@inproceedings{maharjan-rus-2018-tutorial,
    title = "A Tutorial {M}arkov Analysis of Effective Human Tutorial Sessions",
    author = "Maharjan, Nabin  and
      Rus, Vasile",
    editor = "Tseng, Yuen-Hsien  and
      Chen, Hsin-Hsi  and
      Ng, Vincent  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3704/",
    doi = "10.18653/v1/W18-3704",
    pages = "30--34",
    abstract = "This paper investigates what differentiates effective tutorial sessions from less effective sessions. Towards this end, we characterize and explore human tutors' actions in tutorial dialogue sessions by mapping the tutor-tutee interactions, which are streams of dialogue utterances, into streams of actions, based on the language-as-action theory. Next, we use human expert judgment measures, evidence of learning (EL) and evidence of soundness (ES), to identify effective and ineffective sessions. We perform sub-sequence pattern mining to identify sub-sequences of dialogue modes that discriminate good sessions from bad sessions. We finally use the results of sub-sequence analysis method to generate a tutorial Markov process for effective tutorial sessions."
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    <abstract>This paper investigates what differentiates effective tutorial sessions from less effective sessions. Towards this end, we characterize and explore human tutors’ actions in tutorial dialogue sessions by mapping the tutor-tutee interactions, which are streams of dialogue utterances, into streams of actions, based on the language-as-action theory. Next, we use human expert judgment measures, evidence of learning (EL) and evidence of soundness (ES), to identify effective and ineffective sessions. We perform sub-sequence pattern mining to identify sub-sequences of dialogue modes that discriminate good sessions from bad sessions. We finally use the results of sub-sequence analysis method to generate a tutorial Markov process for effective tutorial sessions.</abstract>
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%0 Conference Proceedings
%T A Tutorial Markov Analysis of Effective Human Tutorial Sessions
%A Maharjan, Nabin
%A Rus, Vasile
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F maharjan-rus-2018-tutorial
%X This paper investigates what differentiates effective tutorial sessions from less effective sessions. Towards this end, we characterize and explore human tutors’ actions in tutorial dialogue sessions by mapping the tutor-tutee interactions, which are streams of dialogue utterances, into streams of actions, based on the language-as-action theory. Next, we use human expert judgment measures, evidence of learning (EL) and evidence of soundness (ES), to identify effective and ineffective sessions. We perform sub-sequence pattern mining to identify sub-sequences of dialogue modes that discriminate good sessions from bad sessions. We finally use the results of sub-sequence analysis method to generate a tutorial Markov process for effective tutorial sessions.
%R 10.18653/v1/W18-3704
%U https://aclanthology.org/W18-3704/
%U https://doi.org/10.18653/v1/W18-3704
%P 30-34
Markdown (Informal)
[A Tutorial Markov Analysis of Effective Human Tutorial Sessions](https://aclanthology.org/W18-3704/) (Maharjan & Rus, NLP-TEA 2018)
ACL