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Olney, Andrew M. – Grantee Submission, 2022
Cloze items are a foundational approach to assessing readability. However, they require human data collection, thus making them impractical in automated metrics. The present study revisits the idea of assessing readability with cloze items and compares human cloze scores and readability judgments with predictions made by T5, a popular deep…
Descriptors: Readability, Cloze Procedure, Scores, Prediction
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2018
While hierarchical machine learning approaches have been used to classify texts into different content areas, this approach has, to our knowledge, not been used in the automated assessment of text difficulty. This study compared the accuracy of four classification machine learning approaches (flat, one-vs-one, one-vs-all, and hierarchical) using…
Descriptors: Artificial Intelligence, Classification, Comparative Analysis, Prediction
Neuman, Susan B.; Wong, Kevin M.; Kaefer, Tanya – Grantee Submission, 2017
The purpose of this study was to investigate the influence of digital and non-digital storybooks on low-income preschoolers' oral language comprehension. Employing a within-subject design on 38 four-year-olds from a Head Start program, we compared the effect of medium on preschoolers' target words and comprehension of stories. Four digital…
Descriptors: Oral Language, Story Reading, Low Income Groups, Disadvantaged Youth