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 |
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| 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 |
| 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.] |
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