Modern Developments in Educational Measurement and Psychometrics: From Theory to Practice
A special issue of Education Sciences (ISSN 2227-7102).
Deadline for manuscript submissions: 31 March 2027 | Viewed by 119
Editors
Interests: invariant measurement; Rasch measurement theory; rater-mediated assessments; AI-mediated assessments
Special Issue Information
Dear Colleagues,
Educational measurement and psychometrics provide the theoretical and methodological foundations for developing, evaluating, and improving assessments across educational contexts. Over the past decade, the field has grown substantially as new measurement models, computational tools, and technology-enhanced testing environments have expanded both the scope of what can be measured and the precision with which measurement occurs. Modern assessments now generate rich data, such as response scores and response times, which allow researchers to investigate how examinees meaningfully engage with test content. At the same time, the increasing use of artificial intelligence in item development, automated scoring, and test assembly has created new opportunities alongside new challenges for validity and fairness.
This Special Issue aims to bring together recent theoretical and applied contributions in educational measurement and psychometrics. We welcome original research articles and reviews that advance measurement theory, including developments in item response theory, Rasch measurement theory, and invariant measurement. Contributions on rater effects in performance assessment, process data modeling, the use of artificial intelligence in assessment design and scoring, computerized adaptive testing, and fairness in testing are also welcomed. We are especially interested in work that connects measurement theory to practical assessment problems.
Topics of interest for publication include, but are not limited to, the following:
- Advances in item response theory and Rasch measurement theory;
- Invariant measurement and its applications;
- Rater effects and rater-mediated performance assessment;
- AI-mediated assessment;
- Process data and response time modeling;
- Artificial intelligence in assessment design and scoring;
- Computerized adaptive testing and multistage testing;
- Fairness, differential item functioning, and measurement equity;
- Cognitive diagnostic models and diagnostic classification;
- Standard setting, scale development, and score reporting;
- Validity theory and validation studies.
Prof. Dr. George Engelhard
Dr. Jiawei Xiong
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- educational measurement
- psychometrics
- Rasch measurement
- invariant measurement
- process data
- artificial intelligence
- computerized adaptive testing
- rater effects
- fairness
- validity
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