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Botarleanu, Robert-Mihai; Dascalu, Mihai; Watanabe, Micah; McNamara, Danielle S.; Crossley, Scott Andrew – Grantee Submission, 2021
The ability to objectively quantify the complexity of a text can be a useful indicator of how likely learners of a given level will comprehend it. Before creating more complex models of assessing text difficulty, the basic building block of a text consists of words and, inherently, its overall difficulty is greatly influenced by the complexity of…
Descriptors: Multilingualism, Language Acquisition, Age, Models
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
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
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
McNamara, Danielle S. – Discourse Processes: A multidisciplinary journal, 2017
This study demonstrates the generalization of previous laboratory results showing the benefits of Self-Explanation Reading Training (SERT) to college students' course exam performance. The participants were 265 students enrolled in an Introductory Biology course, 59 of whom were provided with SERT. The results showed that SERT benefited students…
Descriptors: Reading Strategies, Reading Comprehension, Introductory Courses, Biology
McNamara, Danielle S. – Grantee Submission, 2017
This study demonstrates the generalization of previous laboratory results showing the benefits of self-explanation reading training (SERT) to college students' course exam performance. The participants were 265 students enrolled in an Introductory Biology course, 59 of whom were provided with SERT training. The results showed that SERT benefited…
Descriptors: Biology, Correlation, Introductory Courses, Knowledge Level
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
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
Crossley, Scott A.; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2014
The study applied the Multi-Dimensional analysis used by Biber (1988) to examine the functional parameters of essays. Co-occurrence patterns were identified within an essay corpus (n=1529) using a linguistic indices provided by Co-Metrix. These patterns were used to identify essay groups that shared features based upon situational parameters.…
Descriptors: Essays, Writing (Composition), Computational Linguistics, Cues
McNamara, Danielle S.; Louwerse, Max M.; McCarthy, Philip M.; Graesser, Arthur C. – Discourse Processes: A Multidisciplinary Journal, 2010
This study addresses the need in discourse psychology for computational techniques that analyze text on multiple levels of cohesion and text difficulty. Discourse psychologists often investigate phenomena related to discourse processing using lengthy texts containing multiple paragraphs, as opposed to single word and sentence stimuli.…
Descriptors: Computational Linguistics, Connected Discourse, Difficulty Level, Rhetoric
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