An AIoT-Based Interactive Literacy Game Using Micro:bit and Ensemble k-NN–Decision Tree for STEAM Learning

Hayatunnufus; Nirmala Aizya Agatha Silalahi

doi:10.32734/jocai.v10.i2-25800

Authors

Hayatunnufus University of Sumatera Utara https://orcid.org/0000-0003-2883-6179
Nirmala Aizya Agatha Silalahi University of Sumatera Utara

DOI:

https://doi.org/10.32734/jocai.v10.i2-25800

Keywords:

Literacy, Microbit, AIoT, Machine Learning

Abstract

Low literacy performance among Indonesian students, as indicated by PISA result, highlights the need for innovative learning approaches. This study proposes an interactive literacy game integrating Micro:bit and machine learning within an AIoT framework for STEAM education. The system uses accelerometer data to recognize hand gestures (left, right, up) as multiple-choice answers. A dataset of 61,148 gesture samples was collected and processed. An ensemble model combining k-Nearest Neighbor (k-NN) and Decision Tree algorithms achieved a classification accuracy of 90.25%. Real-time implementation with elementary students (n=19) yielded a gesture recognition accuracy of 95.03%. User testing showed high engagement (8.5/10) and positive learning impact (8.9/10), demonstrating the system’s effectiveness as a lightweight, interactive tool for literacy education.

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