Bachelor's Programme in Development Studies Programme description

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

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

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

In summary, the core structure for all students is:

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

or

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

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

Sem 1: Common course - Specialization course - Specialization course

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

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

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

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

Sem 1: Common course - Specialization course - Specialization course

Sem 2: Common course - Specialization course - Elective course

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

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

Common course content

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

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

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

Specialization track content

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

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

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

Universal Design of ICT

Specialization courses (SPEC)

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

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

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

The specialization track objectives for Universal Design of ICT are:

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

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

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

This specialization can be supplemented with either more core infrastructure courses, such as Globalization of Technology, Universal Design Fieldwork, Programming and APIs for Interaction, and Intelligent User Interfaces, or with any of the many other specialization courses from other tracks such as Agile Service Management and Developer Operations or Problem solving with scripting.

Recommended, but not required, prior knowledge:

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

Cloud-based Services and Operations

Specialization courses (SPEC)

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

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

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

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

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

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

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

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

Recommended, but not required, prior knowledge

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

Data Science

Specialization courses (SPEC)

• ACIT4510 - Statistical Learning (1st Semester)
• ACIT4420 - Problem-Solving with scripting (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 "ACIT4420 - Problem-Solving with scripting", will let the students learn to incorporate analysis methods into tools to automate the analysis process as well as streamline different variations of the same analysis.

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

Recommended, but not required, prior knowledge

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

Mathematical Modelling and Scientific Computing

Specialization courses (SPEC)

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

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

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

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

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

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

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

Recommended, but not required, prior knowledge

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

Applied Artificial Intelligence

Specialization courses (SPEC)

• ACIT4420 - Problem solving with scripting (1st Semester)
• ACIT4610 - Evolutionary artificial intelligence and robotics (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.

In this specialization, the first course in "ACIT4420 - Problem solving with scripting" will focus on automating processes and systems, with particular emphasis on tools that are required for autonomous and intelligent systems development. In addition, the course in "ACIT4610 - Evolutionary artificial intelligence and robotics" will provide the basis for modelling and analyzing complex systems, programming and controlling them with biologically inspired artificial intelligence, swarm intelligence, and evolutionary robotics. This course will provide insight into creating autonomous machines and systems that can adapt, evolve, and learn over time.

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

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

Recommended, but not required, prior knowledge

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

Robotics and Control

Specialization courses (SPEC)

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

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

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

The specialization track objectives for Robotics and Control are:

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

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

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

The course "ACIT4060 - Programming Electric Circuits" provides hands-on experience with microcontrollers and the design and analysis of linear electric circuits and instrument amplifier circuits. The course "ACIT4020 - Aerial Robotics" provides a hands-on overview of common theories and methods used in the design of autonomous and remotely piloted aerial robotic systems.

There is a range of relevant specialization courses from other tracks that can be suitable for Robotics and Control students. For instance, "ACIT4310 - Applied and Computational Mathematics", "ACIT4420 - Problem solving with scripting", "ACIT4630 - Advanced Machine Learning and Deep Learning", "ACIT4040 - Applied Artificial Intelligence Project", "ACITXXXX - 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 "MAUU5020 - Intelligent User Interfaces".

Recommended, but not required, prior knowledge

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

Biomedical Engineering

Specialization courses (SPEC)

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

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

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

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

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

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

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

The elective course "ACIT4060 - Programming Electric Circuits", provides hands-on experience with microcontrollers and the design and analysis of linear electric circuits and instrument amplifier circuits. Another suggested elective course for Biomedical Engineering programme is anatomy and physiology from the faculty of health sciences at OsloMet (digital platform). In addition, the following elective courses are suggested: "ACITXXXX - Internet of Thing", "ACIT4030 - Machine Learning for images and 3D data", "MAUU4020 - Intelligent User Interfaces" 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 ACIT4321 - Quantum Information Technology. Students who wish to improve their programming skills could benefit from taking the course "ACIT4420 - Problem-solving with scripting" and "Scripting for automation". In addition, subjects "Biomechanics", and "Cellular physiology" from faculty of health sciences could be complementary in this specialization.

Recommended, but not required, prior knowledge:

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

Short and long thesis

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

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

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

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

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

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

Elective courses

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

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

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

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

The learning outcomes will be achieved by means of different learning methods adapted to each individual course. Teaching methods include lectures, assignments, seminars, project work and the use of online learning resources. Project work is carried out both in groups and individually. In some courses, lab environments are available for students to practice their skills. The labs will mimic the real world as closely as possible in order to allow for a realistic setting for the students.

