TY - JOUR
T1 - Dynamic Measurement Modeling
T2 - Using Nonlinear Growth Models to Estimate Student Learning Capacity
AU - Dumas, Denis G.
AU - McNeish, Daniel
N1 - Publisher Copyright:
© 2017, © 2017 AERA.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Single-timepoint educational measurement practices are capable of assessing student ability at the time of testing but are not designed to be informative of student capacity for developing in any particular academic domain, despite commonly being used in such a manner. For this reason, such measurement practice systematically underestimates the potential of students from nondominant socioeconomic or ethnic groups, who may not have had adequate opportunity to develop various academic skills but can nonetheless do so in the future. One long-standing approach to the partial rectification of this issue is dynamic assessment (DA), a technique that features multiple testing occasions integrated with learning opportunities. However, DA is extremely resource intensive to incorporate into educational assessment practice and cannot be applied to extant large-scale data sets. In this article, the authors describe a recently developed statistical technique, dynamic measurement modeling (DMM), which is capable of estimating quantities associated with DA—including student capacity for learning a particular skill—from existing large-scale longitudinal assessment data, allowing the core concepts of DA to be scaled up for use with secondary data sets such as those collected by Statewide Longitudinal Data Systems in the United States. The authors show that by considering several assessments over time, student capacity can be reliably estimated, and these capacity estimates are much less affected by student race/ethnicity, gender, and socioeconomic status than are single-timepoint assessment scores, thereby improving the consequential validity of measurement.
AB - Single-timepoint educational measurement practices are capable of assessing student ability at the time of testing but are not designed to be informative of student capacity for developing in any particular academic domain, despite commonly being used in such a manner. For this reason, such measurement practice systematically underestimates the potential of students from nondominant socioeconomic or ethnic groups, who may not have had adequate opportunity to develop various academic skills but can nonetheless do so in the future. One long-standing approach to the partial rectification of this issue is dynamic assessment (DA), a technique that features multiple testing occasions integrated with learning opportunities. However, DA is extremely resource intensive to incorporate into educational assessment practice and cannot be applied to extant large-scale data sets. In this article, the authors describe a recently developed statistical technique, dynamic measurement modeling (DMM), which is capable of estimating quantities associated with DA—including student capacity for learning a particular skill—from existing large-scale longitudinal assessment data, allowing the core concepts of DA to be scaled up for use with secondary data sets such as those collected by Statewide Longitudinal Data Systems in the United States. The authors show that by considering several assessments over time, student capacity can be reliably estimated, and these capacity estimates are much less affected by student race/ethnicity, gender, and socioeconomic status than are single-timepoint assessment scores, thereby improving the consequential validity of measurement.
KW - achievement gap
KW - assessment
KW - effect size
KW - individual differences
KW - longitudinal studies
KW - psychometrics
KW - testing
UR - http://www.scopus.com/inward/record.url?scp=85028683916&partnerID=8YFLogxK
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U2 - 10.3102/0013189X17725747
DO - 10.3102/0013189X17725747
M3 - Article
AN - SCOPUS:85028683916
SN - 0013-189X
VL - 46
SP - 284
EP - 292
JO - Educational Researcher
JF - Educational Researcher
IS - 6
ER -