other program and if the sufficient prerequisite knowledge is met.

Møter mellom studenter fra ulike kulturer kan gi tilleggskompetanser for yrkesutøvelse i vårt flerkulturelle samfunn. Gjennom aktivt å inkludere kulturkunnskap i programmet forberedes studentene til den nye virkelighet; at globalisering av arbeidsmarkedet gjør internasjonal erfaring, språk- og kulturkunnskap samt endringskompetanse stadig viktigere.

Instituttet har en aktiv utvekslingspraksis og tilrettelegger ellers for internasjonalisering ved at:

• studentene kan ta deler av utdanningen ved en av instituttets samarbeidsinstitusjoner i utlandet
• utenlandske studenter kan ta deler av sin utdanning ved instituttet
• 4. semester er internasjonalt semester der undervisning og faglitteratur primært er på engelsk
• 5. semester er tilrettelagt for utveksling ved at både undervisning og faglitteratur i stor grad er på engelsk
• internasjonalisering hjemme er vektlagt blant annet gjennom integrering av utenlandske utvekslingsstudenter i klassen
• kulturkunnskap er en viktige del av undervisningen.

Prosedyre for utveksling

Universitetet har en internasjonal seksjon som arbeider med studentutveksling, se OsloMets nettsider. Instituttet er ansvarlig for den faglige forhåndsgodkjenningen av studentene før utreise: https://student.oslomet.no/en/hvor-nar

For oppdatert oversikt over samarbeidsavtaler, se OsloMets nettsider.

Møter mellom studenter fra ulike kulturer kan gi tilleggskompetanser for yrkesutøvelse i vårt flerkulturelle samfunn. Gjennom aktivt å inkludere kulturkunnskap i programmet forberedes studentene til den nye virkelighet; at globalisering av arbeidsmarkedet gjør internasjonal erfaring, språk- og kulturkunnskap samt endringskompetanse stadig viktigere.

Instituttet har en aktiv utvekslingspraksis og tilrettelegger ellers for internasjonalisering ved at:

• studentene kan ta deler av utdanningen ved en av instituttets samarbeidsinstitusjoner i utlandet
• utenlandske studenter kan ta deler av sin utdanning ved instituttet
• 4. semester er internasjonalt semester der undervisning og faglitteratur primært er på engelsk
• 5. semester er tilrettelagt for utveksling ved at både undervisning og faglitteratur i stor grad er på engelsk
• internasjonalisering hjemme er vektlagt blant annet gjennom integrering av utenlandske utvekslingsstudenter i klassen
• kulturkunnskap er en viktige del av undervisningen.

Prosedyre for utveksling

Universitetet har en internasjonal seksjon som arbeider med studentutveksling, se OsloMets nettsider. Instituttet er ansvarlig for den faglige forhåndsgodkjenningen av studentene før utreise: https://student.oslomet.no/en/hvor-nar

For oppdatert oversikt over samarbeidsavtaler, se OsloMets nettsider.

Bestemmelser om eksamen er gitt i lov om universiteter og høgskoler og forskrift om studier og eksamen ved OsloMet. https://lovdata.no/dokument/SF/forskrift/2012-06-26-718

Muntlig og praktiske eksamener skal ha to sensorer da disse eksamensformene ikke kan påklages. Formelle feil kan likevel påklages.

Eksamener som kun sensureres internt, skal jevnlig trekkes ut til ekstern sensurering.

Vurderingsuttrykk ved eksamen skal være bestått/ikke bestått (B/IB) eller en gradert skala med fem trinn fra A til E for bestått og F for ikke bestått.

Studieprogresjon

Se forkunnskapskrav til det enkelte emne. For å kunne levere bacheloroppgaven må studenten ha bestått samtlige øvrige emner i studiet.

Bacheloroppgave

I BAPD3910 skal studentene gjennomføre og dokumentere et designprosjekt hvor de tar ansvar for egne valg og benytter seg av kompetansen de har bygget opp gjennom hele studieforløpet. De skal videreutvikle seg, vise at de behersker designprosessen og synliggjøre sitt kompetansenivå som produktdesignere.

Det fleirkulturelle og internasjonale perspektivet er forankra i alle fag og alt lærararbeid i lærarutdanninga. Utdanninga førebur studenten på det internasjonale og fleirkulturelle læringsmiljøet som ventar i praksisperiodane og i framtidig yrke som lærar blant anna gjennom bruk av relevant og komparativ internasjonal forsking, faglitteratur og nettressursar.

Det blir lagt til rette for at delar av utdanninga kan takast i utlandet gjennom dei internasjonale avtalane lærarutdanninga har etablert. Dette gjeld både fagstudium og praksisopphald. 6. semester er spesielt tilrettelagt for utvekslingar, men det er også mogleg andre semestre avhengig av eige fagval. Det 6. semesteret, det internasjonale semesteret, er ikkje bare lagt opp med tanke på utveksling, men også med internasjonalt perspektiv for studentar som studerer heime. Det fagovergripande temaet dette semesteret er internasjonal utdanning. Læringsarbeidet på campus nyttar den ressursen internasjonale studentar og dei utanlandske forelesarane som lærarane våre samhandlar med, innehar. Det vil også bli trekt vekslar på kompetansen til internasjonale organisasjonar som Redd Barna og Røde Kors. Pensum i alle fag inneheld oppdatert litteratur på engelsk. Det er i løpet av studiet mogleg å velje engelskspråklege emne der interne studentar møter internasjonale studentar. Den internasjonale dimensjonen blir tillagt auka vekt gjennom utdanningsløpet. I kvar fagplan er det beskrive korleis dei ulike studiefaga arbeider med internasjonalisering.

