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Öncel, Püren; Flynn, Lauren E.; Sonia, Allison N.; Barker, Kennis E.; Lindsay, Grace C.; McClure, Caleb M.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2021
Automated Writing Evaluation systems have been developed to help students improve their writing skills through the automated delivery of both summative and formative feedback. These systems have demonstrated strong potential in a variety of educational contexts; however, they remain limited in their personalization and scope. The purpose of the…
Descriptors: Computer Assisted Instruction, Writing Evaluation, Formative Evaluation, Summative Evaluation
Balyan, Renu; Crossley, Scott A.; Brown, William, III; Karter, Andrew J.; McNamara, Danielle S.; Liu, Jennifer Y.; Lyles, Courtney R.; Schillinger, Dean – Grantee Submission, 2019
Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate…
Descriptors: Patients, Literacy, Health Services, Profiles
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Crossley, Scott A.; Allen, Laura K.; Kyle, Kristopher; McNamara, Danielle S. – Discourse Processes: A multidisciplinary journal, 2014
Natural language processing (NLP) provides a powerful approach for discourse processing researchers. However, there remains a notable degree of hesitation by some researchers to consider using NLP, at least on their own. The purpose of this article is to introduce and make available a "simple" NLP (SiNLP) tool. The overarching goal of…
Descriptors: Natural Language Processing, Discourse Analysis, Language Research, Computer Oriented Programs
Schillinger, Dean; Balyan, Renu; Crossley, Scott A.; McNamara, Danielle S.; Liu, Jennifer Y.; Karter, Andrew J. – Grantee Submission, 2020
Objective: To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing. Data Source: With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal.…
Descriptors: Literacy, Profiles, Computational Linguistics, Syntax
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Crossley, Scott A.; Skalicky, Stephen; Dascalu, Mihai; McNamara, Danielle S.; Kyle, Kristopher – Discourse Processes: A multidisciplinary journal, 2017
Research has identified a number of linguistic features that influence the reading comprehension of young readers; yet, less is known about whether and how these findings extend to adult readers. This study examines text comprehension, processing, and familiarity judgment provided by adult readers using a number of different approaches (i.e.,…
Descriptors: Reading Processes, Reading Comprehension, Readability, Adults
Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Grantee Submission, 2016
A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the literature on writing. Previous research suggests that higher quality…
Descriptors: Role, Writing (Composition), Natural Language Processing, Hypothesis Testing
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Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Journal of Educational Psychology, 2016
A commonly held belief among educators, researchers, and students is that high-quality texts are easier to read than low-quality texts, as they contain more engaging narrative and story-like elements. Interestingly, these assumptions have typically failed to be supported by the literature on writing. Previous research suggests that higher quality…
Descriptors: Role, Writing (Composition), Natural Language Processing, Hypothesis Testing
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Crossley, Scott A.; Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Journal of Educational Data Mining, 2016
This study investigates a novel approach to automatically assessing essay quality that combines natural language processing approaches that assess text features with approaches that assess individual differences in writers such as demographic information, standardized test scores, and survey results. The results demonstrate that combining text…
Descriptors: Essays, Scoring, Writing Evaluation, Natural Language Processing
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Crossley, Scott A.; Roscoe, Rod; McNamara, Danielle S. – Written Communication, 2014
This study identifies multiple profiles of successful essays via a cluster analysis approach using linguistic features reported by a variety of natural language processing tools. The findings from the study indicate that there are four profiles of successful writers for the samples analyzed. These four profiles are linguistically distinct from one…
Descriptors: Essays, Natural Language Processing, Computational Linguistics, Multivariate Analysis
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Duran, Nicholas D.; Hall, Charles; McCarthy, Philip M.; McNamara, Danielle S. – Applied Psycholinguistics, 2010
The words people use and the way they use them can reveal a great deal about their mental states when they attempt to deceive. The challenge for researchers is how to reliably distinguish the linguistic features that characterize these hidden states. In this study, we use a natural language processing tool called Coh-Metrix to evaluate deceptive…
Descriptors: Computer Mediated Communication, Linguistics, Information Technology, Deception