After taking this course students are able to understand the major principals and innovations in educational measurement. Students:
- Will understand the use of data forensics in educational measurement.
- Will understand the use and design of computerized adaptive test in educational measurement, and can calculate and interpret the scores
- Gain insight into the principles of systematic design of assessment of and for learning, which in turn aid valid decisions about the learner.
- Are able to review an existing test design by applying the principles taken from the ECD sub models as evaluative criteria.
- Will gain insight in large-scale assessments methodology and analysis.
- Are familiar with the most important methods of standard setting, both classical methods for multiple-choice tests (e.g., Angoff and Nedelsky methods) and methods for complex performance-based assessments, such as, the extended Angoff method as well as its variations.
- Will understand review systems for educational assessments and will be able to systematically evaluate the quality of educational assessments.
Relationship with EST labour market (Intended Learning Outcomes [ILO's] as described in the EST programme specific appendix of the EER):
- This course contributes (strongly) to: Domain expertise
- To a limited extent, this course contributes to: Research competence, Academic reflection, Advice competency, Design competency
This courses assesses the following ILO's in some way (can include formal/informal, formative/summative, peer/expert): Domain expertise, Research competence.
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The course gives an overview of the state-of-the-art in educational measurement methodology. Students will get to know the professional field of educational measurement including the contemporary major interest and innovations. The course will be taught by leading experts in the area of Educational Measurement. Specialists from companies like the educational measurement institute Cito, RCEC, Explain and from universities will each address one of six guest lectures. For each of these topics, students are expected to read a number of scientific papers, will make an assignment about each topic for which the answer is posted on a personal blog. Peers will provide feedback on the completed assignments.
Assessment:
Students have to do assignments which has to be completed at a sufficient level (minimum grade 5,5).
The final score for this course depends on the means score of the assignments that are given during the course and the peer feedback activities.
Relationship with technology:
- Students work with specific programs to analyse assessment tasks and data and create assessment related products
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