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Promotion of Folk Music Ensemble to Reform of Music Courses in Undergraduate Colleges

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DOI: 10.23977/curtm.2023.060220 | Downloads: 12 | Views: 367

Author(s)

Shi Ke 1,2

Affiliation(s)

1 School of Music, Jining Normal University, Wulanchabu, Inner Mongolia, China
2 Philippine Christian University Center for International Education, Manila, Philippine

Corresponding Author

Shi Ke

ABSTRACT

Folk music ensembles combine music theory, music practice, and music aesthetics to form a framework for enhancing students' musical thinking, provide a framework for practical learning, and are important for improving students' overall quality, professional skills, and training in the spirit of teamwork. The purpose of this paper is to examine folk music ensembles as a catalyst for reforming music curriculum instruction in undergraduate institutions. Understanding the current state of research on school folk ensembles and school cultural heritage, as well as studying and understanding curriculum standards, traditional Chinese music aesthetics, folk instrumental music, and other related theories and excellent research results, serve as the basic theoretical foundation and factual basis for this study. Through research on various aspects of the music curriculum development, performance, teaching process and evaluation strategies at the University of M, to understand the students' learning of music, their needs for music and their awareness of the curriculum reform, the experimental results suggest that folk music ensembles should be strengthened to promote the implementation of the music curriculum teaching reform in undergraduate institutions.

KEYWORDS

Folk Music Ensemble, Undergraduate Institutions, Music Curriculum, Teaching Reform

CITE THIS PAPER

Shi Ke, Promotion of Folk Music Ensemble to Reform of Music Courses in Undergraduate Colleges. Curriculum and Teaching Methodology (2023) Vol. 6: 141-146. DOI: http://dx.doi.org/10.23977/curtm.2023.060220.

REFERENCES

[1] Diana Omigie, Marcus T. Pearce, Katia Lehongre, Dominique Hasboun, Vincent Navarro, Claude Adam, Séverine Samson. Intracranial Recordings and Computational Modeling of Music Reveal the Time Course of Prediction Error Signaling in Frontal and Temporal Cortices. J. Cogn. Neurosci. 31(6): 855-873 (2019)
[2] Manuel J. Espigares-Pinazo, José M. Bautista-Vallejo, Marina García-Carmona. Evaluations in the Moodle-Mediated Music Teaching-Learning Environment. Technol. Knowl. Learn. 27(1): 17-31 (2022)
[3] Juhan Nam, Keunwoo Choi, Jongpil Lee, Szu-Yu Chou, Yi-Hsuan Yang. Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach. IEEE Signal Process. Mag. 36(1): 41-51 (2019)
[4] Muhammed Kuliya, Sani Usman. Perceptions of E-learning among undergraduates and academic staff of higher educational institutions in north-eastern Nigeria. Educ. Inf. Technol. 26(2): 1787-1811 (2021)
[5] Ayesha Bhimdiwala, Rebecca Colina Neri, Louis M. Gomez. Advancing the Design and Implementation of Artificial Intelligence in Education through Continuous Improvement. Int. J. Artif. Intell. Educ. 32(3): 756-782 (2022)
[6] Irene-Angelica Chounta, Emanuele Bardone, Aet Raudsep, Margus Pedaste. Exploring Teachers' Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education. Int. J. Artif. Intell. Educ. 32(3): 725-755 (2022)
[7] Ali Darvishi, Hassan Khosravi, Shazia W. Sadiq, Barbara Weber. Neurophysiological Measurements in Higher Education: A Systematic Literature Review. Int. J. Artif. Intell. Educ. 32(2): 413-453 (2022)
[8] Shamya Karumbaiah, Jaclyn Ocumpaugh, Ryan S. Baker. Context Matters: Differing Implications of Motivation and Help-Seeking in Educational Technology. Int. J. Artif. Intell. Educ. 32(3): 685-724 (2022)
[9] Radek Pelánek. Adaptive, Intelligent, and Personalized: Navigating the Terminological Maze behind Educational Technology. Int. J. Artif. Intell. Educ. 32(1): 151-173 (2022)
[10] Daniel Schiff: Education for AI, not AI for Education. The Role of Education and Ethics in National AI Policy Strategies. Int. J. Artif. Intell. Educ. 32(3): 527-563 (2022)
[11] Selin Akgün, Christine Greenhow. Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics 2(3): 431-440 (2022)
[12] Muhammad Ali Chaudhry, Emre Kazim. Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021. AI Ethics 2(1): 157-165 (2022)
[13] Jon Dron. Educational technology: what it is and how it works. AI Soc. 37(1): 155-166 (2022)
[14] Andreas Kakouris, Eleni Sfakianaki, Marios Tsioufis. Lean thinking in lean times for education. Ann. Oper. Res. 316(1): 657-697 (2022)

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