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Botarleanu, Robert-Mihai; Dascalu, Mihai; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2020
A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified…
Descriptors: Natural Language Processing, Writing Skills, Difficulty Level, Reading Comprehension
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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
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Kopp, Kristopher J.; Johnson, Amy M.; Crossley, Scott A.; McNamara, Danielle S. – Grantee Submission, 2017
An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict…
Descriptors: Reading Comprehension, Reading Instruction, Intelligent Tutoring Systems, Reading Strategies
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Corlatescu, Dragos; Watanabe, Micah; Ruseti, Stefan; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2023
Reading comprehension is essential for both knowledge acquisition and memory reinforcement. Automated modeling of the comprehension process provides insights into the efficacy of specific texts as learning tools. This paper introduces an improved version of the Automated Model of Comprehension, version 3.0 (AMoC v3.0). AMoC v3.0 is based on two…
Descriptors: Reading Comprehension, Models, Concept Mapping, Graphs
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Crossley, Scott A.; McNamara, Danielle S. – Grantee Submission, 2014
This study explores correlations between human ratings of essay quality and component scores based on similar natural language processing indices and weighted through a principal component analysis. The results demonstrate that such component scores show small to large effects with human ratings and thus may be suitable to providing both summative…
Descriptors: Essays, Computer Assisted Testing, Writing Evaluation, Scores
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Dascalu, Mihai; Jacovina, Matthew E.; Soto, Christian M.; Allen, Laura K.; Dai, Jianmin; Guerrero, Tricia A.; McNamara, Danielle S. – Grantee Submission, 2017
iSTART is a web-based reading comprehension tutor. A recent translation of iSTART from English to Spanish has made the system available to a new audience. In this paper, we outline several challenges that arose during the development process, specifically focusing on the algorithms that drive the feedback. Several iSTART activities encourage…
Descriptors: Spanish, Reading Comprehension, Natural Language Processing, Intelligent Tutoring Systems
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Perret, Cecile A.; Johnson, Amy M.; McCarthy, Kathryn S.; Guerrero, Tricia A.; Dai, Jianmin; McNamara, Danielle S. – Grantee Submission, 2017
This paper introduces StairStepper, a new addition to Interactive Strategy Training for Active Reading and Thinking (iSTART), an intelligent tutoring system (ITS) that provides adaptive self-explanation training and practice. Whereas iSTART focuses on improving comprehension at levels geared toward answering challenging questions associated with…
Descriptors: Reading Comprehension, Reading Instruction, Intelligent Tutoring Systems, Reading Strategies
Nicula, Bogdan; Perret, Cecile A.; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2019
Theories of discourse argue that comprehension depends on the coherence of the learner's mental representation. Our aim is to create a reliable automated representation to estimate readers' level of comprehension based on different productions, namely self-explanations and answers to open-ended questions. Previous work relied on Cohesion Network…
Descriptors: Prediction, Reading Comprehension, Network Analysis, Information Sources
Nicula, Bogdan; Perret, Cecile A.; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2020
Open-ended comprehension questions are a common type of assessment used to evaluate how well students understand one of multiple documents. Our aim is to use natural language processing (NLP) to infer the level and type of inferencing within readers' answers to comprehension questions using linguistic and semantic features within their responses.…
Descriptors: Natural Language Processing, Taxonomy, Responses, Semantics
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McCarthy, Kathryn S.; Jacovina, Matthew E.; Snow, Erica L.; Guerrero, Tricia A.; McNamara, Danielle S. – Grantee Submission, 2017
iSTART is an intelligent tutoring system designed to provide self-explanation instruction and practice to improve students' comprehension of complex, challenging text. This study examined the effects of extended game-based practice within the system as well as the effects of two metacognitive supports implemented within this practice. High school…
Descriptors: Reading Comprehension, Reading Instruction, Intelligent Tutoring Systems, Reading Strategies
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Roscoe, Rod D.; Crossley, Scott A.; Snow, Erica L.; Varner, Laura K.; McNamara, Danielle S. – Grantee Submission, 2014
Automated essay scoring tools are often criticized on the basis of construct validity. Specifically, it has been argued that computational scoring algorithms may be unaligned to higher-level indicators of quality writing, such as writers' demonstrated knowledge and understanding of the essay topics. In this paper, we consider how and whether the…
Descriptors: Correlation, Essays, Scoring, Writing Evaluation
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Roscoe, Rod D.; Snow, Erica L.; Brandon, Russell D.; McNamara, Danielle S. – Grantee Submission, 2013
The ability of educational games to promote students' engagement in learning and practice depends on perceived enjoyment of those games. This study investigated high school students' perceptions and enjoyment of games within the Writing Pal intelligent tutoring system. In accord with research on motivation, results showed that perceived…
Descriptors: Educational Games, Educational Technology, Technology Uses in Education, Intelligent Tutoring Systems
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Johnson, Amy M.; Guerrero, Tricia A.; Tighe, Elizabeth L.; McNamara, Danielle S. – Grantee Submission, 2017
There is little empirical research available on the substantial problem of adult low literacy rates, and limited educational technologies are available to address distinct instructional needs of this population. This paper reports on development and testing of a version of Interactive Strategy Training for Active Reading and Thinking (iSTART) for…
Descriptors: Reading Comprehension, Reading Instruction, Intelligent Tutoring Systems, Reading Strategies
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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
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Johnson, Amy M.; McCarthy, Kathryn S.; Kopp, Kristopher J.; Perret, Cecile A.; McNamara, Danielle S. – Grantee Submission, 2017
Intelligent tutoring systems for ill-defined domains, such as reading and writing, are critically needed, yet uncommon. Two such systems, the Interactive Strategy Training for Active Reading and Thinking (iSTART) and Writing Pal (W-Pal) use natural language processing (NLP) to assess learners' written (i.e., typed) responses and provide immediate,…
Descriptors: Reading Instruction, Writing Instruction, Intelligent Tutoring Systems, Reading Strategies
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