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Author Graesser, Art ♦ Olney, Andrew ♦ Ventura, Matthew ♦ Jackson, G. Tanner
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Major Component ♦ Tutor Student ♦ World Knowledge ♦ Learning Environment ♦ Particular Expectation ♦ Tutorial Dialogue ♦ Autotutor Autotutor ♦ Mixed Initiative Dialogue ♦ Latent Semantic Analysis ♦ Challenging Question ♦ Lsa Component ♦ Animated Agent ♦ Computer Tutor ♦ Natural Language
Description AutoTutor is a learning environment with an animated agent that tutors students by holding a conversation in natural language. AutoTutor presents challenging questions and then engages in mixed initiative dialogue that guides the student in building an answer. AutoTutor 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. This paper describes AutoTutor, reports some analyses on the adequacy of the LSA component, and proposes some improvements in computing the coverage of particular expectations and misconceptions. Tutorial Dialogue with AutoTutor AutoTutor is a computer tutor that holds conversations with learners in natural language (Graesser et al. 2004;
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2005-01-01
Publisher Institution Proceedings of the FLAIRS Conference