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Tatsuoka, Kikumi K.; Tatsuoka, Maurice M. – Journal of Educational Measurement, 1983
This study introduces the individual consistency index (ICI), which measures the extent to which patterns of responses to parallel sets of items remain consistent over time. ICI is used as an error diagnostic tool to detect aberrant response patterns resulting from the consistent application of erroneous rules of operation. (Author/PN)
Descriptors: Achievement Tests, Algorithms, Error Patterns, Measurement Techniques
Tatsuoka, Kikumi K.; And Others – 1980
Implementation of an adaptive achievement test for teaching signed-numbers operations to junior high students is described. A computer program capable of finding 240 basic errors in signed-number computations was written on the PLATO system and used for analyzing a 64-item conventional test, as well as an adaptive test of addition problems. The…
Descriptors: Adaptive Testing, Computer Assisted Testing, Educational Diagnosis, Error Patterns
Birenbaum, Menucha; Tatsuoka, Kikumi K. – 1980
Much valuable information can be gained by analyzing the students' wrong responses. When a student answers a free response item she/he gives the response which she/he considers to be the correct one. Therefore, diagnosing the algorithm that led the student to his/her answer provides an important source of information for assessing his/her…
Descriptors: Academic Achievement, Achievement Tests, Adaptive Testing, Algorithms
Tatsuoka, Kikumi K.; Tatsuoka, Maurice M. – 1986
The rule space model permits measurement of cognitive skill acquisition, diagnosis of cognitive errors, and detection of the strengths and weaknesses of knowledge possessed by individuals. Two ways to classify an individual into his or her most plausible latent state of knowledge include: (1) hypothesis testing--Bayes' decision rules for minimum…
Descriptors: Artificial Intelligence, Bayesian Statistics, Cognitive Development, Computer Assisted Testing
Tatsuoka, Kikumi K.; Tatsuoka, Maurice M. – 1985
The study examines the rule space model, a probabilistic model capable of measuring cognitive skill acquisition and of diagnosing erroneous rules of operation in a procedural domain. The model involves two important components: (1) determination of a set of bug distributions (bug density functions representing clusters around the rules); and (2)…
Descriptors: Artificial Intelligence, Cognitive Processes, Computer Assisted Testing, Computer Software