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Designing smarter touch-based interfaces for educational contexts

Authors:

Germaine IrwinAuthors Info & Claims

Personal and Ubiquitous Computing, Volume 18, Issue 6

Pages 1471 - 1483

https://doi.org/10.1007/s00779-013-0749-9

Published: 01 August 2014 Publication History

Abstract

In next-generation classrooms and educational environments, interactive technologies such as surface computing, natural gesture interfaces, and mobile devices will enable new means of motivating and engaging students in active learning. Our foundational studies provide a corpus of over 10,000 touch interactions and nearly 7,000 gestures collected from nearly 70 adults and children aged 7 years and above, which can help us understand the characteristics of children's interactions in these modalities and how they differ from adults. Based on these data, we identify key design and implementation challenges of supporting children's touch and gesture interactions, and we suggest ways to address them. For example, we find children have more trouble successfully acquiring onscreen targets and having their gestures recognized than do adults, especially the youngest age group (7---10 years old). The contributions of this work provide a foundation that will enable touch-based interactive educational apps that increase student success.

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Cited By

Lin CSong TMiao YHe JYang MWang HShen C(2024)ChildShield: An Implicit and Continuous Child Identification System on SmartphonesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.330241421:4(2257-2272)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TDSC.2023.3302414
Miao YTian QLin CSong TZhou YZhao JGao SYang MShen CElkind E(2023)Learning heuristically-selected and neurally-guided feature for age group recognition using unconstrained smartphone interactionProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/338(3029-3037)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/338
Guarino ALettieri NMalandrino DZaccagnino RCapo C(2022)Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devicesNeural Computing and Applications10.1007/s00521-022-07454-434:21(18473-18495)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00521-022-07454-4
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Index Terms

Designing smarter touch-based interfaces for educational contexts
1. Applied computing
  1. Education
  2. Enterprise computing
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens

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cover image Personal and Ubiquitous Computing

Personal and Ubiquitous Computing Volume 18, Issue 6

August 2014

243 pages

ISSN:1617-4909

Issue’s Table of Contents

Copyright © Copyright © 2014 Springer-Verlag London.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 August 2014

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Cited By

Lin CSong TMiao YHe JYang MWang HShen C(2024)ChildShield: An Implicit and Continuous Child Identification System on SmartphonesIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.330241421:4(2257-2272)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1109/TDSC.2023.3302414
Miao YTian QLin CSong TZhou YZhao JGao SYang MShen CElkind E(2023)Learning heuristically-selected and neurally-guided feature for age group recognition using unconstrained smartphone interactionProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/338(3029-3037)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/338
Guarino ALettieri NMalandrino DZaccagnino RCapo C(2022)Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devicesNeural Computing and Applications10.1007/s00521-022-07454-434:21(18473-18495)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00521-022-07454-4
Vatavu RAnthony LBrown Q(2022)Child or Adult? Inferring Smartphone Users’ Age Group from Touch Measurements AloneHuman-Computer Interaction – INTERACT 201510.1007/978-3-319-22723-8_1(1-9)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-319-22723-8_1
Zaccagnino RCapo CGuarino ALettieri NMalandrino D(2021)Techno-regulation and intelligent safeguardsMultimedia Tools and Applications10.1007/s11042-020-10446-y80:10(15803-15824)Online publication date: 6-Feb-2021
https://dl.acm.org/doi/10.1007/s11042-020-10446-y
Cheng YJi XLi XZhang TMalebary SQu XXu W(2020)Identifying Child Users via Touchscreen InteractionsACM Transactions on Sensor Networks10.1145/340357416:4(1-25)Online publication date: 28-Jul-2020
https://dl.acm.org/doi/10.1145/3403574
Woodward JCato JSmith JWang IBenda BAnthony LRuiz J(2020)Examining Fitts' and FFitts' Law Models for Children's Pointing Tasks on TouchscreensProceedings of the 2020 International Conference on Advanced Visual Interfaces10.1145/3399715.3399844(1-5)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3399715.3399844
Chen ZChen YShaw AAloba AAntonenko PRuiz JAnthony LTruong KHeylen DCzerwinski MBerthouze NChetouani MNakano M(2020)Examining the Link between Children's Cognitive Development and Touchscreen Interaction PatternsProceedings of the 2020 International Conference on Multimodal Interaction10.1145/3382507.3418841(635-639)Online publication date: 21-Oct-2020
https://dl.acm.org/doi/10.1145/3382507.3418841
Anthony LFails J(2019)Quantitative Methods for Child-Computer InteractionProceedings of the 18th ACM International Conference on Interaction Design and Children10.1145/3311927.3325166(710-713)Online publication date: 12-Jun-2019
https://dl.acm.org/doi/10.1145/3311927.3325166
Shukri SHowes A(2019)Children adapt drawing actions to their own motor variability and to the motivational context of actionInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2019.06.004130:C(152-165)Online publication date: 1-Oct-2019
https://dl.acm.org/doi/10.1016/j.ijhcs.2019.06.004
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