Mathematics for Grades 1-7, Course 1 Programme description

Programplanen bygger på forskrift om rammeplan for grunnskolelærerutdanningene for 1.–7. trinn og 5.–10. trinn, fastsatt av Kunnskapsdepartementet 1. mars 2010, og nasjonale retningslinjer for grunnskolelærerutdanningen 1.–7. trinn.

Studieprogrammet er organisert i to emneplaner. Elevperspektivet vil være framtredende i begge emner. For alle elever er det viktig at de får mulighet til å bygge opp matematisk kompetanse ut fra egne forutsetninger. Dette fordrer at lærerne har god kjennskap til hvordan elever vanligvis utvikler matematisk forståelse, samt at lærerne evner å avdekke og sette seg inn i de forskjellige elevenes kunnskaper. På grunnlag av god innsikt i elevenes faglige utgangspunkt skal matematikkundervisningen gi elevene mulighet til innlevelse og den skal fremme deres fantasi og nysgjerrighet, både individuelt og i fellesskap.

Målgruppen for emnet er lærere som underviser i grunnskolen og som ønsker å undervise i matematikk på 1.-7. trinn. Studiet er utviklet på oppdrag fra Kunnskapsdepartementet, med bakgrunn i Kompetanse for kvalitet - strategi for etter- og videreutdanning.

Opptakskrav er bestått lærerutdanning innrettet for undervisning i grunnskolen. Søkere med bestått førskole- eller barnehagelærerutdanning må ha tilleggsutdanning for å undervise i barneskolens 1. til 4. trinn (GLSM 60 studiepoeng eller GLSM 30 og 30 studiepoeng i matematikk eller norsk rettet mot barnetrinnet eller PAPS 1+2). Studenter som innvilges studieplass, må være i arbeid som lærer eller ha kontakt med en skole der det er mulig å ta aktivt del i matematikkundervisning. Kravet om bestått lærerutdanning kan fravikes dersom søkeren kun mangler faget hun/han søker på, for å få fullført sin lærerutdanning.

Søkere rangeres etter karakterpoeng fra lærerutdanningen. Søkere som har fått innvilget stipend og/eller vikarmidler fra Utdanningsdirektoratet (Udir), får 5 tilleggspoeng.

Læringsutbytte fremkommer i emneplanene.

Fagmetodiske problemstillinger inngår som en viktig del av studiet, som det å bruke og drøfte ulike læringsmiljøer, undervisningsmetoder, hjelpemidler, lærebøker. Studiet gjør studentene fortrolige med de grunnleggende ferdighetene å kunne uttrykke seg skriftlig og muntlig, å kunne lese og regne og å kunne bruke digitale verktøy i faget matematikk.

For mer utfyllende informasjon, se den enkelte emneplan.

Emnet er organisert i samlinger i høst- og vårsemesteret. Studentene forventes å delta aktivt i samlingene og å ta ansvar for egen læring.

For mer utfyllende informasjon, se den enkelte emneplan.

The Higher Education Entrance Qualification/prior learning and work experience, Mathematics (R1+R2) and Physics 1. An introductory course or qualifications from a technical college under previous regimes are sufficient to meet the qualification requirements. Applicants with qualifications from a technical college pursuant to the Act relating to Tertiary Vocational Education (2003) only need to take Mathematics R1+R2 and Physics 1.

Reference is made to the Regulations concerning Admission to Higher Education: https://lovdata.no/dokument/LTI/forskrift/2007-01-31-173

After completing and passing the three-year bachelor’s degree programme in Mathematical Modeling and Data Science, the candidate is expected to have achieved the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The candidate:

• has broad knowledge that provides a holistic system perspective on engineering in general, with a specialization in mathematical modeling and data science. Key knowledge includes mathematical problem-solving, understanding of physical principles, and the development and use of scientific software.
• has fundamental knowledge in mathematics, natural sciences, relevant social sciences and economics, and how these can be applied in engineering problem-solving.
• has knowledge of the history of technology, technological development, the engineer's role in society, relevant legislation related to the use of mathematical modeling and data science,forklare and the various consequences of using the technology.
• is familiar with research and development work in mathematical modeling and data science, as well as relevant methods and practices within engineering.
• can update their knowledge in the field through information retrieval and contact with professional environments and practices.

