Master Programme in Applied Social Sciences - Programme Option Child Care, part-time Programme description

Studieretningen barnevern retter seg mot personer med ulik relevant profesjonell fagbakgrunn som ønsker utdypet kunnskap om barnevernsfeltet og analytisk kompetanse for å utforske barnevernrelaterte fenomen.

Studieretningen gir innsikt i samfunnsforholdenes betydning for barns velferd og livsforhold. Den viser sammenhenger mellom velferdsordninger, sosialpolitiske beslutninger og barns konkrete hverdagsliv. Dette er en masterutdanning som forstår barnevern i vid betydning. Studieretningen har som mål å styrke et bredt fagfelt der barnevern gis betydning ikke bare innen et spesifikt barnevern, men generelt i velferdsforvaltningen, sosialtjenesten, helsesektoren og i barns oppvekstinstitusjoner.

Barnevern er et samfunnsmessig virksomhetsfelt hvor praksis baseres på implisitte og eksplisitte teorier fra en rekke fagdisipliner. Barnevern er en normativ virksomhet, og det er behov for et kritisk grunnlag for å vurdere hva som er gyldig og relevant kunnskap for praksis. Forståelser av barn og barns samfunnsmessige status er viktige premisser for barnevernets virksomhet til enhver tid.

Studieretningen kombinerer praksisnær og forskningsbasert undervisning.

Studiet kvalifiserer til

• stillinger i kommunal og statlig barneverntjeneste
• stillinger knyttet til utvikling- og forskningsprosjekter innen fagfeltet
• opptak til ph.d.-studier

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 the Content and Structure section, 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 international master's degree, the applicant must be able to document sufficient mastery of English. Please consult the current regulations at OsloMet for a complete overview: English proficiency requirements for master's - OsloMet

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. To ensure even representation in each of the specializations, if a country is overrepresented in applications (with the exception of applicants from Norway), the program reserves the right to assign a maximum of three students from each country to a specialization.

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.

Applied Artificial Intelligence

• 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

Biomedical Engineering

• 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
• BSc in StatisticsYou must also upload a motivation statement in a 3-minute video (MP4)

Cloud-based Services and Operations

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

Cyber security

• BSc in Computer science, Computer Engineering or Informatics
• BSc in Information technology or other equivalent qualifications, with at least 80 ECTS within the field of computer scienceYou also need a minimum of 10 ECTS in data security or a similar technical topics, such as network security, information security, mobile security, applied cryptography, privacy-enhancing technology or computer security management.

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, 10 ECTS in Statistics courses and 10 ECTS in programming

Mathematical Modelling and Scientific Computing

• 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

Robotics and Control

• 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.
• BSc in StatisticsYou must also upload a motivation statement in a 3-minute video (MP4)

Universal Design of ICT

• 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.

ACIT is a combination of courses and a thesis project at the end. Students can choose between a short or a long thesis project. 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. 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 any prerequisite knowledge requirements are met.

Requiring students to take a specialization topic outside their own specialization, gives them a broader scholarly perspective and provides a platform for interacting with students, teachers and researchers from other fields, increasing students' interdiscplinary knoweldge and skillsets.

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 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: 10 ECTS Common course + 2 x 10 ECTS Specialization course

Sem 2: 10 ECTS Common course + 10 ECTS Specialization course + 10 ECTS Master's Thesis Phase 1

Sem 3: 10 ECTS Alternative specialization course + 20 ECTS Master's Thesis Phase 2

Sem 4: 30 ECTS Master's Thesis Phase 3

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: 10 ECTS Common course + 2 x 10 ECTS Specialization course

Sem 2: 10 ECTS Common course + 10 ECTS Specialization course + 10 ECTS Elective

Sem 3: 10 ECTS Alternative specialization course + 2 x 10 ECTS Elective

Sem 4: 30 ECTS Master's Thesis

Common course content

ACIT has two common courses which are mandatory for all students in the program. The first common course, Understanding and Communcating Research 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. Students will be trained in design and innovation processes, focusing on the ability to interact with team members building on each others' respective knowledge and skills.

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.

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

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 specialization track objectives for Biomedical Engineering are:

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". 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

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 favorably 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

Cyber security

Specialization courses (SPEC)

• ACIT4050 – Applied Computer and Network Security (1st semester)
• ACIT4280 – Privacy by Design (1st semester)
• ACIT4290 – Practical Cyber Security (2nd semester)

Elective courses

• ACIT4025 - Cyber Security and Privacy (Autumn semester)
• ACIT4055 - Security politics, cyberwar and ethics (Spring semester)

The purpose of cyber security studies is to provide individuals with the knowledge and skills to help protect computer systems, networks, and other digital devices from unauthorized access, attacks, and theft. It also aims to promote safe and responsible use of technology, raise awareness about cyber threats and risks, and encourage proactive measures to prevent cyber incidents. Cyber security education helps individuals to understand the magnitude of cyber threats and their potential impact on personal and professional lives. It prepares them to identify, analyze, and mitigate cyber risks and vulnerabilities, and equips them with techniques to respond to cyber-attacks appropriately. Additionally, cyber security education supports the development of a skilled workforce in the field of cyber security to meet the growing demand for cyber security professionals.

