Understanding Distributed Tutorship in Online Language Tutoring

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With the rise of the gig economy, online language tutoring platforms are becoming increasingly popular. They provide temporary and flexible jobs for native speakers as tutors and allow language learners to have one-on-one speaking practices on demand. However, the lack of stable relationships hinders tutors and learners from building long-term trust. "Distributed tutorship"- temporally discontinuous learning experience with different tutors - has been underexplored yet has many implications for modern learning platforms. In this paper, we analyzed tutorship sequences of 15, 959 learners and found that around 40% of learners change to new tutors every session; 44% learners change to new tutors while reverting to previous tutors sometimes; only 16% learners change to new tutors and then fix on one tutor. We also found suggestive evidence that higher distributedness - higher diversity and lower continuity in tutorship - is correlated to slower improvements in speaking performance scores with a similar number of sessions. We further surveyed 519 and interviewed 40 learners and found that more learners preferred fixed tutorship while some do not have it due to various reasons. Finally, we conducted semi-structured interviews with three tutors and one product manager to discuss the implications for improving the continuity in learning under distributed tutorship.
Publisher
Association for Computing Machinery
Issue Date
2022-03-23
Language
English
Citation

12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022, pp.164 - 174

DOI
10.1145/3506860.3506883
URI
http://hdl.handle.net/10203/300270
Appears in Collection
CS-Conference Papers(학술회의논문)
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