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Koris, Riina; Nokelainen, Petri – International Journal of Educational Management, 2015
Purpose: The purpose of this paper is to study Bayesian dependency modelling (BDM) to validate the model of educational experiences and the student-customer orientation questionnaire (SCOQ), and to identify the categories of educatonal experience in which students expect a higher educational institutions (HEI) to be student-customer oriented.…
Descriptors: College Students, Questionnaires, Bayesian Statistics, Educational Experience

Murphy, Gregory L.; Ross, Brian H. – Cognitive Psychology, 1994
Eleven experiments involving over 200 undergraduate students investigated how categorization of examples influences feature prediction for new examples. Results suggest that category-based prediction generally relies on a single category rather than multiple categories when there is a clear target category. (SLD)
Descriptors: Bayesian Statistics, Classification, Higher Education, Inferences

Jones, W. Paul – Measurement and Evaluation in Counseling and Development, 1993
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.…
Descriptors: Bayesian Statistics, Classification, College Students, Computer Assisted Testing
Shields, W. S. – 1974
A procedure for predicting categorical outcomes using categorical predictor variables was described by Moonan. This paper describes a related technique which uses prior probabilities, updated by joint likelihoods, as classification criteria. The procedure differs from Moonan's in that the outcome having the greatest posterior probability is…
Descriptors: Bayesian Statistics, Behavioral Science Research, Classification, Higher Education
Haladyna, Tom; Roid, Gale – 1980
The problems associated with misclassifying students when pass-fail decisions are based on test scores are discussed. One protection against misclassification is to set a confidence interval around the cutting score. Those whose scores fall above the interval are passed; those whose scores fall below the interval are failed; and those whose scores…
Descriptors: Bayesian Statistics, Classification, Comparative Analysis, Criterion Referenced Tests