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

Arabella Sinclair, Katherine McCurdy, Christopher Lucas, Adam Lopez, Dragan Gasevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings of the 12th International Conference on Educational Data Mining
Subtitle of host publicationJuly 2nd − 5th 2019 Montreal Canada
EditorsCollin F Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou
Number of pages6
ISBN (Electronic)978-1-7336736-0-0
Publication statusPublished - 5 Jul 2019
EventEducational Data Mining 2019 - Montreal, Canada
Duration: 2 Jul 20195 Jul 2019


ConferenceEducational Data Mining 2019
Abbreviated titleEDM 2019
Internet address


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