Arbeidskrav skal vere levert/utført innan fastsette fristar. Gyldig fråvær dokumentert med for eksempel sjukemelding, gir ikkje fritak for å innfri arbeidskrav. Studentar som på grunn av sjukdom eller annan dokumentert gyldig årsak ikkje leverer/utfører arbeidskrav innan fristen, kan få forlenga frist. Ny frist for å innfri arbeidskrav blir avtalt i kvart enkelt tilfelle med den aktuelle læraren.

Arbeidskrav skal vurderast til "Godkjent" eller "Ikkje godkjent". Studentar som leverer/utfører arbeidskrav innan fristen, men som får vurderinga "Ikkje godkjent", har høve til to nye innleveringar/forsøk. Studenten må da sjølv avtale ny innlevering av det aktuelle arbeidskravet med faglæraren. Studentar som ikkje leverer/utfører arbeidskrav innan fristen og som ikkje har dokumentert gyldig årsak, får ingen nye forsøk. 

Nærare informasjon om arbeidskrav finst i den enkelte fag- og emneplanen.

Krav om deltaking

Krav om deltaking er nedfelt i den enkelte fag- og emneplanen og i plan for praksis. Det kan også vere krav om deltaking knytt til fagovergripande periodar. Disse blir konkretisert i årsplanen for studieåret som er klar ved byrjinga av studieåret.

Studenten møter ulike vurderingsformer i løpet av studiet. Dette har to føremål, både å kunne vurdere alle sider ved lærarkvalifiseringa, samt å gi studentane erfaring med ulike vurderingsmetodar som er relevant for det seinare arbeidet i skulen, læring og vurdering av elevar. Nærare informasjon finst i den enkelt fag- og emneplanen.

Læringsmiljø og studieinnsats

Femårig grunnskulelærarutdanning er eit fulltidsstudium som kvalifiserer for kyndig yrkesutøving. Gode læringsprosessar er den beste motivasjonen til innsats. Tiltak som utdanninga vil satse spesielt på for å sikre ein høg studieinnsats:

• Relevant undervisning som er kopla til for- og etterarbeidet til studentane, aleine og i grupper.
• Vurderingsordningar og vurderingskriterium er tydeleg relevante for framtidig yrke og knytt til jamt arbeid gjennom semesteret.
• Det blir lagt til rette for jamn arbeidsbelastning gjennom semesteret, blant anna ved at arbeidskrav og andre oppgåver er tilgjengelege i god tid.
• Studiemiljøet blir jamleg evaluert, og tiltak vurderte og følgde opp.
• Studentane blir årleg kalla inn til ein kontaktlærarsamtale.
• Studentane vel ved starten av kvart studieår representantar til fagutval for faga. Fagutvala møter faglærarar og studieleiar med jamne mellomrom.
• Fagutvala, studieevalueringane og kontaktlærarsamtalane er viktige element i kvalitetssikringssystemet på høgskulen.

Progresjonskrav i studiet

For å kunne starte i tredje studieåret må studenten ha bestått minst 90 studiepoeng frå dei to første studieåra i utdanninga. Alle eksamenane frå det første studieåret må være bestått. Kravet må vere oppfylt seinast ved utløpet av ordinær eksamen i vårsemesteret i andre studieåret.

For å kunne starte i fjerde studieåret må alle eksamenane til og med femte semester være bestått. Kravet må vere oppfylt seinast ved utløpet av ordinær eksamen i vårsemesteret i tredje studieåret. 

I tredje studieåret skal studentane skrive ei profesjonsretta FoU-oppgåve i kombinasjon mellom eit undervisningsfag og faget pedagogikk og elevkunnskap. Oppgåva må vere bestått før studentane kan begynne på masteroppgåva.

Bestemmelser med omsyn til bestått praksis

Progresjonskrav

Praksisopplæringa i første studieår må vere bestått før studenten kan fortsette i andre studieår. Tilsvarande krav gjeld for alle studieår i syklus 1. Praksis tredje år syklus1 må vere bestått før studenten kan fortsette på syklus 2.

Antall forsøk bestå praksis

Dersom praksisstudiet ikkje er bestått og det er brukt to forsøk, må normalt studiet avbrytes, (jf. forskrift om studiar og eksamen ved Høgskolen i Oslo og Akershus § 8-4).

Å vere skikka

Lærarutdanningsinstitusjonane har ansvar for å vurdere om studentane er skikka for læraryrket. Løpande vurdering går føre seg gjennom heile studiet og inngår i ei heilskapleg vurdering av dei faglege og personlege føresetnadene for å kunne fungere som lærar. Ein student som utgjer ei mogleg fare for elevars liv, fysiske og psykiske helse, rettar og tryggleik, er ikkje skikka for yrket. Studentar som viser manglande evne til å meistre læraryrket, skal så tidlig som mogleg i utdanninga bli informert om dette. Dei skal eventuelt få råd og rettleiing slik at dei kan forbetre seg, eller få råd om å avslutte utdanninga. Konkrete vedtak om studenten er skikka som lærar, kan fattast gjennom heile studiet.