The specialization objectives for Cyber Security are:

• The student will acquire advanced knowledge of defensive and offensive cyber security and learn how to analyze threats and vulnerabilities as well as to plan solutions and mitigations to those threats.
• The student will gain deep insights into information privacy and data protection and will learn risk and impact assessments methods as well as strategies and tools for designing data protection into data systems.
• The student will acquire professional ethics by studying the impact and consequences of cyber security, the lack thereof, and the consequences of mishandling personal information. Societal impact, ethical perspectives and cyber politics will second technical expert knowledge.

Recommended, but not required, prior knowledge for successful studies in cyber security:

General knowledge about information security from OsloMet’s course ITPE3100 Datasikkerhet (or equivalent courses with compatible curriculum).

Bachelor project with relationship to information security/information privacy

Introductory knowledge in cryptography and its applications (encryption, digital cash, blockchain, digital signatures, digital identities, and other topics)

Bachelor-level knowledge in programming, software engineering, networking

Ability to read and process literature in English.

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

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 - Robotics and Control Project" provides a hands-on experience with the application of novel theories and methods into a specific system in robotics and control.

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

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

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 of 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.

Etter å ha fullført studiet har kandidaten følgende kunnskap, ferdigheter og generell

kompetanse:

Kunnskap

Kandidaten har

• avansert kunnskap om og forståelse av samfunnsvitenskapelige teorier som er relevante for barnevernets virksomhetsområde
• inngående innsikt i ulike analytiske perspektiver på praksis, institusjoner og sosialpolitiske intervensjoner
• kan anvende sosialfaglig kunnskap på nye områder innenfor fagområdet
• kan analysere faglige problemstillinger med utgangspunkt i fagområdets historie, tradisjoner, egenart og plass i samfunnet

Ferdigheter

Kandidaten kan

• analysere barnevernfaglige fenomen og barnevernets praksis i lys av relevante samfunnsvitenskapelige teorier, begrep og forståelsesgrunnlag.
• kritisk analysere og reflektere over barnevernets komplekse mandat og funksjon i samfunnet
• anvende ulike data- og litteraturkilder for analyse
• delta i forskning og utviklingsarbeid knyttet til barnevernets fagfelt og/eller ulike brukergrupper, samt barnevernets betingelser og samfunnsmessige funksjoner
• gjennomføre en systematisk og kritisk analyse av en problemstilling, anvende sentrale forskningsprinsipper og gi en klar akademisk framstilling av resultatene
• vurdere metodiske fremgangsmåter for utforskning av fagfeltet

  Generell kompetanse

 Kandidaten har

• fordypet kunnskap om vitenskapens muligheter og begrensninger for barnevernsfeltet med en bevissthet om etiske aspekter ved forskning og fagutvikling i barnevern

 Kandidaten kan

• lese forskningslitteratur kritisk og reflektert
• vurdere problemstillinger knyttet til fag-, yrkes- og forskningsetikk
• formidle kunnskap og egne forskningsresultater skriftlig og muntlig
• anvende forskningsbasert kunnskap på områder knyttet til barnevernfaglig arbeid
• vurdere kritisk iverksettelser av sosialpolitiske tiltak som berører barn og familier

Studiet tilbys som heltid over to år og deltid over tre år. Studiebelastningen på deltid er 67%.

Strukturen og organisering av emner er tilsvarende for begge studentgrupper, men studiebelastningen er henholdsvis omlag 30 og 20 studiepoeng per semester for heltids- og deltidsstudentene.

All undervisning foregår på dagtid, og er ikke samlingsbasert. Undervisningen vil forsøksvis legges i bolker.

Foruten masteroppgaven består studiet av seks obligatoriske emner, og to valgfrie emner på til sammen 20 studiepoeng. Teoriemnene bygger på hverandre. Det anbefales derfor å ta emnene i den rekkefølge de er satt opp i oversiktstabellen.

Hensikten med valgemnene er at studenten skal tilegne seg spesiell kompetanse gjennom tematisk fordypning. Studenten skal utvikle analytisk kompetanse og videreutvikle sin forståelse og vurderingsevne innenfor det valgte temaet. 

Masteroppgaven er et selvstendig arbeid på 40 studiepoeng. Pågående forskningsprosjekter som studenter kan knytte seg opp til vil bli presentert i andre semester på instituttets mastertorg. I andre semester skal studentene utarbeide en prosjektskisse for masteroppgaven og deretter søke om veiledning. Heltidsstudenter ferdigstiller oppgaven i fjerde semester og deltidsstudenter i sjette semester.

Alle emner som inngår i graden må være bestått før studenten kan levere inn masteroppgaven til sensur.

Emner ved andre utdanninger og utdanningsinstitusjoner både i Norge og utlandet kan også godkjennes etter individuell søknad så lenge de oppfyller de faglige kravene i masterprogrammet.

