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Characterizing Amateur Tutoring Behavior on a Large Online Learning Platform

Authors:

Prasanta Bhattacharya,

Tuan PhanAuthors Info & Claims

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

Pages 239 - 242

https://doi.org/10.1145/3430895.3460148

Published: 08 June 2021 Publication History

Abstract

Online tutoring is an increasingly popular learning and business model, which matches students with amateur tutors recruited through a platform. While such platforms offer a unique opportunity to study how online and on-demand learning happens at scale, little is still known about how tutors engage on the platform. We present an exploratory analysis of tutor engagement on a large online tutoring platform, using an anonymized dataset of virtual sessions between tutors and students. Specifically, we show significant association between tutor engagement and associated factors like the subject and difficulty level of questions, and the experience of tutors on the platform. Moreover, we highlight important heterogeneities in tutor behavior, particularly in their work intensity. Our findings hold important implications for tutor retention and learning effectiveness on such platforms.

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Cited By

Wang DGao LShan DChen GZhang CKao B(2024)When Tutors Simultaneously Instruct Students from the Primary, Middle, and High School Levels in Online One-on-One Tutoring: Investigating the Interaction Dynamics Using AI, ENA, and LSA MethodsJournal of Science Education and Technology10.1007/s10956-024-10154-4Online publication date: 13-Sep-2024
https://doi.org/10.1007/s10956-024-10154-4

Index Terms

Characterizing Amateur Tutoring Behavior on a Large Online Learning Platform
1. Applied computing
  1. Education
    1. Distance learning
    2. E-learning
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Informal education
      2. Student assessment

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Information

Published In

cover image ACM Other conferences

L@S '21: Proceedings of the Eighth ACM Conference on Learning @ Scale

June 2021

380 pages

ISBN:9781450382151

DOI:10.1145/3430895

General Chair:
Christoph Meinel
Hasso Plattner Institute for Digital Engineering Potsdam, Germany
,
Program Chairs:
Mar Pérez-Sanagustín
Paul Sabatier University Toulouse III, France
,
Marcus Specht
TU Delft, Netherlands
,
Amy Ogan
Carnegie Mellon University, USA

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2021

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Hong Kong Research Grants Council

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L@S '21

L@S '21: Eighth (2021) ACM Conference on Learning @ Scale

June 22 - 25, 2021

Virtual Event, Germany

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Overall Acceptance Rate 117 of 440 submissions, 27%

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Cited By

Wang DGao LShan DChen GZhang CKao B(2024)When Tutors Simultaneously Instruct Students from the Primary, Middle, and High School Levels in Online One-on-One Tutoring: Investigating the Interaction Dynamics Using AI, ENA, and LSA MethodsJournal of Science Education and Technology10.1007/s10956-024-10154-4Online publication date: 13-Sep-2024
https://doi.org/10.1007/s10956-024-10154-4

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