Fostering a Post-Digital Avant-Garde: Research-led teaching of music technology

Ferguson, John R.; Brown, Andrew R.

doi:10.1017/s1355771816000054

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…According to Biencinto et al, since it is difficult to compare the teaching of different disciplines, courses of different kinds, teaching links, and teaching objects, the consideration of the instructional quality assessment system is based primarily on the instructional quality that can visibly reflect the instructional quality and share characteristics. The most fundamental components are designed [ 9 , 10 ]. Ma put forth an index system that is suitable for instructional assessment and proposed a data processing method that is standardised for index system data.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Lan

Fan²

2022

Journal of Environmental and Public Health

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In order to speed up the process of high-quality education and improve the level of education quality among the general public, people have pushed for the use of music art education in recent years. In this respect, this study covers the CNN-based assessment of the quality of music art teaching and creates a set of evaluation indices for that quality. The model architecture, network topology, learning parameters, and learning algorithm are all determined using this information, which also acts as the basis for the NN assessment model. The MATLAB simulation tool uses the CNN assessment model to train and learn a predetermined quantity of instructional quality data. The training experiment shows that this system can outperform other comparative systems in prediction accuracy by roughly 95%. Additionally, both the training and prediction accuracy of the model are completely acceptable. The evaluation findings and analytical data of the music art instructional quality assessment system created in this study can be used as a guide for determining the music art instructional quality and for making judgments regarding it.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

Lan

Fan²

2022

Journal of Environmental and Public Health

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“…In order to improve the quality of music teaching in the institution of higher learning, Ferguson J R and others put forward suggestions for effectively relocating and integrating music instructional resources, and discussed from three aspects: clarifying educational goals, integrating instructional resources, and innovating college music instructional models [ 8 ]. Harrison expounds the concept, types, and characteristics of online music education resources, then presents the results of the online questionnaire survey on both teaching and learning, and mainly analyzes the demand trends of online music education resource service objects [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

Lei

2022

Journal of Environmental and Public Health

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In order to overcome the problem that learners and teachers cannot find instructional resources to meet their needs and information overload in the massive resources, this article proposes and designs a music instructional resource management platform based on DCNN. This article expounds the overall goal, design principle, overall structure, and interface design of the system. At the same time, the whole construction process of a music instructional resources integration system based on DCNN is discussed in detail from the aspects of configuration of development environment, localization of platform interface, and realization of main functions of the system. In addition, through the demand analysis tool, the demand of college music instructional resources management is analyzed in detail and deeply, and the demand document is formed. This article makes an in-depth study on the categories of music instructional resources and summarizes the resource classification methods that are in line with the actual instructional activities. The experiments show that the accuracy of the proposed algorithm is improved by about 6% compared with the fuzzy clustering algorithm. At the same time, the stability of this system can reach 96.14%. This system is rich in functions and easy to use and can provide a feasible scheme for the management of instructional resources in various disciplines.

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“…Computer technology is an assistant means for music teaching, and it is also a medium of communication between teachers and students during music teaching [25,26]. If teachers could apply information technology based on actual teaching conditions, it'll greatly improve the quality of music teaching [27,28].…”

Section: Countermeasures For the Application Of Computer Technology In Music Classmentioning

confidence: 99%

A New Music Teaching Mode Based on Computer Automatic Matching Technology

Zhang

Ding

2021

Int. J. Emerg. Technol. Learn.

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The rapidly developing computer technology has been extensively applied in music teaching. The computer automatic matching technology supports the automatic generation of randomized teaching contents, providing a good tool to develop the musical thinking of students. This paper tentatively introduces this technology to music teaching, and derives a new music teaching mode. The results show that, computer technology can effectively assist in music teaching; the inclusion of computer technology in music teaching arouses students’ interest in music activities; the application of computer automatic matching technology has improved students’ professional skills, live music performance, as well as music ability. The research results greatly promote the reform of music teaching modes and methods.

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Fostering a Post-Digital Avant-Garde: Research-led teaching of music technology

Cited by 12 publications

References 20 publications

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

[Retracted] Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

A New Music Teaching Mode Based on Computer Automatic Matching Technology

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