MINF6100 Nation, State, and the Multi-national Society Course description

Det er arbeidskrav knyttet opp mot praksisstudiene. For nærmere informasjon, se vurderingsrapportene for de enkelte praksisperiodene.

Obligatorisk deltakelse

Se programplanen.

Etter fullført studium har kandidaten følgende læringsutbytte definert som kunnskap, ferdigheter og generell kompetanse:

Kunnskaper

Kandidaten

• har kunnskap om barnehager i Norge, herunder barnehagens egenart, historie, samfunnsmandat, lovgrunnlag og styringsdokumenter
• har bred pedagogikkfaglig og fagdidaktisk kunnskap, kunnskap om ledelse og tilrettelegging av pedagogisk arbeid og om barns lek, omsorg og læring
• har bred kunnskap om hvordan barns danning foregår, om moderne barndom, barnekultur, barns ulike oppvekstvilkår, bakgrunn og utvikling i et samfunn preget av språklig, sosialt, religiøst, livssynsmessig og kulturelt mangfold
• har bred kunnskap om barns språkutvikling, flerspråklighet, sosiale, fysiske og skapende utvikling og gryende digitale-, lese-, skrive- og matematikkferdigheter
• har bred kunnskap om barns rettigheter og om hva som kjennetegner et inkluderende, likestilt, helsefremmende og lærende barnehagemiljø
• har kunnskap om barn i vanskelige livssituasjoner, herunder kunnskap om vold og seksuelle overgrep mot barn og hvordan sette i gang nødvendige tiltak etter gjeldende lovverk
• har kunnskap om nasjonalt og internasjonalt forsknings- og utviklingsarbeid med relevans for barnehagelærerprofesjonen

Ferdigheter

Kandidaten

• kan bruke sin faglighet og relevante resultater fra forsknings- og utviklingsarbeid til å lede og tilrettelegge for barns lek, undring, læring, og utvikling og til å begrunne sine valg
• kan vurdere, stimulere og støtte ulike barns allsidige utvikling i samarbeid med hjemmet og andre relevante instanser
• kan bruke sin faglighet til improvisasjon i lek, læring og formidling
• kan vurdere, stimulere og støtte barns ulike evner, og ta hensyn til barns ulike bakgrunn og forutsetninger
• kan igangsette kreative prosesser og tilrettelegge for kultur- og naturopplevelser, med fokus på barns skapende aktivitet, helhetlige læring og opplevelse av mestring
• kan anvende relevante digitale og faglige verktøy, strategier og uttrykksformer i egne læringsprosesser, i pedagogisk arbeid, i samhandling med hjemmet og relevante eksterne instanser
• kan identifisere særskilte behov hos enkeltbarn, herunder identifisere tegn på vold eller seksuelle overgrep. På bakgrunn av faglige vurderinger skal kandidaten kunne etablere samarbeid med aktuelle tverrfaglige og tverretatlige samarbeidspartnere til barnets beste
• kan lede og veilede medarbeidere, reflektere kritisk over barnehagens praksis og justere denne
• kan finne, vurdere og henvise til informasjon og fagstoff og fremstille dette slik at det belyser en problemstilling

Generell kompetanse

Kandidaten

• kan utøve profesjonsetisk skjønn, særlig knyttet til ansvar, respekt og maktperspektiver
• kan planlegge, lede, gjennomføre, dokumentere, og reflektere over pedagogisk arbeid knyttet til barnehagens innhold og oppgaver i tråd med etiske krav og retningslinjer, og med utgangspunkt i forsknings- og erfaringsbasert kunnskap
• kan samarbeide og kommunisere både på bokmål og nynorsk på en kvalifisert måte i profesjonssammenheng
• kan trekke globale, nasjonale, regionale, lokale og flerkulturelle perspektiver, preget av respekt og toleranse, inn i barnehagens arbeid
• har endrings- og utviklingskompetanse, kan lede pedagogisk utviklingsarbeid og bidra til nytenkning og i innovasjonsprosesser for fremtidens barnehage
• kan formidle sentralt fagstoff muntlig og skriftlig, kan delta i faglige diskusjoner innenfor utdanningens ulike fagområder og dele sine kunnskaper og erfaringer med andre
• har evne til å samarbeide med tilsatte, lede barn og lede arbeide med andre ansatte.
• kan samarbeide med foresatte

