Leveraging Biometric Technology for Enhanced Learning Outcomes in Music Education: Implications for Biotechnological Applications

Sai Wang
Department of Environmental Arts, Hebei University of Environmental Engineering, Qinhuangdao, Hebei, 066102, China.
Jun Wu
Dean’s Office, Qinhuangdao Open University, Qinhuangdao, Hebei, 066000, China.

DOI:https://doi.org/10.5912/jcb2036

Abstract:

This paper explores the integration of multi-biometric identification technology into music education, highlighting its potential to enhance learning outcomes through personalized and secure interactions. A comparative analysis of various biometric features—stability, collectability, performance, user acceptance, and resistance to spoofing—guided the selection of multi-biometric technology as the cornerstone of a novel music art teaching system. This system uniquely combines the distinct aspects of music education with advanced biometric identity verification, resulting in a comprehensive architecture designed to support interactive learning environments. The effectiveness of the proposed system was rigorously tested across multiple dimensions: overall interface usability, software functionality, and biometric performance. Interface tests focused on usability rationality and consistency, while software functionality tests covered essential aspects such as personnel, attendance, and system management. The biometric performance, primarily measured through face and fingerprint recognition rates, demonstrated high accuracy with an average misrecognition rate for combined facial and static hand shape recognition at 1.85%, and overall system recognition rates exceeding 91.5%. These findings affirm that the integration of multi-biometric technologies not only secures the music teaching environment but also enhances the educational experience by ensuring efficient, personalized student engagement. The successful implementation and validation of this system suggest significant implications for biotechnological applications in educational settings, proposing a scalable model for other disciplines seeking similar enhancements.