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Sonia, Allison N.; Joseph, Magliano P.; McCarthy, Kathryn S.; Creer, Sarah D.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2022
The constructed responses individuals generate while reading can provide insights into their coherence-building processes. The current study examined how the cohesion of constructed responses relates to performance on an integrated writing task. Participants (N = 95) completed a multiple document reading task wherein they were prompted to think…
Descriptors: Natural Language Processing, Connected Discourse, Reading Processes, Writing Skills
Allen, Laura K.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2019
With increased availability of information in modern societies, individuals are often faced with complex decisions regarding how to integrate and judge the veracity of available information. Generally, these issues have been approached using computational techniques to "detect" and "reduce" the spread of information across…
Descriptors: Information Dissemination, Misconceptions, Discourse Analysis, Reader Text Relationship
Allen, Laura K.; Mills, Caitlin; Perret, Cecile; McNamara, Danielle S. – Grantee Submission, 2019
This study examines the extent to which instructions to self-explain vs. "other"-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146)…
Descriptors: Language Processing, Science Instruction, Computational Linguistics, Teaching Methods
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Allen, Laura K.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2017
The current study examined the degree to which the quality and characteristics of students' essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed…
Descriptors: Writing Evaluation, Essays, Persuasive Discourse, Natural Language Processing
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
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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