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Welch, Catherine J.; Dunbar, Stephen B. – Educational Measurement: Issues and Practice, 2020
The use of assessment results to inform school accountability relies on the assumption that the test design appropriately represents the content and cognitive emphasis reflected in the state's standards. Since the passage of the Every Student Succeeds Act and the certification of accountability assessments through federal peer review practices,…
Descriptors: Accountability, Test Construction, State Standards, Content Validity
Tobin, Mollie; Lietz, Petra; Nugroho, Dita; Vivekanandan, Ramya; Nyamkhuu, Tserennadmid – Australian Council for Educational Research, 2015
Not much is known about the ways in which assessment data have actually been used in education policy to date. Understanding the role of assessments in informing system-level decision-making is a first step towards helping stakeholders improve the design and usefulness of assessments. Moreover, this understanding can help to further discussions…
Descriptors: Educational Policy, Measurement, Decision Making, Data Analysis
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van der Linden, Wim J. – 1986
Differences between traditional linear regression and a Bayesian approach to classification are discussed. Classification consists of assigning subjects to one of several available treatments on the basis of their test scores when the success of each treatment is measured by a different criterion. Formulating this problem as an empirical Bayes…
Descriptors: Achievement Tests, Bayesian Statistics, Classification, Decision Making
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Tittle, Carol Kehr; And Others – Educational Measurement: Issues and Practice, 1993
Major changes in educational and psychological theories that have come about since the cognitive and affective taxonomies of educational objectives were published in 1956 and 1964 are traced. The changes emphasize the need to understand thinking in the context of students' beliefs and self-directed cognitions. (SLD)
Descriptors: Achievement Tests, Affective Behavior, Classification, Classroom Research
Vos, Hans J. – 1994
As part of a project formulating optimal rules for decision making in computer assisted instructional systems in which the computer is used as a decision support tool, an approach that simultaneously optimizes classification of students into two treatments, each followed by a mastery decision, is presented using the framework of Bayesian decision…
Descriptors: Achievement Tests, Bayesian Statistics, Classification, Computer Managed Instruction