After taking this course students are able to understand the major principals and innovations in educational measurement.
Students:
Part 1
- develop knowledge and understanding of fundamental concepts needed for the analysis of (educational) measurement data. Students will learn state of the art techniques how to analyze different types of datasets, Bayesian psychometrics and the integration of data science and psychometrics.
- will learn how to apply their knowledge to problems in the field of (educational) measurement.
- will be able to advise and consult researchers in applying the state of the art techniques in the field of educational measurement.
- are able to report the results of their analysis for a scientific journal publication. Students are able to communicate and translate the results from their analysis to different societal stakeholders.
- have gained experience in analyzing (complex) educational data and writing reports for both specialist and non-specialist audiences.
Part 2
- develop knowledge and understanding of Bayesian psychometric methods (theory and applications in Bayesian latent variable modeling).
- develop knowledge and understanding of computational psychometrics (Markov chain Monte Carlo methods, parallel computing, R-software)
- will learn how to apply advanced Bayesian methods for large-scale assessments. (measurement invariance testing, longitudinal psychometric research, missing data, plausible value technology)
- will learn how to apply multivariate models for high-dimensional data (process data, multidimensionality, network models)
Part 3
- develop knowledge and understanding of Data pre-processing for various types of raw data
- develop knowledge of popular methods in statistical learning including (high-dimensional) linear regression, classification, resampling, (deep) neural nets, support vector machines, and random forests.
- learn to apply most popular text mining algorithms (including sentiment analysis and topic modeling)
- be able to analyze datasets with the learned tools.
- be able to interpret the outcome of the methods and to draw valid conclusions from it
- are able to Integrate statistical learning methods and psychometrics
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The course Educational Measurement (15 ECTS) takes place in the first semester of the year. We offer a course on contemporary psychometrics, i.e. statistics and data science are as they are applied to data from psychological or educational tests, surveys, and various kinds of new data sources relevant for measuring human behavior and ability. 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 major interest and innovations. The course consists of three parts. The first part is about handling and analyzing contemporary datasets. In the second part of this elective, you will be introduced into Bayesian psychometrics. The third part is about the entanglement of psychometrics and data science.
Assignment
For each part, students have to do an assignment, that has to be completed at a sufficient level (minimum grade 5.5).
The final score for this course is equal to the average score on the three assignments.
Final assignment (minimum grade 5,5)
Registration
Students of the research master Methodology and Statistics for the Behavioural, Biomedical and Social Sciences are enrolled at the University of Utrecht (UU). However, in order to be able to participate in this course offered at the University of Twente (UT), students must register at the UT as a ‘bijvakker’. Please make sure to arrange this well in time (June/July) before the start of the course!
Use the application form ‘Bijvakstudent UT’ on: https://www.utwente.nl/onderwijs/student-services/procedures-services/aanmelding-inschrijving/bijvakstudent-of-kies-op-maat/
Under enrolment details at the UT, please fill out:
Opleiding / educational program: Educational Science and Technology (EST)
Faculteit / faculty: BMS
After the bijvakker registration is completed, students receive a personal UT account (incl. student number, e-mail), that they must use to register for this specific course 202100002 - Educational measurement (15 EC) in Osiris,
In case of questions about the UT registration you may contact the EST study adviser y.c.h.dethouars@utwente.nl or EST programme coordinator m.e.tijhuis@utwente.nl
After completing the course, research master students can obtain a formal transcript of records via StudentServices@utwente.nl and submit this at the University of Utrecht for registration of the EC’s + grade in their MSBBSS master’s programme
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