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Showing 1 to 15 of 19 results Save | Export
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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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Roscoe, Rod D.; McNamara, Danielle S. – Journal of Educational Psychology, 2013
The Writing Pal (W-Pal) is a novel intelligent tutoring system (ITS) that offers writing strategy instruction, game-based practice, essay writing practice, and formative feedback to developing writers. Compared to more tractable and constrained learning domains for ITS, writing is an ill-defined domain because the features of effective writing are…
Descriptors: Feasibility Studies, Intelligent Tutoring Systems, Writing Instruction, Writing Strategies
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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)
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Dascalu, Mihai; Trausan-Matu, Stefan; McNamara, Danielle S.; Dessus, Philippe – International Journal of Computer-Supported Collaborative Learning, 2015
As Computer-Supported Collaborative Learning (CSCL) gains a broader usage, the need for automated tools capable of supporting tutors in the time-consuming process of analyzing conversations becomes more pressing. Moreover, collaboration, which presumes the intertwining of ideas or points of view among participants, is a central element of dialogue…
Descriptors: Cooperative Learning, Electronic Learning, Computer Mediated Communication, Computer Uses in Education
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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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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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)
Panaite, Marilena; Ruseti, Stefan; Dascalu, Mihai; Balyan, Renu; McNamara, Danielle S.; Trausan-Matu, Stefan – Grantee Submission, 2019
Intelligence Tutoring Systems (ITSs) focus on promoting knowledge acquisition, while providing relevant feedback during students' practice. Self-explanation practice is an effective method used to help students understand complex texts by leveraging comprehension. Our aim is to introduce a deep learning neural model for automatically scoring…
Descriptors: Computer Assisted Testing, Scoring, Intelligent Tutoring Systems, Natural Language Processing
McCarthy, Kathryn S.; Likens, Aaron D.; Johnson, Amy M.; Guerrero, Tricia A.; McNamara, Danielle S. – Grantee Submission, 2018
Research suggests that promoting metacognitive awareness can increase performance in, and learning from, intelligent tutoring systems (ITSs). The current work examines the effects of two metacognitive prompts within iSTART, a reading comprehension strategy ITS in which students practice writing quality self-explanations. In addition to comparing…
Descriptors: Metacognition, Difficulty Level, Prompting, Intelligent Tutoring Systems
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McCarthy, Kathryn S.; Likens, Aaron D.; Johnson, Amy M.; Guerrero, Tricia A.; McNamara, Danielle S. – International Journal of Artificial Intelligence in Education, 2018
Research suggests that promoting metacognitive awareness can increase performance in, and learning from, intelligent tutoring systems (ITSs). The current work examines the effects of two metacognitive prompts within iSTART, a reading comprehension strategy ITS in which students practice writing quality self-explanations. In addition to comparing…
Descriptors: Metacognition, Difficulty Level, Prompting, Intelligent Tutoring Systems
McCarthy, Kathryn S.; Watanabe, Micah; McNamara, Danielle S. – Grantee Submission, 2020
The Design Implementation Framework, or DIF, is a design approach that evaluates learner and user experience at multiple points in the development of intelligent tutoring systems. In this chapter, we explore how DIF was used to make system modifications to iSTART, a game-based intelligent tutoring system for reading comprehension. Using DIF as a…
Descriptors: Intelligent Tutoring Systems, Reading Comprehension, Educational Games, Program Development
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Jacovina, Matthew E.; McNamara, Danielle S. – Grantee Submission, 2017
In this chapter, we describe several intelligent tutoring systems (ITSs) designed to support student literacy through reading comprehension and writing instruction and practice. Although adaptive instruction can be a powerful tool in the literacy domain, developing these technologies poses significant challenges. For example, evaluating the…
Descriptors: Intelligent Tutoring Systems, Literacy Education, Educational Technology, Technology Uses in Education
McNamara, Danielle S.; Jacovina, Matthew E.; Snow, Erica L.; Allen, Laura K. – Grantee Submission, 2015
Work in cognitive and educational psychology examines a variety of phenomena related to the learning and retrieval of information. Indeed, Alice Healy, our honoree, and her colleagues have conducted a large body of groundbreaking research on this topic. In this article we discuss how 3 learning principles (the generation effect, deliberate…
Descriptors: Learning Processes, Instructional Design, Intelligent Tutoring Systems, Writing Instruction
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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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