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:

Knowledge

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

Skills

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

Competencies

  • 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

Admission

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.

Prerequisites

Formal prerequisite knowledge

Obligatory requirement of having completed MAE4000 Data Science,