The course is connected to the following study programs

  • Subject didactics in Teacher Education Level 1-7, 2-year Master’s Programme
  • Master's 5-Year Programme in Teacher Education, level 1-7

Teaching language

Norwegian

Prerequisites

Completed 60 ECTS credits in mathematics

Course contents

The course is based on the students' basic knowledge of applied statistics and probability. The emphasis is on the use of statistical methods to authentic problems in society, such as health, environment, migration, education, etc. The course uses a project-based learning approach, in which students gain experience in the use and interpretation of data visualization and data analysis, which involves simple use of statistical software. The course deals with society's increasing datafication, including big data, missing data, uncertainty of predictions, improvement or difference, correlation, model building, data collection and interpretation of statistics. It includes studies of research articles including statistical surveys, among which those related to children's learning. An introduction to the theory behind project-based and inquiry-based learning is given and the students will design a project-based lesson on statistics in primary schools.

The subject in practice
During the 5 days in teaching practice, it will be arranged for the student to gain experience with the use of various teaching resources in mathematics and student-active teaching methods

Learning outcomes

On successful completion of the course, the student should:

  • have insight into the use of statistics in society, in particular collection of big data

  • have applied knowledge of concepts in descriptive statistics and probability calculations, such as estimation, predictions, significance, expectation values, accidentality, uncertainty, sample and correlation

  • have knowledge of the background of statistical methods and techniques for data collection and data analysis

  • be able to evaluate the relevance of methods and interpret results and show a reflected attitude towards the use of statistics in society

  • be able to interpret regression models to look at the relationship between data sets, and has knowledge about the difference of correlation and causality

  • be able to use statistical software for data visualization and data analysis

  • be able to understand, summarize, popularize and criticize statistical studies, in particular studies on mathematics education with relevance to Primary School

  • have insight into theories and research on project-based and inquiry-based learning

  • have introductory knowledge of the design of inquiry- and project-based learning activities with societal relevance

Examination requirements

The teacher education students

  • approved attendance in the course

  • approved required assignments. (see Canvas for more information)

  • approved practice

Other students

  • approved attendance in the course

  • approved portfolio collection of mathematical tasks.(see Canvas for more information)

  • approved didactic work of similar scope as practice

Teaching methods

Joint teaching, work in small groups, compulsory assignments, oral presentations. Statistical software is used where appropriate.

Requirements for minimum 70% compulsory participation.

The course has an expected scope of work of 267 hours.

It is included 5 days practice for the teacher education students. This is a preparation for the teaching practice in the 8th semester.

Other students will perform a didactic work of similar scope as practice.

Evaluation

The person responsible for the course, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm- or end of term evaluation, see also the Quality System, section 4.1. Information about evaluation method for the course will be posted on Canvas.

Assessment methods and criteria

Individually portfoli assessment of presentations and project work.

Information about the content of the portfolio will be given in Canvas by the start of the semester. There will not be arranged a postponed exam for the portfolio.

Reduction of Credits

This course’s contents overlap with the following courses. A reduction of credits will occur if one of these courses is taken in addition:

Course Reduction of Credits
MA-217 – Statistical Machine Learning 7.5
MA-425 – Statistics for Primary School 7.5
MA-166 – Statistics 2.5
Last updated from FS (Common Student System) June 30, 2024 11:37:49 PM