Grunnlaget for vurdering er læringsutbyttebeskrivelser for den enkelte praksisperioden, og er konkretisert i en vurderingsrapport. Studentene blir vurdert med grunnlag i observasjon og skriftlig dokumentasjon knyttet til planlegging og gjennomføring av undervisning og skriftlige refleksjonslogger med egenvurdering. Både undervisningen og loggene drøftes med praksislærer. Studenten har selv ansvar for å arkivere egenvurderingen i en individuell praksismappe i universitetets digitale læringssystem.

Vurdering i praksis skal bidra til læring og utvikling hos studentene. Vurderingen skal foregå underveis i praksisperiodene, slik at studentene er orientert om hvordan de fungerer i forhold til forventet læringsutbytte i den enkelte praksisperioden. Midt i praksisperioden skal studentene få en midtveisvurdering av praksislærer.

Etter avsluttet praksisperiode skriver praksislærere en sluttvurdering. Vurdering av studenter i praksisstudiet er et felles ansvarsområde for faglærerne i lærerutdanningen, praksislærer og rektor ved praksisskolen. Det benyttes karakteren bestått/ikke bestått, og det er praksislærer som setter denne karakteren.

Studenter som står i fare for ikke å bestå en praksisperiode, skal ha skriftlig melding om dette senest midtveis i praksisperioden. Studenten innkalles snarest til møte med representant fra utdanningen, praksislærer og rektor. Studenter som vurderes til «ikke bestått» i en praksisperiode, kan likevel fullføre semesteret i utdanningen.

Ny praksisperiode gjennomføres neste gang ordinær praksis organiseres. Hvis praksis blir vurdert til «bestått» ved andre gangs forsøk, kan studenten gjenoppta studiet. Hvis praksisperioden blir vurdert til «ikke bestått» ved andre gangs forsøk, må studiet normalt avbrytes. For nærmere presisering av bestemmelsene, se § 8 i forskrift om studier og eksamen ved OsloMet.

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. 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.

OsloMet har et strategisk fokus på storby, mangfold og internasjonalisering, og flerkulturelle perspektiver er gjennomgående i hele barnehagelærerutdanningen. I studiet skal studentene forholde seg til faglitteratur på skandinavisk og engelsk språk. OsloMet tilbyr engelskspråklige fordypninger for studenter på alle bachelor programmene, der deltar også innreisende studenter. Disse fordypningene går i perioden august – desember, dvs. at det er andre undervisningsdager enn ellers på programmet.

Ordninger for utveksling finnes i fjerde studieår, hvor både fordypning og bacheloroppgave kan gjennomføres ved utdanningsinstitusjoner OsloMet har samarbeidsavtaler med. Å studere eller å ta praksis i utlandet kan gi studenten mulighet til å utvikle sin internasjonale kompetanse, språkferdigheter, personlige egenskaper og omstillingsevne.

Vurdering er et viktig element i profesjonskvalifiseringen og knyttes til arbeidskrav, eksamener og praksisstudier. Studentene skal kunne prøves i forhold til forventet læringsutbytte.

Gjennom studiet møter studentene ulike vurderingsformer. Studenten vil prøves i både skriftlige, muntlige, digitale og praktiske vurderingsformer. De ulike vurderingsformene skal bidra til progresjon i utdanning og ivareta studenter med ulik bakgrunn og ulike forutsetninger og behov. Nærmere beskrivelse av vurderingsformer framgår av den enkelte emneplan.

Se den enkelte emneplan for vurderings-/eksamensformer.

Rettigheter og plikter ved eksamen

Eksamenskandidatens rettigheter og plikter framgår av forskrift for studier og eksamen ved OsloMet. Forskriften beskriver blant annet vilkår for ny/utsatt eksamen, klageadgang og hva som regnes som fusk ved eksamen. Kandidaten har plikt til å gjøre seg kjent med bestemmelsene i forskriften.

