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Arthur C. Graesser
Researcher at University of Memphis
Publications - 623
Citations - 41856
Arthur C. Graesser is an academic researcher from University of Memphis. The author has contributed to research in topics: Intelligent tutoring system & Reading (process). The author has an hindex of 95, co-authored 614 publications receiving 38549 citations. Previous affiliations of Arthur C. Graesser include University of Illinois at Urbana–Champaign & University of California.
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Book ChapterDOI
Question Answering and Generation
TL;DR: This chapter presents an overview of recent developments in Question Answering and Generation starting with a description of the question landscape.
Journal ArticleDOI
Advances from the Office of Naval Research STEM Grand Challenge: expanding the boundaries of intelligent tutoring systems.
TL;DR: The current paper provides an overview of the Office of Naval Research STEM Grand Challenge program, the systems funded under the program, and summaries of the articles within this special issue.
Journal ArticleDOI
Computational linguistics analysis of leaders during crises in authoritarian regimes
TL;DR: The authors investigated linguistic patterns in the discourse of three prominent autocratic leaders whose tenure lasted for multiple decades and found that these leaders utilize the central persuasion route, with more formal discourse patterns during times of crises versus non-crises.
Proceedings Article
Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System.
Ying Fang,Keith T. Shubeck,Anne Lippert,Qinyu Cheng,Genghu Shi,Shi Feng,Jessica Gatewood,Su Chen,Zhiqiang Cai,Philip I. Pavlik,Jan C. Frijters,Daphne Greenberg,Arthur C. Graesser +12 more
TL;DR: Clustering analysis is used to capture learning patterns in over 250 adults who used the ITS, CSAL (Center for the Study of Adult Literacy) AutoTutor, to gain reading comprehension skills, and reveals four types of readers: proficient readers, struggling readers, conscientious readers and disengaged readers.
Proceedings Article
AutoTutor's Coverage of Expectations during Tutorial Dialogue.
TL;DR: AutoTutor is a learning environment with an animated agent that tutors students by holding a conversation in natural language and uses latent semantic analysis (LSA) as a major component that statistically represents world knowledge and tracks whether particular expectations and misconceptions are expressed by the learner.