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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 Hits 1 – 7 of 7 1 Predicting Reading Comprehension from Constructed Responses: Explanatory Retrievals as Stealth Assessment McCarthy, Kathryn S.; Allen, Laura K.; Hinze, Scott R. In: Artificial Intelligence in Education (2020) BASE Show details 2 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 3 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 4 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.; McNamara, Danielle S.. - : Zenodo, 2016 Abstract: 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 features and individual differences increases the accuracy of automatically assigned essay scores over using either individual differences or text features alone. The findings presented here have important implications for writing educators because they reveal that essay scoring methods can benefit from the incorporation of features taken not only from the essay itself (e.g., features related to lexical and syntactic complexity), but also from the writer (e.g., vocabulary knowledge and writing attitudes). The findings have implications for educational data mining researchers because they demonstrate new natural language processing approaches that afford the automatic ... : The file is in PDF format. If your computer does not recognize it, simply download the file and then open it with your browser. ... Keyword: automated essay scoring; individual differences; intelligent tutoring systems; natural language processing; writing quality URL: https://dx.doi.org/10.5281/zenodo.3554594 https://zenodo.org/record/3554594 BASE Hide details 5 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 6 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 7 Linguistic microfeatures to predict L2 writing proficiency: A case study in Automated Writing Evaluation Crossley, Scott A.; Kyle, Kristopher; Allen, Laura K.... In: Journal of Writing Assessment, vol 7, iss 1 (2014) BASE Show details

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