Automatic Item Generation: Theory and Practice

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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.

References

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.

Applied Psychological Measurement, 14, Generative testing: From conception to implementation. Kyllonen Eds. Hillsdale, NJ: Erlbaum. A feasibility study of on-the-fly item generation in adaptive testing. Journal of Technology, Learning, and Assessment, 2 3. How the internet will help large-scale assessment reinvent itself. Educational Policy Analysis Archives, 9, Case, S. Constructing written test questions for the basic and clinical sciences 3rd ed.

Downing, S. Handbook of test development. Mahwah, NJ: Erlbaum. Drasgow, F.

References

Technology and testing. Brennan Ed. New tests and new items: Opportunities and issues.

Creating Content for Educational Testing Using a Workflow That Supports Automatic Item Generation

Embretson, S. Generating items during testing: Psychometric issues and models. Psychome-trika, 64, Generating abstract reasoning items with cognitive theory. Cognitive design systems: A structural equation modeling approach applied to developing a spatial reasoning test. Embretson Ed.

Understanding and quantifying cognitive complexity level in mathematical problem solving items. Psychological Science Quarterly, 50, Automatic item generation and cognitive psychology. Sinharay Eds. Amsterdam, The Netherlands: Elsevier. Fischer, G. The linear logistic test model as an instrument in educational research. Acta Psychologica, 37, Geerlings, H. Modeling rule-based item generation. Psychometrika, 76, Gierl, M. Using judgments from content specialists to develop cognitive models for diagnostic assessments. Using the Attribute Hierarchy Method to make diagnostic inferences about examinees' skills in mathematics: An operational implementation of a.

International Journal of Testing, 10, Automatic item generation: Theory and practice. New 0 York, NY: Routledge. Using the Attribute Hierarchy Method to make diagnostic inferences about examinees' cognitive skills in algebra on the SAT. Journal of Technology, Learning, and Assessment, 6 6. Developing a taxonomy of item model types to promote assessment engineering. Journal of Technology, Learning, and Assessment, 7 2.


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Computerized adaptive testing with item cloning. Applied Psychological Measurement, 27, Items shells: A method for writing effective multiple-choice test items. Evaluation and the Health Professions, 12, Hively, W, Patterson, H. A "universe-defined" system of arithmetic achieve-ment tests.

Journal of Educational Measurement, 5, Irvine, S. Item generation for test development. Janssen, R. Modeling the effect of item designs within the Rasch model. Models with item and group predictors. Wilson Eds. New York, NY: Springer. LaDuca, A. Item modeling procedures for constructing content-equivalent multiple-choice questions. Medical Education, 20, Leighton, J. The learning sciences in educational assessment: The role of cognitive models. Luecht, R.

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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.