The Construction of Online Course Learning Model of Piano Education from the Perspective of Deep Learning

Huang, Nansong; Ding, Xiangxiang

doi:10.1155/2022/4378883

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…The training system is based on the use of a recommendation for online lessons. This approach to learning improves the efficiency of the educational process and influences the acquisition of musical skills (Huang & Ding, 2022). Listening skills can be developed through the use of the Listen By Looking app, which is based on the use of a game format during instruction.…”

Section: Resultsmentioning

confidence: 99%

The role of a teacher in modern music education: can a student learn music with the help of modernized online educational technologies without teachers?

Yao

2023

Educ Inf Technol

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Music education is aimed at the development of musical abilities, emotionality during the performance of musical compositions, and all-round development. The aim of the article is to determine the possibilities of schoolchildren obtaining musical knowledge with the help of modernized online technologies, as well as to determine the importance of a teacher in modern music education. The indicators were determined by a questionnaire, the data collection for which was done using a Likert scale. Initially, the paper identified approaches to teaching students prior to the study. The results showed that more emphasis was placed on the use of book-based theoretical materials (46%), which allowed only 21% of students to achieve a high level of knowledge. The use of information technology was common among 9% of students, which allowed 76% of students to achieve high results, which is based on acquiring knowledge in a shorter time. The authors concluded that it is necessary to provide improved stages of learning, which will expand the use of modernized technology. Work on the theoretical basics of piano playing can be implemented with the Vivace app; Flow app helps one to work on sound features; Functional Ear Trainer app is about developing rhythm and hearing; and Chordana Play app helps one to work on a piece of music. After training, by calculating the coefficient of effectiveness, it was found that students in group #1 (0.791), who mastered the skills of playing the piano according to the developed training stages and without a teacher, showed the quality of acquired knowledge lower than students in group #2 (0.853), who were trained under the direct influence of a teacher. The data obtained confirm the high quality of learning in the groups, as the educational process was built with the correct distribution of workload and providing opportunities for the development of musical skills. It is established that the students of group 1 to a greater extent developed independence (29%), and group 2 was good at the accuracy of musical tasks’ sequence (28%). The practical significance of the work is based on the possibility of transforming the music learning system with the help of modern technology. Prospects for the study can be based on a comparison of the quality of teaching the piano and vocal training, excluding the participation of a teacher in the learning process.

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Section: Resultsmentioning

confidence: 99%

The role of a teacher in modern music education: can a student learn music with the help of modernized online educational technologies without teachers?

Yao

2023

Educ Inf Technol

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“…e preprocessed data are fed into the model training. e model parameters are adjusted in real time through the UI provided by Deeplearning4J, and the model parameters with better evaluation effects are obtained [23].…”

Section: Model Construction Of Piano Performance Evaluation Based On Rnnmentioning

confidence: 99%

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Liao

2022

Mobile Information Systems

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In recent years, the piano education industry has occupied a huge market. However, the automatic evaluation function of piano performance has shortcomings in existing piano education products. Deep learning (DL) algorithm and the recurrent neural network (RNN) structure can help in automatics evaluation function of the piano performance. This paper proposes a Musical Instrument Digital Interface (MIDI) piano evaluation scheme based on the RNN structure and the Spark computing engine using the Deeplearning4J DL framework. The Deeplearning4J framework can run on the Java Virtual Machine; therefore, the entire system does not require cross-platform development. The Spark distributed computing engine realizes parallelization in music data preprocessing, feature extraction, and model training. Combined with the training user interface (UI) provided by the Deeplearning4J, it can improve developmental efficiency. Additionally, the RNN parameters are analyzed. The results demonstrate that the error value of the three-layer RNN structure is smaller than other closest rivals’ techniques. In particular, few piano training institutions and MIDI website datasets are used as the basis, and the experimental samples are collected. The neural network is trained, and the performance of the evaluation model is tested. The results show that the evaluation outcomes of the designed performance evaluation model for the piano are fundamentally consistent with the real levels of the players with assured feasibility; after 3k times of the training periods, the error of the RNN model is close to 0.01 and the network converges.

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“…Xiao et al [23] proposed a personalized recommendation system with an online learning combination algorithm by combining association rules, content filtering, and collaborative filtering. Huang and Ding [9] used the K-means clustering algorithm to apply temporal data to a neural collaborative filtering algorithm to provide better, dynamic, and personalized online course recommendations for piano education. Ren et al [18] proposed a deep course recommendation model with multimodal feature extraction based on the long short-term memory network (LSTM) and attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

Online course recommendation algorithm based on multilevel fusion of user features and item features

Wang

et al. 2022

Comp Applic In Engineering

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To meet the demand for accurate recommendation and personalized learning in online education, an online course recommendation algorithm with multilevel fusion of user features and item features is proposed for contentbased recommendation systems applied to online education courses that have weak generalization ability and cannot cope well with data sparsity. The algorithm is improved on the deep learning recommendation algorithm named Wide & Deep Learning (WDL), which is a CTR (Click-through rate prediction) prediction algorithm. MMF (Multi-level Fusion Feature) uses collaborative filtering to replace linear methods in the wide part of WDL, introduces feature interactions to model user representations and item representations separately, and uses ResNet (Residual Network) ideas to improve deep neural network (DNN) in the deep part to reduce the performance degradation caused by overfitting. The experimental validation was conducted on the online education data set and the public data setMovieLens-1M, and the AUC was improved by 1.21% and 1.46%, respectively. Meanwhile, the effect brought by this improved algorithm is interpretable.

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The Construction of Online Course Learning Model of Piano Education from the Perspective of Deep Learning

Cited by 5 publications

References 27 publications

The role of a teacher in modern music education: can a student learn music with the help of modernized online educational technologies without teachers?

The role of a teacher in modern music education: can a student learn music with the help of modernized online educational technologies without teachers?

Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Online course recommendation algorithm based on multilevel fusion of user features and item features

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