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Christopher R. Cox; Matthew J. Cooper Borkenhagen; Mark S. Seidenberg – Grantee Submission, 2019
Learning to read English requires learning the complex statistical dependencies between orthography and phonology. Previous research has focused on how these statistics are learned in neural network models provided with as much training as needed. Children, however, are expected to acquire this knowledge in a few years of school with only limited…
Descriptors: Second Language Learning, English (Second Language), Reading Instruction, Orthographic Symbols
Peter Organisciak; Michele Newman; David Eby; Selcuk Acar; Denis Dumas – Grantee Submission, 2023
Purpose: Most educational assessments tend to be constructed in a close-ended format, which is easier to score consistently and more affordable. However, recent work has leveraged computation text methods from the information sciences to make open-ended measurement more effective and reliable for older students. This study asks whether such text…
Descriptors: Learning Analytics, Child Language, Semantics, Age Differences
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Zhang, Mo; Breyer, F. Jay; Lorenz, Florian – ETS Research Report Series, 2013
In this research, we investigated the suitability of implementing "e-rater"® automated essay scoring in a high-stakes large-scale English language testing program. We examined the effectiveness of generic scoring and 2 variants of prompt-based scoring approaches. Effectiveness was evaluated on a number of dimensions, including agreement…
Descriptors: Computer Assisted Testing, Computer Software, Scoring, Language Tests
Jia, Yujie – ProQuest LLC, 2013
This study employed Bachman and Palmer's (2010) Assessment Use Argument framework to investigate to what extent the use of a second language oral test as an exit test in a Hong Kong university can be justified. It also aimed to help test developers of this oral test identify the most critical areas in the current test design that might need…
Descriptors: Test Use, Language Tests, Oral Language, Second Language Learning
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von Davier, Matthias – ETS Research Report Series, 2005
Probabilistic models with more than one latent variable are designed to report profiles of skills or cognitive attributes. Testing programs want to offer additional information beyond what a single test score can provide using these skill profiles. Many recent approaches to skill profile models are limited to dichotomous data and have made use of…
Descriptors: Models, Diagnostic Tests, Language Tests, Language Proficiency