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Wang, Zuowei; O'Reilly, Tenaha; Sabatini, John; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2021
We compared high school students' performance in a traditional comprehension assessment requiring them to identify key information and draw inferences from single texts, and a scenario-based assessment (SBA) requiring them to integrate, evaluate and apply information across multiple sources. Both assessments focused on a non-academic topic.…
Descriptors: Comparative Analysis, High School Students, Inferences, Reading Tests
McCarthy, Kathryn S.; Guerrero, Tricia A.; Kent, Kevin M.; Allen, Laura K.; McNamara, Danielle S.; Chao, Szu-Fu; Steinberg, Jonathan; O'Reilly, Tenaha; Sabatini, John – Discourse Processes: A Multidisciplinary Journal, 2018
Background knowledge is a strong predictor of reading comprehension, yet little is known about how different types of background knowledge affect comprehension. The study investigated the impacts of both domain and topic-specific background knowledge on students' ability to comprehend and learn from science texts. High school students (n = 3,650)…
Descriptors: Knowledge Level, Reading Comprehension, High School Students, Pretests Posttests
McCarthy, Kathryn S.; Guerrero, Tricia A.; Kent, Kevin M.; Allen, Laura K.; McNamara, Danielle S.; Chao, Szu-Fu; Steinberg, Jonathan; O'Reilly, Tenaha; Sabatini, John – Grantee Submission, 2018
Background knowledge is a strong predictor of reading comprehension; yet little is known about how different types of background knowledge affect comprehension. The study investigated the impacts of both domain and topic-specific background knowledge on students' ability to comprehend and learn from science texts. High school students (n = 3650)…
Descriptors: Knowledge Level, Reading Comprehension, High School Students, Pretests Posttests
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
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