AutoTutor: A tutor with dialogue in natural language

被引：236

作者：

Graesser, AC ^{[1
]}

Lu, SL ^{[1
]}

Jackson, GT ^{[1
]}

Mitchell, HH ^{[1
]}

Ventura, M ^{[1
]}

Olney, A ^{[1
]}

Louwerse, MM ^{[1
]}

机构：

[1] Univ Memphis, Dept Psychol, Memphis, TN 38152 USA

来源：

BEHAVIOR RESEARCH METHODS INSTRUMENTS & COMPUTERS | 2004年 / 36卷 / 02期

关键词：

Latent Semantic Analysis; Computer Literacy; Intelligent Tutoring System; World Knowledge; Discourse Process;

D O I：

10.3758/BF03195563

中图分类号：

B841 [心理学研究方法];

学科分类号：

040201 ;

摘要：

AutoTutor is a learning environment that tutors students by holding a conversation in natural language. AutoTutor has been developed for Newtonian qualitative physics and computer literacy. Its design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging problems (formulated as questions) from a curriculum script and then engages in mixed initiative dialogue that guides the student in building an answer. It provides the student with positive, neutral, or negative feedback on the student's typed responses, pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information with assertions, identifies and corrects erroneous ideas, answers the student's questions, and summarizes answers. AutoTutor has produced learning gains of approximately .70 sigma for deep levels of comprehension.

引用

页码：180 / 192

页数：13

共 80 条

[1] An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor [J].