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Corlatescu, Dragos-Georgian; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…
Descriptors: Reading Comprehension, Memory, Inferences, Syntax
Nicula, Bogdan; Dascalu, Mihai; Newton, Natalie N.; Orcutt, Ellen; McNamara, Danielle S. – Grantee Submission, 2021
Learning to paraphrase supports both writing ability and reading comprehension, particularly for less skilled learners. As such, educational tools that integrate automated evaluations of paraphrases can be used to provide timely feedback to enhance learner paraphrasing skills more efficiently and effectively. Paraphrase identification is a popular…
Descriptors: Computational Linguistics, Feedback (Response), Classification, Learning Processes
Nicula, Bogdan; Dascalu, Mihai; Newton, Natalie; Orcutt, Ellen; McNamara, Danielle S. – Grantee Submission, 2021
The ability to automatically assess the quality of paraphrases can be very useful for facilitating literacy skills and providing timely feedback to learners. Our aim is twofold: a) to automatically evaluate the quality of paraphrases across four dimensions: lexical similarity, syntactic similarity, semantic similarity and paraphrase quality, and…
Descriptors: Phrase Structure, Networks, Semantics, Feedback (Response)
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
Allen, Laura K.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2018
The assessment of argumentative writing generally includes analyses of the specific linguistic and rhetorical features contained in the individual essays produced by students. However, researchers have recently proposed that an individual's ability to flexibly adapt the linguistic properties of their writing may more accurately capture their…
Descriptors: Writing (Composition), Persuasive Discourse, Essays, Language Usage
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Crossley, Scott; Barnes, Tiffany; Lynch, Collin; McNamara, Danielle S. – International Educational Data Mining Society, 2017
This study takes a novel approach toward understanding success in a math course by examining the linguistic features and affect of students' language production within a blended (with both on-line and traditional face to face instruction) undergraduate course (n=158) on discrete mathematics. Three linear effects models were compared: (a) a…
Descriptors: Success, Mathematics Instruction, Language Usage, Blended Learning
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Graesser, Arthur C.; McNamara, Danielle S.; Cai, Zhiqang; Conley, Mark; Li, Haiying; Pennebaker, James – Elementary School Journal, 2014
Coh-Metrix analyzes texts on multiple measures of language and discourse that are aligned with multilevel theoretical frameworks of comprehension. Dozens of measures funnel into five major factors that systematically vary as a function of types of texts (e.g., narrative vs. informational) and grade level: narrativity, syntactic simplicity, word…
Descriptors: Statistical Analysis, Guidelines, Syntax, Reading Comprehension
Graesser, Arthur C.; McNamara, Danielle S.; Cai, Zhiqiang; Conley, Mark; Li, Haiying; Pennebaker, James – Grantee Submission, 2014
Coh-Metrix analyzes texts on multiple measures of language and discourse that are aligned with multilevel theoretical frameworks of comprehension. Dozens of measures funnel into five major factors that systematically vary as a function of types of texts (e.g., narrative vs. informational) and grade level: narrativity, syntactic simplicity, word…
Descriptors: Statistical Analysis, Guidelines, Syntax, Reading Comprehension
Guo, Liang; Crossley, Scott A.; McNamara, Danielle S. – Grantee Submission, 2013
This study explores whether linguistic features can predict second language writing proficiency in the Test of English as a Foreign Language (TOEFL iBT) integrated and independent writing tasks and, if so, whether there are differences and similarities in the two sets of predictive linguistic features. Linguistic features related to lexical…
Descriptors: English (Second Language), Linguistics, Second Language Learning, Writing Skills
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Varner, Laura K.; Jackson, G. Tanner; Snow, Erica L.; McNamara, Danielle S. – Grantee Submission, 2013
This study expands upon an existing model of students' reading comprehension ability within an intelligent tutoring system. The current system evaluates students' natural language input using a local student model. We examine the potential to expand this model by assessing the linguistic features of self-explanations aggregated across entire…
Descriptors: Reading Comprehension, Intelligent Tutoring Systems, Natural Language Processing, Reading Ability
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Crossley, Scott A.; Varner, Laura K.; McNamara, Danielle S. – Grantee Submission, 2013
Linguistic properties of writing prompts have been shown to influence the writing patterns contained in student essays. The majority of previous research on these prompt-based effects has focused on the lexical and syntactic properties of writing prompts and essays. The current study expands this research by investigating the effects of prompt…
Descriptors: Persuasive Discourse, Prompting, Writing Instruction, Essays
Crossley, Scott A.; Kyle, Kristopher; Allen, Laura K.; Guo, Liang; McNamara, Danielle S. – Grantee Submission, 2014
This study investigates the potential for linguistic microfeatures related to length, complexity, cohesion, relevance, topic, and rhetorical style to predict L2 writing proficiency. Computational indices were calculated by two automated text analysis tools (Coh- Metrix and the Writing Assessment Tool) and used to predict human essay ratings in a…
Descriptors: Computational Linguistics, Essays, Scoring, Writing Evaluation
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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
McNamara, Danielle S.; Crossley, Scott A.; Roscoe, Rod – Grantee Submission, 2013
The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Writing Instruction, Feedback (Response)
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Crossley, Scott A.; Allen, David; McNamara, Danielle S. – Language Teaching Research, 2012
Texts are routinely simplified to make them more comprehensible for second language learners. However, the effects of simplification upon the linguistic features of texts remain largely unexplored. Here we examine the effects of one type of text simplification: intuitive text simplification. We use the computational tool, Coh-Metrix, to examine…
Descriptors: Linguistic Input, English (Second Language), Second Language Learning, Intuition
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