Towards a Machine Learning Smart Toy Design for Early Childhood Geometry Education: Usability and Performance
Abstract
This study presents the design and evaluation of a plush smart toy prototype for teaching geometry shapes to young children. The hardware design involves the integration of sensors, microcontrollers, an LCD screen, and a machine learning algorithm to enable gesture recognition by the toy. The machine learning algorithm detects whether the child’s gesture outline matches the shape displayed on the LCD screen. A pilot study was conducted with 14 preschool children to assess the usability and performance of the smart toy. The results indicate that the smart toy is easy to use, engages children in learning, and has the potential to be an effective educational tool for preschool children. The findings suggest that smart toys with machine learning algorithms can be used to enhance young children’s learning experiences in a fun and engaging way. This study highlights the importance of designing user-friendly toys that support children’s learning and underscores the potential of machine learning algorithms in developing effective educational toys.
Type
Publication
Electronics, 12(8), 1951
Status
Peer-reviewed
Open access
Authors
Full Professor
I am a Full Professor in the
Department of Electronics and Computing
at the Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture (FESB), University of Split,
Croatia, where I teach
wireless network security, wireless communication networks, data science and
reinforcement learning.
My research spans information security, applied cryptography and the Internet of
Things. Before returning to FESB in 2020, I was an assistant and then associate
professor at the University Department of Forensic Sciences
in Split. I co-founded the SpliTech international
conference and serve on the editorial board of the
Journal of Communications Software and Systems.