Skills

The candidate:

• can apply knowledge and relevant results from research and development work to solve theoretical, technical, and practical problems in mathematical modeling and data science, and justify their choices.
• has knowledge of software and programming languages relevant to mathematical modeling and data science and has broad engineering digital competence.
• can use relevant programming languages to solve scientific problems.
• can work in digital laboratories and master methods and tools as a basis for reproducible, targeted, and innovative work.
• can identify, plan, and carry out engineering projects, tasks, experiments, and tests both independently and in teams.
• can find, evaluate, use, and reference information and academic material and present it in a way that elucidates a problem.
• can contribute to innovation and entrepreneurship through participation in the development and realization of sustainable and socially beneficial products, systems, and solutions.

General competence

The candidate:

• has insight into the environmental, health-related, social, and economic consequences of using mathematical modeling and data science.
• can place the results of mathematical modeling and data science in an ethical and life-cycle perspective.
• can identify safety, vulnerability, privacy, and data security aspects in products and systems that use ICT.
• can communicate engineering knowledge to different target groups both in writing and orally and can help highlight the significance and consequences of technology.
• can reflect on their own professional practice, also in teams and in an interdisciplinary context, and can adapt it to the current work situation.
• can contribute to the development of good practice by participating in professional discussions within mathematical modeling and data science and share their knowledge and experiences with others.
• has information competence; understands why quality-assured knowledge sources should be sought, why sources should be referenced, and is aware of what is defined as plagiarism and cheating in student work.
• can update their knowledge through literature studies, information searches, contact with professional environments and user groups, and through experience.

The program is a three-year engineering education and awards the degree Bachelor in Mathematical Modeling and Data Science. Each academic year comprises 60 credits, meaning the bachelor's program has a total of 180 credits. Each course has a final exam.

In the first semester, the curriculum and the language of instruction will primarily be in Norwegian, but English literature will be increasingly used throughout the program. The fifth semester is offered in English to facilitate increased student exchange. Although the bachelor's program is mainly taught in Norwegian, there is an expectation that students have sufficiently good English skills as much relevant literature and resources are in English.

The content of the teaching in the common part of the education can be summarized as follows:

First year common courses: Foundation of natural science

• Engineering foundation
• Calculus and discrete mathematics
• Programming
• Physics and chemistry

Second year common courses: Breadth

• Linear algebra
• Multivariable calculus and differential equations
• Statistics
• Numerical mathematics

Third year: Specialization

• Quantum mechanics
• Bachelor thesis

In additional there will be elective courses in theoretical mathematics, artificial intelligence and data science, scientific computing and computer science.

The program is structured into the following course groups according to the framework plan:

• Engineering foundation: 30 credits with fundamental mathematics, engineering system thinking, and introduction to engineering practice and methods. This should mainly relate to engineering education and lay the foundation for the engineering profession.
• Program foundation: 50-70 credits with technical subjects, natural sciences, and social sciences. This should mainly relate to the study program and lay the foundation for the field of study.
• Technical specialization: 50-70 credits providing a clear direction within the respective field, building on the engineering foundation and program foundation. This should mainly relate to the study direction and lay the foundation for the field. The bachelor thesis is included in the technical specialization.

Elective courses: 20-30 credits contributing to further academic specialization, either in breadth or depth. The elective courses in the third year can provide a focus on theoretical mathematics, artificial intelligence and data science, or scientific computing. If there are too few students choosing a given elective course in a semester, the course will not be offered that semester.

Elective courses available in the 5th semester:

• MAMO3200 Simulation and Visualization
• MAMO3300 Real Analysis
• DATS2300 Algorithms and Data Structures
• DATA3800 Introduction to Data Science with Scripting
• DAVE3625 Introduction to Artificial Intelligence

Elective courses available in the 6th semester:

• DAVE3606 Resource-efficient programs
• ADSE3200 Visualization
• MAMO2500 Symmetries and Algebraic Structures
• MAMO2400 Thermodynamics and Statistical Physics