The program is based on student-centred learning methods, with student-run seminars and project work that require active participation from students. Students will receive feedback on their presentations and reports.

For seminar-based courses, it is essential for students to be prepared to follow the presentations and actively participate in projects and discussions.

Research and teaching are well integrated throughout the study program. The teaching is constantly updated to encompass new knowledge, and research articles are part of the syllabus in many of the courses. Furthermore, the students will participate in research-related discussions and have the opportunity to be included in ongoing research and development through projects that are part of the study program.

The master's thesis is a piece of independent research or development work in one of the key topics of the study program. Each student will be assigned an internal supervisor, who will ensure that the project complies with research ethics principles and help students to formulate the research question and ensure quality in the collection and analysis of data. Seminars with supervisors and fellow students will develop each student's ability to critically assess and discuss their own and other students' work. The thesis shall be a written report based on research principles and methods.

Students affiliated with the program have two opportunities to apply for doing parts of the program abroad.

In the 3rd semester. For a short thesis, the student will have to find courses that will function as substitutes for both the ASPEC and elective courses. For students choosing the long thesis, a substitute for an ASPEC course needs to be found at the desired university. The program committee will review and approve the substitution of courses.

In the 3rd or 4th semester, students may apply to carry out parts of their master projects and proceed to write their Master's Thesis abroad. This must be based on a "professor-to- professor" arrangement, with an agreement between the student's appointed supervisor and a professor at a higher educational institution abroad, who collaborate with one of the local research groups at OsloMet. The agreement is required to ensure that the master project is of mutual interest for all parties, and for establishing necessary supervision abroad. The student's supervisor will be able to continue giving remote supervision from Norway via email and video/conference-calling.

Requirements:

An average grade C in previously completed courses.

An exchange agreement with the desired university or college must be in place before the student can apply.

Acceptance from a receiving professor or institution to an exchange-stay with the necessary academic relevance. Currently we have no agreements with receiving institutions or professors, but are working towards establishing this.

Applications for going abroad must be sent by e-mail to studie-tkd@oslomet.no

Deadlines: One month prior to alternative 1) and one semester prior to alternative 2). Students submit the Master's Thesis for assessment to, and get their ECTS from, OsloMet.

A coursework requirement is a compulsory piece of work/activity that must be approved before the student may take an examination. Coursework requirements are assessed "approved" or "not approved".

Not approved coursework requirements

Legitimate absence based on, for example, a medical certificate, does not exempt students from meeting the coursework requirements. Students who due to illness or any other documented legitimate absence, who do not meet the coursework requirements within the appointed deadline, should as far as possible, be given a new attempt to meet the requirements, before the relevant examination. An agreement with the relevant lecturer must be made in each individual case.

If, due to the nature of the subject/course, it is not possible to implement a new attempt to meet the requirements, before the course exam the student must expect to submit the coursework requirements at the next possible submission deadline. This may lead to a delay in progression through the Master's programme.

If a work requirement is assessed «not approved», students have two chances to retake the work requirement. A work requirement that three times is assessed «not approved», will result in loss of the right to take the course exam.

Provisions governing examinations are laid down in the Act relating to Universities and University Colleges and the Regulations relating to Studies and Examinations at OsloMet . Please refer to student.oslomet.no.

Assessment methods vary between courses. They include written reports, oral presentations, portfolio assessments and oral examinations.

A portfolio assessment provides an overall assessment awarding one grade for the whole portfolio. Students may only appeal against the determination of the examination grade awarded for the whole portfolio. Any information on weighting of grades must be considered as supplementary information in connection with the final grade. If the portfolio consists of elements such as an oral presentation, practical work etc., the examination result cannot be appealed against. The right to appeal is stated in the individual course descriptions.

If the examination in a course consists of more than one part, all parts of the examination must be passed in order to pass the course.

Normally each course has two internal examiners to assess students' work. Each semester one course will be chosen to include an external examiner, thus the students' work will be assessed by one external and one internal examiner. The Master's thesis is assessed by the two external examiners.

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

Det er lagt til rette for at studenter som ønsker det skal kunne ta ett eller to semester av bachelorstudiet som utvekslingsstudent ved en utenlandsk utdanningsinstitusjon. Normalt vil dette skje i fjerde eller femte semester av bachelorstudiet. OsloMet har inngått samarbeidsavtaler med universiteter i flere land i ulike verdensdeler. Dette gir studentene et bredere tilbud av utviklingsrelaterte emner de kan velge mellom, samtidig som det å tilbringe et semester ved en utdanningsinstitusjon i et annet land er en svært nyttig erfaring.

