E-assessments of students profoundly influence their motivation and play a key role in the educational process. Adapting assessment techniques to current technological advancements allows for effective pedagogical practices, learning processes, and student engagement. The "Handbook of Research on E-Assessment in Higher Education"…

Investigated model for reducing time for administration of Myers-Briggs Type Indicator (MBTI) using real-data simulation of Bayesian scaling in computerized adaptive administration. Findings from simulation study using data from 127 undergraduates are strongly supportive of use of Bayesian scaled computerized adaptive administration of MBTI.…

The overall purpose of this research was to compare a maximum likelihood based tailored testing procedure to a Bayesian tailored testing procedure. The results indicated that both tailored testing procedures produced equally reliable ability estimates. Also an analysis of test length indicated that reasonable ability estimates could be obtained…

Developed an extension of the Rasch model from a Bayesian point of view and used the model to study whether learning occurred throughout a computer-administered intelligence test. Results from 137 college students indicate that learning did occur and that there might be individual differences in learning rate. (SLD)

The reliability of four branching algorithms commonly used in computer adaptive testing (CAT) was examined. These algorithms were: (1) maximum likelihood (MLE); (2) Bayesian; (3) modal Bayesian; and (4) crossover. Sixty-eight undergraduate college students were randomly assigned to one of the four conditions using the HyperCard-based CAT program,…

The issue of test-item selection in support of decision making in adaptive testing is considered. The number of items needed to make a decision is compared for two approaches: selecting items from an item pool that are most informative at the decision point or selecting items that are most informative at the examinee's ability level. The first…