A Method for Automatically Analyzing Intelligent Tutoring System Dialogues with Coh-Metrix

Autor/inn/en	Wolfe, Christopher R.; Widmer, Colin L.; Torrese, Christine V.; Dandignac, Mitchell
Titel	A Method for Automatically Analyzing Intelligent Tutoring System Dialogues with Coh-Metrix
Quelle	In: Journal of Learning Analytics, 5 (2018) 3, S.222-234 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Wolfe, Christopher R.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Data Analysis; Risk; Cancer; Females; Genetics; Intelligent Tutoring Systems; Scores; Dialogs (Language); Decision Making; Verbs; Form Classes (Languages); Computational Linguistics; Prediction; Cognitive Processes; Medical Research; Reliability; Inferences; Screening Tests; Simulation; Discourse Analysis + Suchen Sie Ihr Suchwort? Auswertung; Risiko; Carcinoma; Karzinom; Krebs (med); Krebserkrankung; Weibliches Geschlecht; Humangenetik; Intelligentes Tutorsystem; Dialog; Dialogs; Dialogue; Dialogues; Decision-making; Entscheidungsfindung; Analytischer Sprachbau; Linguistics; Computerlinguistik; Vorhersage; Cognitive process; Kognitiver Prozess; Reliabilität; Inference; Inferenz; Screening-Verfahren; Simulation program; Simulationsprogramm; Diskursanalyse
Abstract	We developed a method for using Coh-Metrix to automatically analyze tutorial dialogues. Coh-Metrix, a web-based tool for automatically evaluating text, is freely available to researchers. We applied the method to 190 tutorial dialogues between women and "BRCA Gist" from two experiments. "BRCA Gist" is an intelligent tutoring system (ITS) to help women make decisions about genetic testing for breast cancer risk. Tutorial dialogues scored high on measures of textual cohesion (deep cohesion, referential cohesion, and the composite variable formality). They also scored high on measures of the situation model (LSA verb overlap and causal verb and causal particle). However, there was mixed support for the hypothesis that higher scores on Coh-Metrix variables would predict subsequent comprehension. A Coh-Metrix principle is that the observable cohesion of a text is a reliable guide to the coherence of the reader's mental representation of that text. Thus it appears that interacting with "BRCA Gist" helped people form coherent mental representations of complex medical materials. We conclude that Coh-Metrix can be used to reliably assess tutorial dialogues and make inferences about the mental representations of people engaged in conversation with an ITS based on observable characteristics of the statements people make. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01