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Direct linkAlmond, Russell G.; Mulder, Joris; Hemat, Lisa A.; Yan, Duanli – Journal of Educational and Behavioral Statistics, 2009
Bayesian network models offer a large degree of flexibility for modeling dependence among observables (item outcome variables) from the same task, which may be dependent. This article explores four design patterns for modeling locally dependent observations: (a) no context--ignores dependence among observables; (b) compensatory context--introduces…
Descriptors: Bayesian Statistics, Models, Observation, Experiments
Download full textAlmond, Russell G. – ETS Research Report Series, 2007
Over the course of instruction, instructors generally collect a great deal of information about each student. Integrating that information intelligently requires models for how a student's proficiency changes over time. Armed with such models, instructors can "filter" the data--more accurately estimate the student's current proficiency…
Descriptors: Markov Processes, Decision Making, Student Evaluation, Learning Processes
Almond, Russell G.; Mulder, Joris; Hemat, Lisa A.; Yan, Duanli – ETS Research Report Series, 2006
Bayesian network models offer a large degree of flexibility for modeling dependence among observables (item outcome variables) from the same task that may be dependent. This paper explores four design patterns for modeling locally dependent observations from the same task: (1) No context--Ignore dependence among observables; (2) Compensatory…
Descriptors: Bayesian Statistics, Networks, Models, Design
