TY - GEN
T1 - Expanding Elementary School Computer Science Education with an Introduction to Machine Learning Through Rhythmic Studies
AU - Hunter, Holly
AU - Payton, Jamie
AU - Julien, Christine
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/10/16
Y1 - 2023/10/16
N2 - Introducing elementary students to computer science and computational thinking (CS/CT) can enhance their problem solving skills and enhance their confidence and sense of belonging in computing. Project moveSMART aims to introduce learning activities into elementary classrooms that address computer science concepts in a way that integrates with core curriculum requirements and promotes physical activity. In this paper, we explore an extension to an initial set of Project moveSMART computer science learning activities to introduce elementary students to machine learning concepts in a way that is integrated with required learning objectives covered in a Physical Education course. Specifically, students use the BBC micro:bit and its on board sensors to capture rhythmic movement data, explore and analyze patterns in the data, and use a learned "dance move recognition"application that uses their data in order to learn about machine learning in an age appropriate way. To demonstrate feasibility of supporting dance move recognition on the resource-constrained device, we developed a prototype, which is able to detect 5 different dance moves with a 96.6% accuracy.
AB - Introducing elementary students to computer science and computational thinking (CS/CT) can enhance their problem solving skills and enhance their confidence and sense of belonging in computing. Project moveSMART aims to introduce learning activities into elementary classrooms that address computer science concepts in a way that integrates with core curriculum requirements and promotes physical activity. In this paper, we explore an extension to an initial set of Project moveSMART computer science learning activities to introduce elementary students to machine learning concepts in a way that is integrated with required learning objectives covered in a Physical Education course. Specifically, students use the BBC micro:bit and its on board sensors to capture rhythmic movement data, explore and analyze patterns in the data, and use a learned "dance move recognition"application that uses their data in order to learn about machine learning in an age appropriate way. To demonstrate feasibility of supporting dance move recognition on the resource-constrained device, we developed a prototype, which is able to detect 5 different dance moves with a 96.6% accuracy.
KW - activity recognition
KW - broadening participation in computing
KW - computer science education
KW - smart health and well-being
KW - wearable computing
UR - https://www.scopus.com/pages/publications/85176141825
UR - https://www.scopus.com/pages/publications/85176141825#tab=citedBy
U2 - 10.1145/3565287.3617623
DO - 10.1145/3565287.3617623
M3 - Conference contribution
AN - SCOPUS:85176141825
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 463
EP - 467
BT - MobiHoc 2023 - Proceedings of the 2023 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
PB - Association for Computing Machinery
T2 - 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2007
Y2 - 23 October 2023 through 26 October 2023
ER -