engleski

Towards a Machine Learning Smart Toy Design for Early Childhood Geometry Education: Usability and Performance

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.

IoT ; smart toy ; machine learning ; early childhood education ; geometry ; usability ; human–computer interaction

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