Sensorordning

For å ivareta et eksternt blikk på all vurdering inngår enten ekstern sensur eller tilsynssensur. Ved tilsynssensur oppnevnes en tilsynssensor som skal føre tilsyn med vurderingsformer, gjennomførte vurderinger og vurderingsprosesser. Evalueringen dokumenteres i en rapport hvor eventuelle forslag til endringer i vurderingsform og vurderingsprosess framkommer.

Kvalitative vurderingskriterier

Det norske karaktersystemet er fastsatt i Lov om universiteter og høyskoler (§ 3-9), med skalaene A-F og bestått/ikke bestått.

Ved bruk av karakterskala bestått/ikke bestått, må følgende være oppfylt for å vurderes til bestått karakter, kandidaten har oppfylt kravene til læringsutbytte og viser nødvendige kunnskaper, ferdigheter og kompetanse. Vurderingskriteriene utdypes i sensorveiledningen til den enkelte eksamen.

Følgende nasjonale vurderingskriterier legges til grunn

A - fremragende: Fremragende prestasjon som klart utmerker seg. Kandidaten viser svært god vurderingsevne og stor grad av selvstendighet.

B - meget god: Meget god prestasjon. Kandidaten viser meget god vurderingsevne og selvstendighet

C - god: Jevnt god prestasjon som er tilfredsstillende på de fleste områder. Kandidaten viser god vurderingsevne og selvstendighet på de viktigste områdene.

D - nokså god: En akseptabel prestasjon med noen vesentlige mangler. Kandidaten viser en viss grad av vurderingsevne og selvstendighet

E - tilstrekkelig: Prestasjonen tilfredsstiller minimumskravene, men heller ikke mer. Kandidaten viser liten vurderingsevne og selvstendighet.

F - ikke bestått: Prestasjon som ikke tilfredsstiller de faglige minimumskravene. Kandidaten viser både manglende vurderingsevne og selvstendighet

Skikkethetsvurdering

Lærerutdanningsinstitusjoner har ansvar for å vurdere om studenter er skikket for barnehagelæreryrket. Løpende skikkethetsvurdering foregår gjennom hele studiet og inngår i en helhetsvurdering av studentens faglige, pedagogiske og personlige forutsetninger for å kunne fungere som barnehagelærer. En student som utgjør en mulig fare for barns liv, fysiske og psykiske helse, rettigheter og sikkerhet, er ikke skikket for yrket. Studenter som viser liten evne til å mestre barnehagelæreryrket, skal så tidlig som mulig i utdanningen få melding om dette. De skal få råd og veiledning for å gjøre dem i stand til å oppfylle kravene om lærerskikkethet eller få råd om å avslutte utdanningen. Skikkethetsvurderingen foregår gjennom hele studiet.

Nærmere informasjon omskikkethet

Programplanen for bachelor i barnehagelærerutdanning bygger på Forskrift om endring i forskrift om rammeplan for barnehagelærerutdanning endret av Kunnskapsdepartementet 30.06.2023, Nasjonale retningslinjer for barnehagelærerutdanning av 17.10.2018, Lov om barnehager fastsatt av Kunnskapsdepartementet 17.06.2005, og Nasjonalt kvalifikasjonsrammeverk for livslang læring fastsatt av Kunnskapsdepartementet 15.12.2011.

Quality assurance

The purpose of OsloMet's quality assurance system is to strengthen students' learning outcomes and development by raising the quality at all levels. Cooperation with the students, and their participation in the quality assurance work, is decisive to the overall learning outcome. Among the overall goals for the quality assurance system is to ensure:

• that the educational activities, including practical training and the learning and study environment, maintain a high level of quality
• that the study programmes are relevant for the professional fields
• that the quality development continues to improve

For the students, this entails, among other things, student evaluations in the form of:

• course evaluations
• annual student surveys for all of OsloMet

More information about the quality assurance system is available here: https://student.oslomet.no/regelverk#etablering-studium-evaluering-kvalitetssystem

Programme supervisor scheme

The programme supervisor scheme is part of the quality assurance of each individual study programme. A programme supervisor is not an examiner, but someone who supervises the quality of the study programmes. All study programmes at OsloMet shall be subject to supervision by a programme supervisor, but there are different ways of practising the scheme. Reference is made to the Guidelines for Appointment and Use of Examiners at OsloMet: https://student.oslomet.no/retningslinjer-sensorer

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