MAE4051 – Selected Topics in Educational Measurement

Schedule, syllabus and examination date

Course content

By focusing on topics connected to more specialized techniques and procedures in educational measurement, this course will offer an opportunity to deepen the knowledge acquired in the first year of the Master in Assessment, Measurement and Evaluation.

The exact list of topics to choose from will vary from year to year. It will typically include topics such as for example large-scale assessment, equating, standard setting, computerized adaptive testing (CAT), Monte Carlo simulations, Generalized Linear Latent and Mixed Models (GLLAMM).

There will be one workshop per topic, so students must choose two workshops to attend. The students should send an e-mail to the administration at the start of the semester specifying which workshops they will attend.

Please see the semester page for specific information about which topics are offered in a given term.

Learning outcome

This course is based on selecting two topics from a larger list of specific applications of theory from the field of educational measurement and assessment. Upon completion of the course you will:


  •  Have gained deep and detailed knowledge about two selected specific contexts where principles from educational measurement are applied


  • Master and understand the principles behind a few specific applications of educational measurement regularly occurring in assessment contexts


  • Be able to make reasonable judgements and carry through all necessary steps in designing and analyzing data with regards to two specific topics/procedures within the field of applied educational measurement


Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

Some of the workshops may be offered as Ph.d. courses. Please check the UV Ph.d. course catalogue to see what is available.


Formal prerequisite knowledge

Obligatory requirement of having completed MAE4000 Data Science, MAE4011 Principles of Measurement, MAE4120 Item Response Theory or equivalent. Students from other programs or departments are welcome to apply if there is room in the course and they have the required background knowledge. Contact to find out if it is possible for you to attend.


Condensed seminar workshops throughout the semester.

Midway each topic, students are required to hand in an outline of each written assignment. Students will be given feedback on their outlines. These outlines have to be approved in order for the student to be allowed to hand in the final versions of the papers.


A written paper for each of the two chosen topics, either in the form of an essay or a report presenting analyses of given datasets.

Previous exams

Submit assignments in Inspera

You submit your assignment in the digital examination system Inspera. Read about how to submit your assignment.

Use of sources and citation

You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Explanations and appeals

Resit an examination

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.


In accordance with the UiO quality assurance system, the course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Autumn 2020 - Analysis of Large-Scale Assessment Data

Facts about this course

Covers two semesters, starting every autumn
Teaching language