One Year Programme in Records Management Programme description

Årsstudiet gir innsikt i sammenhenger mellom teknologi, mennesker og dokumentasjon. Studiet kvalifiserer til arbeid i alle virksomheter som skal håndtere informasjonsflyt og dokumentere aktiviteter, både i privat og offentlig sektor.

I første studieår lærer du om arkivvitenskapelige metoder og rettslige rammer for dokumentasjonsforvaltning/arkivdanning, dvs. for sikring, forvaltning og tilgjengeliggjøring av dokumentasjon som moderne organisasjoner trenger i sin virksomhet.

I andre studieår lærer du om prinsipper for digital dokumentasjonsforvaltning, utfordringer med å gjøre digitale dokumenter anvendelige og pålitelige over tid, og arkiv som samfunnsressurs og kulturarv gjennom danning og tilgjengeliggjøring.

In the era of misinformation and data breaches, investigative journalism plays a pivotal role in holding institutions accountable and informing the public. This course, "Digital Investigative Journalism," is designed for aspiring journalists keen on mastering modern investigative techniques, augmented by digital tools and data analysis.

Language of instruction is English.

This course is within the same specialization as the elective course Unraveling the numbers - financial reporting.

None

Knowledge

• Acquire an advanced understanding of digital research methods and data analytics.
• Approach the use of AI/ML tools for complex investigative scenarios (computer vision, Natural Language Processing, regression, etc).
• Understand the evolving ethical concerns and legal frameworks in digital investigative journalism (privacy concerns, bias in the models, etc).

Skills

• Conduct comprehensive investigations using advanced AI models and data analytics software.
• Use specialized programming languages, such as Python or SQL, to handle complex datasets.
• Produce investigative reports on complex datasets.

General competence

• Present complex investigative findings in an accessible format for the general public.
• Have an understanding of AI models’ limitations, constrains and data requirements.

• Lectures
• Workshops
• Group work

The teaching takes place in person, on campus.

Presence will be mandatory (80 percent) given the class's strong practical and collaborative nature. Students who have more than 20 percent absence from class will not be qualified to take the exam.

Assignment 1: Group project. Students are required to analyze a complex dataset that they have gathered. They must develop a hypothesis driven by data, collect and produce an appropriate dataset, and conduct a thorough analysis of it. The project must be carried out in groups of 2-4 students.

In the practical tasks, the scope will vary depending on which types of media or combinations of these the student chooses. The extent will be specified in the assignment texts or individually in collaboration with the subject teacher. More information about the content and deadlines for the assignments can be found in the teaching plan, which is electronically available to the students at the start of the course.

All required coursework must be completed and approved by the given deadline for the student to take the exam. If the coursework requirements have not been approved, the student will be given one opportunity to submit an improved version by a given deadline.

The exam consists of an individual analytical report, 6 - 8 pages where the students engage in a critical discussion and reflection on their findings in the group project. The project must be handed in as an attachment to the exam.

The exam can be written in Norwegian or English.  

Font type and size: Arial / Calibri / Verdana 12 pt. Line spacing: 1.5 

All aids are permitted, as long as the student complies with the rules for source referencing.