Comparing Human-Human to Human-Computer Tutorial Dialogue

2010 ◽  
Author(s):  
Natalie B. Steinhauser ◽  
Gwendolyn E. Campbell ◽  
Katherine M. Harrison ◽  
Leanne S. Taylor ◽  
Myroslava O. Dzikovska ◽  
...  
Keyword(s):  
Tutorial Dialogue ◽  
Computer Tutorial

scholarly journals Content, Social, and Metacognitive Statements: An Empirical Study Comparing Human-Human and Human-Computer Tutorial Dialogue

2010 ◽  
Author(s):  
Myroslava O. Dzikovska ◽  
Natalie B. Steinhauser ◽  
Johanna D. Moore ◽  
Gwendolyn E. Campbell ◽  
Katherine M. Harrison ◽  
...  
Keyword(s):  
Empirical Study ◽  
Tutorial Dialogue ◽  
Computer Tutorial

scholarly journals Content, Social, and Metacognitive Statements: An Empirical Study Comparing Human-Human and Human-Computer Tutorial Dialogue

2010 ◽  
pp. 93-108 ◽  
Author(s):  
Myroslava O. Dzikovska ◽  
Natalie B. Steinhauser ◽  
Johanna D. Moore ◽  
Gwendolyn E. Campbell ◽  
Katherine M. Harrison ◽  
...  
Keyword(s):  
Empirical Study ◽  
Tutorial Dialogue ◽  
Computer Tutorial

A Computer Tutorial on Consequences in Operant Learning

1997 ◽  
Vol 24 (3) ◽  
pp. 216-217 ◽  
Author(s):  
Robert B. Graham
Keyword(s):  
Correct Answer ◽  
Operant Learning ◽  
Study Guide ◽  
Questionnaire Data ◽  
General Psychology ◽  
Student Records ◽  
Student Reaction ◽  
Computer Tutorial ◽  
Behavioral Programming ◽  
Course Credit

This article describes a computer tutorial that teaches the fundamentals of consequences and contingencies in operant teaming. The tutorial content is appropriate for courses in general psychology, learning, and behavioral programming. Applications to animal and human situations are emphasized. The software repeats questions until the student is able to provide the correct answer, but spaces its repetitions to maximize retention. It saves student records as a basis for assignment of course credit. Student reaction to this form of presentation was very favorable. Questionnaire data showed that the students perceived the tutor as more useful in preparing for a test than a text or study guide would have been.

scholarly journals Evaluating the Effectiveness of a Tutorial Dialogue System for Self-Explanation

2004 ◽  
pp. 443-454 ◽  
Author(s):  
Vincent Aleven ◽  
Amy Ogan ◽  
Octav Popescu ◽  
Cristen Torrey ◽  
Kenneth Koedinger
Keyword(s):  
Dialogue System ◽  
Tutorial Dialogue

Collaborative Discourse Theory as a Foundation for Tutorial Dialogue

2002 ◽  
pp. 542-551 ◽  
Author(s):  
Jeff Rickel ◽  
Neal Lesh ◽  
Charles Rich ◽  
Candace L. Sidner ◽  
Abigail Gertner
Keyword(s):  
Discourse Theory ◽  
Tutorial Dialogue ◽  
Collaborative Discourse

Discourse cohesion in text and tutorial dialogue

2007 ◽  
Vol 15 (3) ◽  
pp. 199-213 ◽  
Author(s):  
Arthur C. Graesser ◽  
Moongee Jeon ◽  
Yan Yan ◽  
Zhiqiang Cai
Keyword(s):  
College Students ◽  
Natural Language ◽  
Semantic Similarity ◽  
Software Tool ◽  
Pedagogical Agent ◽  
Newtonian Physics ◽  
Tutorial Dialogue ◽  
Logical Operators ◽  
Different Types ◽  
Human Tutoring

Discourse cohesion is presumably an important facilitator of comprehension when individuals read texts and hold conversations. This study investigated components of cohesion and language in different types of discourse about Newtonian physics: A textbook, textoids written by experimental psychologists, naturalistic tutorial dialoguebetween expert human tutors and college students, andAutoTutor tutorial dialogue between a computer tutor and students (AutoTutor is an animated pedagogical agent that helps students learn about physics by holding conversations in natural language). We analyzed the four types of discourse with Coh-Metrix, a software tool that measures discourse on different components of cohesion, language, and readability. The cohesion indices included co-reference, syntactic and semantic similarity, causal cohesion, incidence of cohesion signals (e.g., connectives, logical operators), and many other measures. Cohesion data were quite similar for the two forms of discourse in expository monologue (textbooks and textoids) and for the two types of tutorial dialogue (i.e., students interacting with human tutors and AutoTutor), but very different between the discourse of expository monologue and tutorial dialogue. Coh-Metrix was also able to detect subtle differences in the language and discourse of AutoTutor versus human tutoring.

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