| Abstract: |
Nowadays, we have the ease of access to music teaching through multiple formats, from traditional classes to novel Extended Reality (XR) applications [1]. Multiple factors have impact on the final performance in the teaching effectiveness of all computer supported piano teaching, from the accuracy of the different technologies that can be used for detecting the pressed keys, hands posture [10], pedaling [9], or fingering among others, based on audio recognition [7], artificial vision [8]. A usually underestimated dimension is that of the user interface design (UI). To the best of our knowledge, there is no study that investigates in depth the influence of UI on music learning tools, just some aspects such as the evaluation of the feedback [5], and specifically, how the intrinsically error prone recognition algorithms may confuse the piano student. Our emerging project researches the influence of the design of the user interface in the context of music education assisted in Extended Reality (XR) environments. To evaluate the effectiveness and satisfaction of the piano learning experience from a musical perspective [6], previous approaches will be extended [1] by adapting specific metrics for gamification of educational content [11], and other music tasks adapted to evaluation purposes [4]. In addition, criteria from the field of UX/UI design, with a particular focus on XR[2]. All of this will be accomplished by applying the principles of semiotics in the design [3]. REFERENCES [1] Banquiero, M., Valdeolivas, G., Trincado, S., García, N., and Juan, M.-C. Passthrough mixed reality with Oculus Quest 2: A case study on learning piano. IEEE MultiMedia 30, 2 (April 2023) [2] Becker, A., and Freitas, C. M. D. S. Evaluation of XR applications: A tertiary review. ACM Comput. Surv. (nov 2023). [3] Islam, M. N., and Bouwman, H. Towards user–intuitive web interface sign design and evaluation: A semiotic framework. International Journal of Human-Computer Studies 86 (2016). [4] Jeong, D., Kwon, T., Kim, Y., and Nam, J. Graph neural network for music score data and modeling expressive piano performance. In Proceedings of the 36th ICML (Jun 2019). [5] Jiang, Y. Expert and novice evaluations of piano performances: Criteria for computer-aided feedback. (ISMIR) 2023 Hybrid Conference (2023). [6] Kaleli, Y. The effect of computer-assisted instruction on piano education: An experimental study with pre-service music teachers. International Journal of Technology in Education and Science 4 (Jun 2020) [7] Kong, Q., Li, B., Song, X., Wan, Y., and Wang, Y. High-resolution piano transcription with pedals by regressing onset and offset times. IEEE/ACM Transactions on Audio, Speech, and Language Processing (2021). [8] Lee, J., Doosti, B., Gu, Y., Cartledge, D., Crandall, D., and Raphael, C. Observing pianist accuracy and form with computer vision. In 2019 IEEE WACV (2019). [9] Liang, B., Fazekas, G., Mcpherson, A., and Sandler, M. B. Piano pedaller: a measurement system for classification and visualisation of piano pedalling techniques. In NIME (2017). [10] Liu, R., Wu, E., Liao, C.-C., Nishioka, H., Furuya, S., and Koike, H. Pianohandsync: An alignment-based hand pose discrepancy visualization system for piano learning. 2023 Conference on Human Factors in Computing Systems (New York, 2023) [11] Micheloni, E., Tramarin, M., Rodà, A., and Chiaravalli, F. Playing to play: a piano-based user interface for music education video-games. Multimedia Tools Appl (may 2019). |