Paper Info
Title
Automatic Analyses of Cohesion and Coherence in Human Tutorial Dialogues During Hypermedia: A Comparison among Mental Model Jumpers
Abstract
We analyzed cohesion and coherence in tutorial dialogues from 66 think-aloud transcripts collected from a human tutorial dialogue study which investigated the effect of tutoring on middle and high school students' learning about the circulatory system with hypermedia [1]. Our findings showed that there were significant differences in the tutorial dialogues of Jumpers (i.e., those who showed significant pretest-posttest mental model shifts about the science topic) versus No-jumpers (i.e., those who showed no significant shifts) in the semantic/conceptual similarity, readability scores, incidence scores of causal verbs and causal connectives, and turn length. We argue that the semantic/conceptual similarity of the discourse, causal verbs/causal connectives, and longer turns primarily facilitated the improvement in Jumpers' mental models and deep learning.
Year
DOI
Venue
2008
10.1007/978-3-540-69132-7_79
Intelligent Tutoring Systems
Keywords
Field
DocType
significant difference,significant shift,causal connective,automatic analyses,conceptual similarity,mental model jumpers,tutorial dialogue,deep learning,significant pretest-posttest mental model,human tutorial dialogues,mental model,human tutorial dialogue study,causal verb,cohesion,coherence
Cohesion (chemistry),Mental model,Computer science,Hypermedia,Coherence (physics),Readability,Artificial intelligence,Deep learning,Machine learning
Conference
Volume
ISSN
Citations 
5091
0302-9743
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Moongee Jeon101.01
Roger Azevedo212724.65