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Tutorbot Corpus: Evidence of Human-Agent Verbal Alignment in Second Language Learner Dialogues

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Title: Tutorbot Corpus: Evidence of Human-Agent Verbal Alignment in Second Language Learner Dialogues
Language: English
Authors: Sinclair, Arabella, McCurdy, Kate, Lucas, Christopher G., Lopez, Adam, Gaševic, Dragan
Source: International Educational Data Mining Society. 2019.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Peer Reviewed: Y
Page Count: 6
Publication Date: 2019
Document Type: Speeches/Meeting Papers
Reports - Research
Descriptors: 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
Geographic Terms: Spain (Barcelona)
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.]
Abstractor: As Provided
Entry Date: 2019
Accession Number: ED599239
Database: ERIC
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  Data: 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.]
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      – Text: English
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        PageCount: 6
    Subjects:
      – SubjectFull: Second Language Learning
        Type: general
      – SubjectFull: Second Language Instruction
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      – SubjectFull: Dialogs (Language)
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      – SubjectFull: Teaching Methods
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      – SubjectFull: Intelligent Tutoring Systems
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      – SubjectFull: Comparative Analysis
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      – SubjectFull: Spain (Barcelona)
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