Tutorbot corpus: evidence of Human-Agent verbal alignment in Second Language learner dialogues

Arabella Sinclair, Kate McCurdy, Christopher G. Lucas, Adam Lopez, Dragan Gašević

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


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
EditorsCollin F. Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou
Place of PublicationMontreal Canada
PublisherPresses De I Universite Du Quebec
Number of pages6
ISBN (Electronic)9781733673600
Publication statusPublished - 2019
EventEducational Data Mining 2019 - Montreal , Canada
Duration: 2 Jul 20195 Jul 2019
Conference number: 12th


ConferenceEducational Data Mining 2019
Abbreviated titleEDM 2019
Internet address


  • Agent
  • Alignment
  • Assessment
  • Chatbot
  • Dialogue
  • Language learning
  • Second language
  • Student engagement
  • Tutoring

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