Studentene blir presentert for varierte arbeidsformer og arbeidskrav. Gjentagende skriftlig arbeid skal trene reflektert, argumenterende og ryddig framstilling. Muntlige framlegg og diskusjoner skal oppøve studentene i dialogiske og drøftende ferdigheter. Studieretningen fremmer ikke minst faglig selvstendighet gjennom egenstudium og gjennomføring av et større vitenskapelig arbeid.

Større skriftlige oppgaver som semesteroppgave og hjemmeeksamen skal benytte APA-stil ved kildehenvisninger.

Studentene anbefales sterkt å danne litteraturkollokvier.

Masterstudiet i sosialfag - studieretning barnevern skal bidra til å øke studentenes forståelse for internasjonale forhold som berører studiets tematiske områder. Flere av emnene tar opp internasjonale forhold som har betydning for utvikling av sosiale problemer, så vel som mulige løsninger av disse. FNs konvensjon om barnets rettigheter er et dokument som danner grunnlag for diskusjon. Forståelser av barns beste analyseres i lys av vår tids kulturelle mangfold. Videre sees barnevern i lys av majoritets- og minoritetsmekanismer knyttet blant annet til andregjøring. Disse perspektivene kan identifiseres både i pensum og i undervisningsemner.

Det vil være muligheter for utenlandsopphold i 3. semester. Emnene ved den utenlandske utdanningsinstitusjonen må forhåndsgodkjennes av instituttet, for å sikre at de oppfyller de faglige kravene i masterprogrammet.

Studentene oppfordres til å følge med på tilbud som blir annonsert og selv undersøke faglig relevante ordninger. OsloMet - storbyuniversitetet har samarbeid med en rekke utenlandske universiteter og høgskoler.

Studenter kan også søke studier i utlandet på eget initiativ, men har da selv ansvar for å velge emner som kan godkjennes som del av en norsk mastergrad.

Studenter som ønsker å ta et semester i utlandet bør gjøre dette i samråd med studieadministrasjonen.

I henhold til Forskrift om studier og eksamen ved OsloMet - storbyuniversitetet § 5-1.2 kan det settes vilkår for å gå opp til eksamen.

Det fremgår av emnebeskrivelsene om det er satt opp arbeidskrav (herunder krav til obligatorisk nærvær) innenfor et emne. Arbeidskravet må være godkjent før studentene kan fremstille seg til eksamen. Dersom arbeidskravet ikke er levert eller ikke er godkjent mister studenten retten til å fremstille seg til eksamen i det enkelte emne.

For alle emner vil det være obligatorisk oppmøte første dagen om ikke annet er særskilt avtalt. Utover dette er det kun obligatorisk oppmøte dersom det kreves som del av et arbeidskrav.

Regler for deltakelse i gruppearbeid under arbeidskrav og eksamen

Deltagerne forplikter seg til å yte likeverdige bidrag til gruppen. Hvis det i løpet av arbeidsprosessen oppstår uenighet i gruppen mht. likeverdig bidrag/deltakelse i gruppearbeidet, skal saken umiddelbart tas opp med veileder/fagkoordinator og eventuelt tas videre til fagansvarlig. Dersom kravet om likeverdig deltagelse og frammøte ikke er oppfylt, defineres dette som «ikke godkjent» for den aktuelle studenten og han/hun får ikke gå opp til eksamen. Det gis normalt ikke mulighet til å levere en individuell besvarelse. Ny/utsatt eksamen må da gjennomføres neste studieår.

Det benyttes ulike vurderingsformer. Det framgår av emnebeskrivelsene hvilken vurderingsform som benyttes for det enkelte emne.

Det benyttes intern og ekstern sensor til vurdering av masteroppgaven. Til vurdering av de øvrige eksamenene benyttes ekstern sensor ved tvil om en besvarelse er bestått og til sensurering av et tilfeldig utvalg på 25 % av besvarelsene. Karakterene på de besvarelsene som er vurdert av ekstern sensor danner grunnlag for å fastsette nivå på besvarelsene innenfor de ulike karakteruttrykkene. Ved klage på sensur benyttes to nye sensorer, hvorav minst én ekstern til ny sensur.

Det benyttes gradert karakterskala med fem trinn fra A til E for bestått og F for ikke bestått.

Det fremgår av emnebeskrivelsene hvilke hjelpemidler som er tillatt på skriftlig skoleeksamen. Merk at det må søkes om å få bruke tospråklig ordbok på skoleeksamen. Se fakultetets nettsider om eksamen.

Studenter som ønsker det kan besvare alle skriftlige eksamener, inkludert masteroppgaven, på engelsk. Det blir ikke utgitt engelsk oppgavetekst for dem som ønsker å besvare på engelsk.

Studenten bes gjøre seg kjent med Lov om universiteter og høgskoler, og gjeldende forskrift om studier og eksamen ved OsloMet - storbyuniversitetet. Vi gjør særlig oppmerksom på regelverket om fusk under § 7-5 i forskriften, og utfyllende regelverk og presiseringer fra Klagenemnda under universitetets nettsider om eksamen og fusk.

Studenter ved Institutt for sosialfag skal benytte APA-stil ved kildehenvisninger i skriftlige arbeider.