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        <title>Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues</title>
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    <name type="personal">
        <namePart type="given">Luciana</namePart>
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    <name type="personal">
        <namePart type="given">Jayadev</namePart>
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        <namePart type="given">David</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>In this paper we investigate the task of modeling how long it would take a student to respond to a tutor question during a tutoring dialogue. Solving such a task has applications in educational settings such as intelligent tutoring systems, as well as in platforms that help busy human tutors to keep students engaged. Knowing how long it would normally take a student to respond to different types of questions could help tutors optimize their own time while answering multiple dialogues concurrently, as well as deciding when to prompt a student again. We study this problem using data from a service that offers tutor support for math, chemistry and physics through an instant messaging platform. We create a dataset of 240K questions. We explore several strong baselines for this task and compare them with human performance.</abstract>
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%0 Conference Proceedings
%T Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues
%A Benotti, Luciana
%A Bhaskaran, Jayadev
%A Kjartansson, Sigtryggur
%A Lang, David
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F benotti-etal-2018-modeling
%X In this paper we investigate the task of modeling how long it would take a student to respond to a tutor question during a tutoring dialogue. Solving such a task has applications in educational settings such as intelligent tutoring systems, as well as in platforms that help busy human tutors to keep students engaged. Knowing how long it would normally take a student to respond to different types of questions could help tutors optimize their own time while answering multiple dialogues concurrently, as well as deciding when to prompt a student again. We study this problem using data from a service that offers tutor support for math, chemistry and physics through an instant messaging platform. We create a dataset of 240K questions. We explore several strong baselines for this task and compare them with human performance.
%R 10.18653/v1/W18-6117
%U https://aclanthology.org/W18-6117
%U https://doi.org/10.18653/v1/W18-6117
%P 121-131

Markdown (Informal)

[Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues](https://aclanthology.org/W18-6117) (Benotti et al., WNUT 2018)

• Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues (Benotti et al., WNUT 2018)

ACL

• Luciana Benotti, Jayadev Bhaskaran, Sigtryggur Kjartansson, and David Lang. 2018. Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 121–131, Brussels, Belgium. Association for Computational Linguistics.