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Vol.15, No.2, May 2026. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Technology for Learning a Sign Language in an Educational Environment: A Machine Learning-Based Approach
Javier Torres Rojas, Nelson Cueva Jaimes, Diego González Bardales, Sussy Bayona-Oré
© 2026 Sussy Bayona Oré, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 15, Issue 2, Pages 1707-1718, ISSN 2217-8309, DOI: 10.18421/TEM152-63, May 2026.
Received: 06 June 2025.
Abstract:
The integration of emerging technologies, particularly machine learning, into educational environments has opened new opportunities for inclusive learning. This study presents the design, implementation, and evaluation of a web-based sign language learning system powered by machine learning. Waterfall methodology was used to describe the development of web-based systems. The system is structured around three core dimensions: Expression, communication, and comprehension. A pre-experimental quantitative approach was employed with a sample of 145 primary school students in Lima, Peru. Data were collected using a validated Likert-scale questionnaire applied before and after the intervention. Statistical analysis using the Wilcoxon signed-rank test showed significant improvements across all dimensions. Specifically, the percentage of students who felt fully capable of expressing themselves in sign language rose from 2% to 49%, while similar gains were observed in communication and comprehension. These findings demonstrate the potential of machine learning-based tools to support foundational sign language learning in inclusive educational settings. The study contributes to the growing field of AI-enhanced education and highlights the importance of accessible technology for students with and without disabilities.
Keywords – Machine learning, sign language, convolutional neural networks, teaching sign language, inclusivity. |
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