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McCarthy, Kathryn S.; Watanabe, Micah; Dai, Jianmin; McNamara, Danielle S. – Journal of Research on Technology in Education, 2020
Computer-based learning environments (CBLEs) provide unprecedented opportunities for personalized learning at scale. One such system, iSTART (Interactive Strategy Training for Active Reading and Thinking) is an adaptive, game-based tutoring system for reading comprehension. This paper describes how efforts to increase personalized learning have…
Descriptors: Game Based Learning, Reading Comprehension, High School Students, Educational Technology
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McNamara, Danielle S.; Arner, Tracy; Reilley, Elizabeth; Alvarado, Paul; Clark, Chani; Fikes, Thomas; Hale, Annie; Weigele, Betheny – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics
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McNamara, Danielle S.; Jacovina, Matthew; Allen, Laura K. – AERA Online Paper Repository, 2016
Within the context of comprehension and education, there has been a heavy emphasis placed on an individual's ability to construct a coherent and elaborated mental representation of text content. Previous research has aimed to establish the theoretical basis behind the comprehension process, as well as the most effective interventions for improving…
Descriptors: Individual Differences, Thinking Skills, Reading Comprehension, Cognitive Processes
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
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Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
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Fang, Ying; Roscoe, Rod D.; McNamara, Danielle S. – Grantee Submission, 2023
Artificial Intelligence (AI) based assessments are commonly used in a variety of settings including business, healthcare, policing, manufacturing, and education. In education, AI-based assessments undergird intelligent tutoring systems as well as many tools used to evaluate students and, in turn, guide learning and instruction. This chapter…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Student Evaluation, Evaluation Methods
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Jackson, G. Tanner; Boonthum-Denecke, Chutima; McNamara, Danielle S. – Grantee Submission, 2015
Intelligent Tutoring Systems (ITSs) are situated in a potential struggle between effective pedagogy and system enjoyment and engagement. iSTART, a reading strategy tutoring system in which students practice generating self-explanations and using reading strategies, employs two devices to engage the user. The first is natural language processing…
Descriptors: Natural Language Processing, Feedback (Response), Intelligent Tutoring Systems, Reading Strategies
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McCarthy, Kathryn S.; Likens, Aaron D.; Kopp, Kristopher K.; Perret, Cecile A.; Watanabe, Micah; McNamara, Danielle S. – Grantee Submission, 2018
The current study explored relations between non-cognitive traits (Grit, Learning Orientation, Performance Orientation), reading skill, and performance across three experiments conducted in the context of two intelligent tutoring systems, iSTART and Writing Pal. Results showed that learning outcomes (comprehension score, holistic essay score) were…
Descriptors: Persistence, Individual Characteristics, Reading Skills, Performance
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate…
Descriptors: Automation, Scoring, Documentation, Likert Scales
Öncel, Püren; Flynn, Lauren E.; Sonia, Allison N.; Barker, Kennis E.; Lindsay, Grace C.; McClure, Caleb M.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2021
Automated Writing Evaluation systems have been developed to help students improve their writing skills through the automated delivery of both summative and formative feedback. These systems have demonstrated strong potential in a variety of educational contexts; however, they remain limited in their personalization and scope. The purpose of the…
Descriptors: Computer Assisted Instruction, Writing Evaluation, Formative Evaluation, Summative Evaluation
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.; Grasser, Arthur C.; McNamara, Danielle S. – Grantee Submission, 2023
Assessments of natural language can provide vast information about individuals' thoughts and cognitive process, but they often rely on time-intensive human scoring, deterring researchers from collecting these sources of data. Natural language processing (NLP) gives researchers the opportunity to implement automated textual analyses across a…
Descriptors: Psychological Studies, Natural Language Processing, Automation, Research Methodology
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Jacovina, Matthew E.; Snow, Erica L.; Allen, Laura K.; Roscoe, Rod D.; Weston, Jennifer L.; Dai, Jianmin; McNamara, Danielle S. – Grantee Submission, 2015
Intelligent tutoring systems (ITSs) have been successful at improving students' performance across a variety of domains. To help achieve this widespread success, researchers have identified important behavioral and performance measures that can be used to guide instruction and feedback. Most systems, however, do not present these measures to the…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Feedback (Response)
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2021
Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale.…
Descriptors: Computer Assisted Testing, Scoring, Natural Language Processing, Computer Software
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