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ARtonomous: Introducing Middle School Students to Reinforcement Learning Through Virtual Robotics

Authors:

Jennifer King Chen,

Matthew Tarrow,

Adriana Hilliard,

R. Benjamin ShapiroAuthors Info & Claims

IDC '22: Proceedings of the 21st Annual ACM Interaction Design and Children Conference

Pages 430 - 441

https://doi.org/10.1145/3501712.3529736

Published: 27 June 2022 Publication History

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Abstract

Typical educational robotics approaches rely on imperative programming for robot navigation. However, with the increasing presence of AI in everyday life, these approaches miss an opportunity to introduce machine learning (ML) techniques grounded in an authentic and engaging learning context. Furthermore, the needs for costly specialized equipment and ample physical space are barriers that limit access to robotics experiences for all learners. We propose ARtonomous, a relatively low-cost, virtual alternative to physical, programming-only robotics kits. With ARtonomous, students employ reinforcement learning (RL) alongside code to train and customize virtual autonomous robotic vehicles. Through a study evaluating ARtonomous, we found that middle-school students developed an understanding of RL, reported high levels of engagement, and demonstrated curiosity for learning more about ML. This research demonstrates the feasibility of an approach like ARtonomous for 1) eliminating barriers to robotics education and 2) promoting student learning and interest in RL and ML.

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Cited By

Morales-Navarro LKafai Y(2024)Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design ActivitiesProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678117(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678117
Morales-Navarro LKafai YKonda VMetaxa D(2024)Youth as Peer Auditors: Engaging Teenagers with Algorithm Auditing of Machine Learning ApplicationsProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655752(560-573)Online publication date: 17-Jun-2024
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Published In

cover image ACM Conferences

IDC '22: Proceedings of the 21st Annual ACM Interaction Design and Children Conference

June 2022

718 pages

ISBN:9781450391979

DOI:10.1145/3501712

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 27 June 2022

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IDC '22

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IDC '22: Interaction Design and Children

June 27 - 30, 2022

Braga, Portugal

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Overall Acceptance Rate 172 of 578 submissions, 30%

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Cited By

Morales-Navarro LKafai Y(2024)Unpacking Approaches to Learning and Teaching Machine Learning in K-12 Education: Transparency, Ethics, and Design ActivitiesProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3678117(1-10)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3678117
Morales-Navarro LKafai YKonda VMetaxa D(2024)Youth as Peer Auditors: Engaging Teenagers with Algorithm Auditing of Machine Learning ApplicationsProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655752(560-573)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3655752
Mack NAdeleke MIngram VBallou EBriggs-Belt JJordan A(2024)CodeBears: Key Insights Gained from a Summer Coding Camp Empowering Underrepresented Youth2024 Black Issues in Computing Education (BICE)10.1109/BICE60192.2024.00021(80-86)Online publication date: 1-Feb-2024
https://doi.org/10.1109/BICE60192.2024.00021
Wang B(2024)Domain Adaptation in Reinforcement Learning: Approaches, Limitations, and Future DirectionsJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01049-4105:5(1223-1240)Online publication date: 6-Apr-2024
https://doi.org/10.1007/s40031-024-01049-4
Karalekas GVologiannidis SKalomiros J(2023)Teaching Machine Learning in K–12 Using RoboticsEducation Sciences10.3390/educsci1301006713:1(67)Online publication date: 10-Jan-2023
https://doi.org/10.3390/educsci13010067
Ruppert JVelazquez-Ramos DRoque RShapiro R(2023)Taking play and tinkering seriously in AI education: cases from Drag vs AI teen workshopsLearning, Media and Technology10.1080/17439884.2022.216430049:2(259-273)Online publication date: 5-Jan-2023
https://doi.org/10.1080/17439884.2022.2164300
Parsonage GHorton MRead J(2023)The Peer Data Labelling System (PDLS). A Participatory Approach to Classifying Engagement in the ClassroomHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42283-6_13(224-233)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-42283-6_13
Zhang ZLavigne KChurch WSinapov JRogers C(2023)Introducing Reinforcement Learning to K-12 Students with Robots and Augmented RealityRobotics in Education10.1007/978-3-031-38454-7_29(351-365)Online publication date: 4-Oct-2023
https://doi.org/10.1007/978-3-031-38454-7_29
Plasencia-Salgueiro A(2023)Deep Reinforcement Learning for Autonomous Mobile Robot NavigationArtificial Intelligence for Robotics and Autonomous Systems Applications10.1007/978-3-031-28715-2_7(195-237)Online publication date: 16-May-2023
https://doi.org/10.1007/978-3-031-28715-2_7
Mahon JBecker BNamee B(2023)AI and ML in School Level Computing Education: Who, What and Where?Artificial Intelligence and Cognitive Science10.1007/978-3-031-26438-2_16(201-213)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26438-2_16

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