Tutorial Dialogue Modes in a Large Corpus of Online Tutoring Transcripts

被引：2

作者：

Morrison, Donald M. ^{[1
]}

Nye, Benjamin ^{[1
]}

Rus, Vasile ^{[1
]}

Snyder, Sarah ^{[2
]}

Boller, Jennifer ^{[2
]}

Miller, Kenneth ^{[2
]}

机构：

[1] Univ Memphis, Inst Intelligent Syst, Memphis, TN 38152 USA

[2] Tutor Com, New York, NY USA

来源：

ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015 | 2015年 / 9112卷

关键词：

Human tutorial dialogue; Dialogue mode; Data mining; Hybrid tutoring systems;

D O I：

10.1007/978-3-319-19773-9_101

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Building on previous work in this area, we provide a description and justification for a new way of identifying modes and mode switches in tutorial dialogues, part of a coding scheme involving 16 modes and 125 distinct dialogue acts. We also present preliminary results from an analysis of 1,438 human-annotated transcripts, consisting of more than 90,000 turns. Among other findings, this analysis shows subtle differences in the "mode architecture" of successful vs. less successful sessions, as judged by expert tutors.

引用

页码：722 / 725

页数：4

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