A Quasi-Experimental Study on the Application of MuseScore Software to Improve Sight-Singing and Ear-Training Abilities in Music Education at Chuzhou University
Article Sidebar
Main Article Content
Abstract
Background and Aim: Traditional sight-singing and ear-training methods rely primarily on auditory perception, limiting visual engagement and interactive learning. Using MuseScore software can not only solve these limitations but also provide a variety of new learning experiences. This study aimed to explore whether using MuseScore software in sight-singing and ear-training classroom teaching can affect students' performance in rhythm accuracy, pitch accuracy, and melodic dictation.
Materials and Methods: This study is a quasi-experimental study using quantitative research methods. The participants were 120 freshmen from four music major classes at Chuzhou University in Anhui Province. The sample size is 60, and the duration of the experiment is eight weeks. The 30 students in the control group adopted traditional teaching methods; the 30 students in the experimental group used MuseScore software for sight-singing and ear-training teaching. Through eight weeks of teaching from March to May 2024, the pre-test was completed before the first week, and the two groups were post-tested in the ninth week to collect students' scores in rhythm accuracy, pitch accuracy, and melodic dictation. Jamovi software was used to perform an independent sample t-test analysis on the test scores.
Results: The experimental group of students who used the MuseScore software for teaching had higher scores in rhythm accuracy, pitch accuracy, and melodic dictation than the control group, especially in melodic dictation. A significant difference in the melodic dictation scores between the two groups in the post-test, t (58) = -3.04, p< 0.01, 95% confidence interval [-3.43, -0.70], Cohen’s d = -0.78, with a higher effect.
Conclusion: The study's results showed that the use of MuseScore software was effective in sight-singing and ear-training skills. Students who used MuseScore software showed significant improvements in pitch identification, rhythm perception, and musical memory. These results directly support the study objectives. For music educators, these findings mean that they can use MuseScore software to enrich their teaching methods. It is recommended that future research should further expand the population using MuseScore software and develop new features.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright on any article in the International Journal of Sociologies and Anthropologies Science Reviews is retained by the author(s) under the under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Permission to use text, content, images, etc. of publication. Any user to read, download, copy, distribute, print, search, or link to the full texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose. But do not use it for commercial use or with the intent to benefit any business.
References
Ayderova, V. (2021). Music notation software as a visual-aural model in self-regulated practice. Social Sciences, 11(12), 344–360.
Bai, J. (2020). The combination of sight-singing and ear-training teaching and digital technology: Research. Master’s thesis, Hebei Normal University.
Bauer, W. I. (2020). Music learning today: Digital pedagogy for creating, performing, and responding to music (2nd ed.). Oxford University Press.
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.
Dammers, R., & LoPresti, M. (2020). Practical music education technology. Oxford University Press.
Dorfman, J. (2022). Theory and practice of technology-based music instruction (2nd ed.). Oxford University Press.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
Gao, S. (2022). The application of digital teaching methods in sight-singing and ear-training in higher normal education: A study. Master’s thesis, Northeast Normal University.
Gillespie, J. L. (2001). Melodic dictation scoring methods: An exploratory study. Journal of Music Theory Pedagogy, 15(2), 51–68.
Gordon, E. (2007). Learning sequences in music: A contemporary music learning theory. GIA Publications.
Henry, M. L. (2011). The effect of pitch and rhythm difficulty on vocal sight-reading performance. Journal of Research in Music Education, 59(1), 72–84. https://doi.org/10.1177/0022429410397199
Karpinski, G. S. (2000). Aural skills acquisition: The development of listening, reading, and performing skills in college-level musicians. Oxford University Press.
Mayer, R. E. (2005). Principles of multimedia learning based on social cues: Personalization, voice, and image principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 201–212). Cambridge University Press.
Miksza, P., & Elpus, K. (2018). Design and analysis for quantitative research in music education. Oxford University Press.
Mona, D., & Hidayat, H. A. (2021). Utilization of Sibelius software in basic vocals education in the Department of Sendratasik, Universitas Negeri Padang. In Ninth International Conference on Language and Arts (ICLA 2020) (pp. 354–358). Atlantis Press.
Nair, S. M., You, J., Krishnasamy, H. N., Singh, G. K. S., & Siddique, M. (2024). Using Overture to enhance students’ aural musical skills and motivation in learning. Edelweiss Applied Science and Technology, 8(4), 1966–1978.
Norman, T. D. (2023). Free music notation software and web-based applications. Journal of General Music Education, 36(3), 51–52. https://doi.org/10.1177/27527646231163852
Owens, P., & Sweller, J. (2008). Cognitive load theory and music instruction. Educational Psychology, 28(1), 29–45. https://doi.org/10.1080/01443410701413716
Peterson, H. (2006). Technology tips and tricks for music educators. General Music Today, 19(3), 36–43. https://doi.org/10.1177/10483713060190030110
Price, S. T. (2010). The application of cognitive load theory to teaching music reading. Doctoral dissertation, University of Georgia.
Schüler, N. (2021). Modern approaches to teaching sight-singing and ear-training. Facta Universitatis, Series: Visual Arts and Music, 7(1), 83–92.
Shinn, M. (2013). Instant MuseScore. Packt Publishing.
Sweller, J. (2011). Cognitive load theory. Springer.
Todea, D. (2015). The use of the MuseScore software in musical e-learning. Virtual Learn, 88–94.
Watson, M. (2018). MuseScore. Journal of the Musical Arts in Africa, 15(1–2), 143–147. https://doi.org/10.2989/18121004.2018.1534342
Xia, L. (2012). Three ordinary college solfeggio teaching present situation. Master’s thesis, Central China Normal University.
Yu, Q. (2011). Application research of software Auralia and Overture in the teaching of solfeggio and ear training. Master’s thesis, Hunan Normal University.