Dette er beskrevet i hver enkelt emneplan.

Vurderingsordningene varierer i løpet av studiet med type arbeider, progresjon i krav og forventninger og bruk av interne og eksterne sensorer. Studentene vil i løpet av studiet blant annet være innom følgende vurderingsformer: hjemmeeksamen, feltarbeidsoppgave, muntlig eksamen, skriftlig oppgave og skriftlig eksamen under tilsyn.

Oversikt over vurderingsordninger:

1. semester:

UTVB1100 Introduksjon til utviklingsstudier, 20 stp., hjemmeeksamen, A-F

UTVB1200 Regionalkunnskap og global historie, 10 stp., muntlig eksamen, A-F

2. semester:

UTVB1300 Innføring i metoder og vitenskapsteori, 10 stp., skriftlig eksamen under tilsyn, A-F

UTVB1400 Feltarbeid i utviklingsstudier, 20 stp., feltoppgave, A-F

3. semester:

UTVB2000 Makt og politikk, 30 stp., hjemmeeksamen og muntlig eksamen, A-F

4. semester:

UTVB2100 Development and Migration, 10 stp., skriftlig eksamen under tilsyn, A-F

UTVB2200 Education and Religion in Development, 10 stp., hjemmeeksamen, A-F

UTVB2400 Media and Development, 10 stp., skriftlig eksamen under tilsyn, A-F

5. semester:

UTVB3002 Introduction to the Development Enterprise, 10 stp., hjemmeeksamen, A-F

UTVB2300 Economy and Sustainability, 10 stp., hjemmeeksamen, A-F

UTVB3130 Recent Trends in Development Assistance, 10 stp., skriftlig eksamen under tilsyn, A-F

6. semester:

Enten: UTVB3300 Teorier om utvikling, globalisering og sosial endring, 30 stp., hjemmeeksamen og bachelor-/semesteroppgave, A-F

Eller: UTVB3901 Hospitering i utviklingsindustrien, 30 stp., hjemmeeksamen og bacheloroppgave, A-F

Vurderingskriterier ved bruk av gradert karakter:

A, fremragende: Fremragende presentasjon som klart utmerker seg. Kandidaten viser usedvanlig god reflekterende og analytisk evne og uvanlig stor grad av faglig modenhet og selvstendighet.

B, meget god: Meget god presentasjon. Kandidaten viser god reflekterende og analytisk evne og stor grad av faglig modenhet og selvstendighet. Mindre mangler kan aksepteres.

C, god: Jevnt god presentasjon som er tilfredsstillende på de fleste områder. Kandidaten viser en viss reflekterende og/eller analytisk evne og en viss grad av faglig modenhet og selvstendighet. En del mangler kan aksepteres.

D, nokså god: En akseptabel prestasjon med noen vesentlige mangler. Kandidaten viser en viss svakhet når det gjelder reflekterende og/eller analytisk evne og har relativt lav grad av faglig modenhet og selvstendighet.

E, tilstrekkelig: Prestasjonen tilfredsstiller minimumskravene, men heller ikke mer. Kandidaten viser klare svakheter når det gjelder reflekterende og/eller analytisk evne og har lav grad av faglig modenhet og selvstendighet.

F, ikke bestått: Prestasjonen tilfredsstiller ikke de faglige minimumskravene. Kandidaten viser både manglende faglig modenhet og selvstendighet.

Godkjent av studieutvalget ved Høgskolen i Oslo 21. juni 2004

Revisjon godkjent av studieutvalget 13. januar 2015, 23. mai 2016 og 1. juni 2016

Redaksjonelle endringer foretatt 3. juni 2016 og 24. oktober 2016

Revisjon godkjent av studieutvalget 11. mai 2017

Redaksjonelle endringer foretatt 19. juni og 20. desember 2017

Revisjon godkjent på fullmakt av leder i studieutvalget 27. november 2017

Pensum oppdatert desember 2017

Revisjon godkjent av utdanningsutvalget 24. mai 2018

Redaksjonell endring foretatt 12. juli 2018

Revisjon godkjent på fullmakt av leder i utdanningsutvalget 22. mars 2019

Gjeldende fra høstsemesteret 2019