Automatic Item Generation: Theory and Practice
Mark J. Thomas M. He is the author of numerous books in the field of educational assessment and test item development, including the much praised Handbook of Test Development. Automatic Item Generation : Theory and Practice. Gierl , Thomas M. The example is based on data from a small, convenience sample. Hence, extensive model-data fit analyses were not conducted. However, using Embretson , p.
This outcome provides some evidence of the adequacy of the model-data fit of the LLTM-R in our illustrative example. Testing on the internet: Issues, challenges, and opportunities in the field of occupational assessment. Hambleton Eds. Hoboken, NJ: Wiley. Bejar, I. A generative analysis of a three-dimensional spatial task.
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Creating Content for Educational Testing Using a Workflow That Supports Automatic Item Generation
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Mark J. Gierl (Author of Automatic Item Generation)
Engineering the test: From principled item design to automated test assembly. Assessment engineering: An emerging discipline. Assessment engineering in language testing: From data models and templates to psychometrics. Assessment design and development, version 2. Evidence-centered assessment design. Haladyna Eds. Test development. Singley, M. Item generation and beyond: Applications of schema theory to mathematics assessment.
Calibrating item families and summariz-ing the results using family expected response functions. Journal of Educational and Behavioral Statistics, 28, Sireci, S. Innovative item formats in computer-based testing: In pursuit of improved construct representation. Elements of adaptive testing. Zenisky, A. Technological innovations in large-scale assessment. Applied a. If the pattern continues, then the next three numbers should be A. The number that Is 1 group of 5 fewer than Is. What fraction of the measuring cup has oil in it?
Water A. Diagram: Ii of water and 12 of oil in one cup. The cognitive feature codes were used to develop the four parent items for our example. The item feature codes serve as variables that could affect the difficulty level for the generated items. Subsection 2. Using automatic item generation to create items for medical licensure exams. Becker Chair , Beyond essay scoring: Test development through natural language processing.
In its most ambitious form, AIG can be described as the process of using models to generate statistically calibrated items with the aid of computer technology.