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Direct linkLippert, Anne; Shubeck, Keith; Morgan, Brent; Hampton, Andrew; Graesser, Arthur – Technology, Knowledge and Learning, 2020
This article describes designs that use multiple conversational agents within the framework of intelligent tutoring systems. Agents in this case are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with them in natural language. The earliest conversational intelligent…
Descriptors: Intelligent Tutoring Systems, Man Machine Systems, Natural Language Processing, Educational Technology
Download full textLippert, Anne; Shubeck, Keith; Morgan, Brent; Hampton, Andrew; Graesser, Arthur – Grantee Submission, 2020
This article describes designs that use multiple conversational agents within the framework of intelligent tutoring systems. Agents in this case are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with them in natural language. The earliest conversational intelligent…
Descriptors: Intelligent Tutoring Systems, Man Machine Systems, Natural Language Processing, Educational Technology
Li, Haiying; Cai, Zhiqiang; Graesser, Arthur – Grantee Submission, 2018
In this study we developed and evaluated a crowdsourcing-based latent semantic analysis (LSA) approach to computerized summary scoring (CSS). LSA is a frequently used mathematical component in CSS, where LSA similarity represents the extent to which the to-be-graded target summary is similar to a model summary or a set of exemplar summaries.…
Descriptors: Computer Assisted Testing, Scoring, Semantics, Evaluation Methods
Li, Haiying; Cai, Zhiqiang; Graesser, Arthur – International Educational Data Mining Society, 2016
In this paper, we applied the crowdsourcing approach to develop an automated popularity summary scoring, called wild summaries. In contrast, the golden standard summaries generated by one or more experts are called expert summaries. The innovation of our study is to compute LSA (Latent Semantic Analysis) similarities between target summary and…
Descriptors: Peer Acceptance, Electronic Publishing, Collaborative Writing, Grading
Graesser, Arthur; Li, Haiying; Forsyth, Carol – Grantee Submission, 2014
Learning is facilitated by conversational interactions both with human tutors and with computer agents that simulate human tutoring and ideal pedagogical strategies. In this article, we describe some intelligent tutoring systems (e.g., AutoTutor) in which agents interact with students in natural language while being sensitive to their cognitive…
Descriptors: Intelligent Tutoring Systems, Teaching Methods, Computer Simulation, Dialogs (Language)
D'Mello, Sidney K.; Dowell, Nia; Graesser, Arthur – Journal of Experimental Psychology: Applied, 2011
There is the question of whether learning differs when students speak versus type their responses when interacting with intelligent tutoring systems with natural language dialogues. Theoretical bases exist for three contrasting hypotheses. The "speech facilitation" hypothesis predicts that spoken input will "increase" learning,…
Descriptors: Intelligent Tutoring Systems, Prior Learning, Natural Language Processing, Tutoring
Graesser, Arthur; McNamara, Danielle – Educational Psychologist, 2010
This article discusses the occurrence and measurement of self-regulated learning (SRL) both in human tutoring and in computer tutors with agents that hold conversations with students in natural language and help them learn at deeper levels. One challenge in building these computer tutors is to accommodate, encourage, and scaffold SRL because these…
Descriptors: Intelligent Tutoring Systems, Metacognition, Tutors, Natural Language Processing
