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Almond, Russell G.; Kim, Yoon Jeon; Velasquez, Gertrudes; Shute, Valerie J. – Measurement: Interdisciplinary Research and Perspectives, 2014
One of the key ideas of evidence-centered assessment design (ECD) is that task features can be deliberately manipulated to change the psychometric properties of items. ECD identifies a number of roles that task-feature variables can play, including determining the focus of evidence, guiding form creation, determining item difficulty and…
Descriptors: Educational Games, Simulation, Psychometrics, Educational Assessment
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Almond, 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
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Mislevy, Robert J.; Steinberg, Linda S.; Breyer, F. Jay; Almond, Russell G.; Johnson, Lynn – Applied Measurement in Education, 2002
Presents a design framework that incorporates integrated structures for modeling knowledge and skills, designing tasks, and extracting and synthesizing evidence. Illustrates these ideas in the context of a project that assesses problem solving in dental hygiene through computer-based simulations. (SLD)
Descriptors: Computer Simulation, Dental Hygienists, Educational Assessment, Evaluation Utilization
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Almond, 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
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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