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Training Effects and Intelligent Evaluated Pattern of the Holistic Music Educational Approach for Children with Developmental Delay

Author

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  • Liza Lee

    (Department of Early Childhood Development and Education, Chaoyang University of Technology, Taichung 413310, Taiwan
    College of Humanities and Social Sciences, Chaoyang University of Technology, Taichung 413310, Taiwan)

  • Ying-Sing Liu

    (College of Humanities and Social Sciences, Chaoyang University of Technology, Taichung 413310, Taiwan)

Abstract

This study focuses on the holistic music educational approach for young children developed by music therapists and experts, which combines technology with music, integrates it into the local culture, and frameworks it for holistic education. This method includes 231 children in Taiwan’s early intervention education system for children with developmental delay. The learning occurs twice a week for 40 min over 32 weeks. The results show that implementing the holistic music educational approach can significantly improve children’s ability with developmental delay and that supportive training has a positive effect. In addition, the decision tree explores and develops an intelligently evaluated pattern with highly effective learning. This model has a sensitivity rate of 90.6% on the in-sample, and the comprehensive indicator F is 79.9%, so it has a high reference value. In the future, those involved in education will be able to use the data mining to use the auxiliary decision-making system as an assessment tool for young children participating in education pre- and midterm of the course, to prejudge its continued implementation and learning effectiveness, to decide whether to continue to invest in and adjust the curriculum, and to make more effective use of educational resources.

Suggested Citation

  • Liza Lee & Ying-Sing Liu, 2021. "Training Effects and Intelligent Evaluated Pattern of the Holistic Music Educational Approach for Children with Developmental Delay," IJERPH, MDPI, vol. 18(19), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:10064-:d:642689
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    References listed on IDEAS

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    1. Andrew Peplow & Justin Thomas & Aamna AlShehhi, 2021. "Noise Annoyance in the UAE: A Twitter Case Study via a Data-Mining Approach," IJERPH, MDPI, vol. 18(4), pages 1-9, February.
    2. Chenyu Zhang & Jiayue Jiang & Hong Jin & Tinggui Chen, 2021. "The Impact of COVID-19 on Consumers’ Psychological Behavior Based on Data Mining for Online User Comments in the Catering Industry in China," IJERPH, MDPI, vol. 18(8), pages 1-19, April.
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    Cited by:

    1. Liza Lee, 2023. "Expectations and Effectiveness of Preschool Teacher Training Program: A Case Study of Teacher Training Course for the Holistic Music Educational Approach for Young Children," SAGE Open, , vol. 13(4), pages 21582440231, December.
    2. Liza Lee & Ying-Sing Liu, 2023. "The Learning Outcomes of Figurenotes Music Activities for Children With Special Needs Based on the ARMA Models," SAGE Open, , vol. 13(4), pages 21582440231, December.

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