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Banawan, Michelle; Butterfuss, Reese; Taylor, Karen S.; Christhilf, Katerina; Hsu, Claire; O'Loughlin, Connor; Allen, Laura K.; Roscoe, Rod D.; McNamara, Danielle S. – Grantee Submission, 2023
Writing is essential for success in academics and everyday tasks, but the development of writing skills depends on consistent access to high-quality instruction, extended practice, and personalized feedback. To address these demands and meet students' needs, educators and researchers have turned to technology-based writing tools. Ideally, these…
Descriptors: Intelligent Tutoring Systems, Writing (Composition), Technology Uses in Education, Feedback (Response)
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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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Fang, Ying; Li, Tong; Huynh, Linh; Christhilf, Katerina; Roscoe, Rod D.; McNamara, Danielle S. – Grantee Submission, 2023
Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has…
Descriptors: Literacy, Student Evaluation, Educational Games, Literacy Education
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Nicula, Bogdan; Dascalu, Mihai; Arner, Tracy; Balyan, Renu; McNamara, Danielle S. – Grantee Submission, 2023
Text comprehension is an essential skill in today's information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while…
Descriptors: Reading Comprehension, Language Processing, Models, STEM Education
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Mason, Anna E.; Braasch, Jason L. G.; Greenberg, Daphne; Kessler, Erica D.; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2022
This study examined the extent to which prior beliefs and reading instructions impacted elements of a reader's mental representation of multiple texts. College students' beliefs about childhood vaccinations were assessed before reading two anti-vaccine and two pro-vaccine texts. Participants in the experimental condition read for the purpose of…
Descriptors: College Students, Beliefs, Immunization Programs, Vocabulary
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Allen, Laura K.; Jacovina, Matthew E.; Johnson, Adam C.; McNamara, Danielle S.; Roscoe, Rod D. – Grantee Submission, 2016
Revising is an essential writing process yet automated writing evaluation systems tend to give feedback on discrete essay drafts rather than changes across drafts. We explore the feasibility of automated revision detection and its potential to guide feedback. Relationships between revising behaviors and linguistic features of students' essays are…
Descriptors: Revision (Written Composition), Automation, Writing Evaluation, Feedback (Response)
Crossley, Scott A.; Kim, Minkyung; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2019
Summarization is an effective strategy to promote and enhance learning and deep comprehension of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation of students' summaries requires time and effort. This problem has led to the development of automated models of summarization quality. However,…
Descriptors: Automation, Writing Evaluation, Natural Language Processing, Artificial Intelligence
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McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2023
When students learn, they activate, use, revise, and acquire knowledge. As such, knowledge is a fundamental asset. We advocate for an asset-based approach which capitalizes on students' knowledge through prompts and activities that invite learners to leverage what they already know. Considering knowledge as an asset means that educators must…
Descriptors: Epistemology, Definitions, Prompting, Learning Activities
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Shin, Jinnie; Balyan, Renu; Banawan, Michelle P.; Arner, Tracy; Leite, Walter L.; McNamara, Danielle S. – Grantee Submission, 2023
Despite the proliferation of video-based instruction and its benefits--such as promoting student autonomy and self-paced learning--the complexities of online teaching remain a challenge. To be effective, educators require extensive training in digital teaching methodologies. As such, there's a pressing need to examine and comprehend the…
Descriptors: Algebra, Mathematics Instruction, Video Technology, Personal Autonomy
McCarthy, Kathryn S.; Watanabe, Micah; Dai, Jianmin; McNamara, Danielle S. – Grantee Submission, 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
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
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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