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Fostering Equitable Help-Seeking for K-3 Students in Low Income and Rural Contexts

Authors:

Judith Odili Uchidiuno,

Jessica Hammer,

Amy OganAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 684, Pages 1 - 14

https://doi.org/10.1145/3411764.3445144

Published: 07 May 2021 Publication History

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Abstract

Adaptive Collaborative Learning Support (ACLS) systems improve collaboration and learning for students over individual work or collaboration with non-adaptive support. However, many ACLS systems are ill-suited for rural contexts where students often need multiple kinds of support to complete tasks, may speak languages unsupported by the system, and require more than pre-assigned tutor-tutee student pairs for more equitable learning. We designed an intervention that fosters more equitable help-seeking by automatically detecting student struggles and prompts them to seek help from specific peers that can help. We conducted a mixed-methods experimental study with 98 K-3 students in a rural village in Tanzania over a one-month period, evaluating how the system affects student interactions, system engagement, and student learning. Our intervention increased student interactions by almost 4 times compared to the control condition, increased domain knowledge interactions, and propelled students to engage in more cognitively challenging activities.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
,
Aaron Quigley
University of New South Wales, Australia
,
Program Chairs:
Katherine Isbister
University of California Santa Cruz, USA
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Takeo Igarashi
The University of Tokyo, Japan
,
Publications Chairs:
Pernille Bjørn
University of Copenhagen, Denmark
,
Steven Drucker
Microsoft Research, USA

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Published: 07 May 2021

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Cited By

Kim SHo JLi YFan BYang WRamey JFox SZhu HZimmerman JEslami M(2024)Integrating Equity in Public Sector Data-Driven Decision Making: Exploring the Desired Futures of Underserved StakeholdersProceedings of the ACM on Human-Computer Interaction10.1145/36869058:CSCW2(1-39)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686905
Fung KLee LHui PSong S(2024)Can Underprivileged Children Learn Effectively at Home? A Six-Month Study of Game-Based Traditional Chinese Learning During the Pandemic LockdownIEEE Transactions on Learning Technologies10.1109/TLT.2023.325588217(294-309)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3255882
Huang ERees Lewis DGaudani SEasterday MGerber E(2023)Intelligent Coaching Systems: Understanding One-to-many Coaching for Ill-defined Problem SolvingProceedings of the ACM on Human-Computer Interaction10.1145/35796147:CSCW1(1-24)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579614
Kao DMagana AMousas C(2021)Evaluating Tutorial-Based Instructions for Controllers in Virtual Reality GamesProceedings of the ACM on Human-Computer Interaction10.1145/34746615:CHI PLAY(1-28)Online publication date: 6-Oct-2021
https://dl.acm.org/doi/10.1145/3474661

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