Tutorbot Corpus: Evidence of Human-Agent Verbal Alignment in Second Language Learner Dialogues [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).

Autor/inn/en	Sinclair, Arabella; McCurdy, Kate; Lucas, Christopher G.; Lopez, Adam; Gaševic, Dragan
Titel	Tutorbot Corpus: Evidence of Human-Agent Verbal Alignment in Second Language Learner Dialogues [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (12th, Montreal, Canada, Jul 2-5, 2019).
Quelle	(2019), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Second Language Learning; Second Language Instruction; Dialogs (Language); Teaching Methods; Intelligent Tutoring Systems; Comparative Analysis; Bayesian Statistics; Computational Linguistics; Learning Processes; Vocabulary Development; English (Second Language); Discourse Analysis; Foreign Countries; Spain (Barcelona) + Suchen Sie Ihr Suchwort? Zweitsprachenerwerb; Fremdsprachenunterricht; Dialog; Dialogs; Dialogue; Dialogues; Teaching method; Lehrmethode; Unterrichtsmethode; Intelligentes Tutorsystem; Linguistics; Computerlinguistik; Learning process; Lernprozess; Wortschatzarbeit; English as second language; English; Second Language; Englisch als Zweitsprache; Diskursanalyse; Ausland
Abstract	Prior research has shown that, under certain conditions, Human-Agent (H-A) alignment exists to a stronger degree than that found in Human-Human (H-H) communication. In an H-H Second Language (L2) setting, evidence of alignment has been linked to learning and teaching strategy. We present a novel analysis of H-A and H-H L2 learner dialogues using automated metrics of alignment. Our contributions are twofold: firstly we replicated the reported H-A alignment within an educational context, finding L2 students align to an automated tutor. Secondly, we performed an exploratory comparison of the alignment present in comparable H-A and H-H L2 learner corpora using Bayesian Gaussian Mixture Models (GMMs), finding preliminary evidence that students in H-A L2 dialogues showed greater variability in engagement. [For the full proceedings, see ED599096.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01