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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 89 1 Precision communication: Physicians’ linguistic adaptation to patients’ health literacy Schillinger, Dean; Duran, Nicholas D.; McNamara, Danielle S.... In: Sci Adv (2021) BASE Show details 2 Multi-document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links Dascalu, Maria-Dorinela; Ruseti, Stefan; Dascalu, Mihai... In: Artificial Intelligence in Education (2020) BASE Show details 3 Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study Schillinger, Dean; Balyan, Renu; Crossley, Scott A.... In: Health Serv Res (2020) BASE Show details 4 Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction Nicula, Bogdan; Perret, Cecile A.; Dascalu, Mihai... In: Artificial Intelligence in Education (2020) BASE Show details 5 Sequence-to-Sequence Models for Automated Text Simplification Botarleanu, Robert-Mihai; Dascalu, Mihai; Crossley, Scott Andrew... In: Artificial Intelligence in Education (2020) BASE Show details 6 Challenges and solutions to employing natural language processing and machine learning to measure patients’ health literacy and physician writing complexity: The ECLIPPSE study Brown, William; Balyan, Renu; Karter, Andrew J.... In: J Biomed Inform (2020) BASE Show details 7 Secure Messaging with Physicians by Proxies for Patients with Diabetes: Findings from the ECLIPPSE Study Semere, Wagahta; Crossley, Scott; Karter, Andrew J.... In: J Gen Intern Med (2019) BASE Show details 8 Writing flexibility in argumentative essays: a multidimensional analysis [<Journal>] Allen, Laura K. [Verfasser]; Likens, Aaron D. [Verfasser]; McNamara, Danielle S. [Verfasser] DNB Subject Category Language Show details 9 Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ... Crossley, Scott; Allen, Laura K.; Snow, Erica L.. - : Zenodo, 2016 BASE Show details 10 Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ... Crossley, Scott; Allen, Laura K.; Snow, Erica L.. - : Zenodo, 2016 BASE Show details 11 To Aggregate or Not? Linguistic Features in Automatic Essay Scoring and Feedback Systems Crossley, Scott A.; Kyle, Kristopher; McNamara, Danielle S. In: Journal of Writing Assessment, vol 8, iss 1 (2015) BASE Show details 12 Automated evaluation of text and discourse with Coh-Metrix McNamara, Danielle S.. - New York [u.a.] : Cambridge Univ. Press, 2014 Leibniz-Zentrum Allgemeine Sprachwissenschaft Show details 13 Does writing development equal writing quality? A computational investigation of syntactic complexity in L2 learners Crossley, Scott A.; McNamara, Danielle S. In: Journal of second language writing. - Amsterdam ˜[u.a]œ : Elsevier 26 (2014), 66-79 OLC Linguistik Show details 14 Analyzing Discourse Processing Using a Simple Natural Language Processing Tool Crossley, Scott A.; Allen, Laura K.; Kyle, Kristopher... In: Discourse processes. - London [u.a.] : Routledge, Taylor and Francis Group 51 (2014) 5, 511-534 OLC Linguistik Show details 15 Society for Text and Discourse Annual Meeting 2013: Introduction to the Special Issue McNamara, Danielle S.; Schober, Michael F. In: Discourse processes. - London [u.a.] : Routledge, Taylor and Francis Group 51 (2014) 5, 357-358 OLC Linguistik Show details 16 What Is Successful Writing? An Investigation Into the Multiple Ways Writers Can Write Successful Essays Crossley, Scott A.; Roscoe, Rod; McNamara, Danielle S. In: Written communication. - Beverly Hills, Calif. [u.a.] : Sage Publ. 31 (2014) 2, 184-214 OLC Linguistik Show details 17 Emergent behaviors in computer-based learning environments: Computational signals of catching up Snow, Erica L.; Jackson, G. Tanner; McNamara, Danielle S. In: Computers in human behavior. - Amsterdam [u.a.] : Elsevier 41 (2014), 62-70 OLC Linguistik Show details 18 Analyzing discourse processing using a simple natural language processing tool Crossley, Scott A.; Allen, Laura K.; Kyle, Kristopher... In: Discourse Processes 51 (2014) 5, 511-534 IDS Bibliografie zur Gesprächsforschung Show details 19 Linguistic microfeatures to predict L2 writing proficiency: A case study in Automated Writing Evaluation Crossley, Scott A.; Kyle, Kristopher; Allen, Laura K.; Guo, Liang; McNamara, Danielle S. In: Journal of Writing Assessment, vol 7, iss 1 (2014) Abstract: 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 corpus of 480 independent essays written for the TOEFL. A stepwise regression analysis indicated that six linguistic microfeatures explained 60% of the variance in human scores for essays in a test set, providing an exact accuracy of 55% and an adjacent accuracy of 96%. To examine the limitations of the model, a post-hoc analysis was conducted to investigate differences in the scoring outcomes produced by the model and the human raters for essays with score differences of two or greater (N = 20). Essays scored as high by the regression model and low by human raters contained more word types and perfect tense forms compared to essays scored high by humans and low by the regression model. Essays scored high by humans but low by the regression model had greater coherence, syntactic variety, syntactic accuracy, word choices, idiomaticity, vocabulary range, and spelling accuracy as compared to essays scored high by the model but low by humans. Overall, findings from this study provide important information about how linguistic microfeatures can predict L2 essay quality for TOEFL-type exams and about the strengths and weaknesses of automatic essay scoring models. URL: https://escholarship.org/uc/item/06n1v820 BASE Hide details 20 What's so simple about simplified texts? A computational and psycholinguistic investigation of text comprehension and text processing Crossley, Scott A.; Yang, Hae Sung; McNamara, Danielle S.. - : University of Hawaii National Foreign Language Resource Center, 2014. : Center for Language & Technology, 2014 BASE Show details Page: 1 2 3 4 5

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