Exploring Key Competencies and Professional Development of Music Teachers in Primary Schools in the Era of Artificial Intelligence

Zhao, Xian; Guo, Zhongling; Liu, Shanqin

doi:10.1155/2021/5097003

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…Then, the minimum sum of the maximum number of D nm can be found by ing formula (14) with formula (15). In the combinatory optimization of index data correlations, it is a must to ensure that n∈N DA , and m∈P DA , and that, in d i1j1 and d i2j2 , i 1 is not equal to i 2 , and j 1 is not equal to j 2 .…”

Section: Data Correlation Modelmentioning

confidence: 99%

“…With the dawn of the new era, college teachers' professional literacy becomes increasingly diverse and complex. The PD of college teachers is significantly affected by their teaching and scientific research (TSR) ability [10][11][12][13][14][15][16]. College teachers must have an educational concept compatible with the reform and development of higher education, boast good classroom teaching ability and TSR ability, and support PD with these abilities [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Correlations Between Teaching and Scientific Research Ability and Professional Development of College Teachers

Zhou¹

2022

Int. J. Emerg. Technol. Learn.

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In the new era, the professional development (PD) of college teachers is significantly affected by their teaching and scientific research (TSR) ability. With a high TSR ability, teachers can enhance their professional literacy, explore the student behaviors in teaching practice, and optimize and improve the teaching mode. The research of college teachers’ TSR ability has just started, lacking systematic, in-depth exploration. There is no report on the TSR subjects, or the effect of TSR ability on PD, not to mention empirical analysis of the correlations between TSR ability and PD. Therefore, this paper probes into the correlations between the TSR ability and PD of college teachers. Firstly, the data correlation principles were introduced for TSR ability and PD of college teachers, and an evaluation index system (EIS) was established for college teachers’ TSR ability. Then, the scores of college teachers’ TSR ability in a region were associated with the scores of college teachers’ PD. On this basis, the nearest neighbor algorithm and the joint compatibility branch and bound (JCBB) algorithm were combined to derive an improved index data correlation model. Through experiments, the proposed model was proved effective, and the regional analysis results were obtained.

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Section: Data Correlation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlations Between Teaching and Scientific Research Ability and Professional Development of College Teachers

Zhou¹

2022

Int. J. Emerg. Technol. Learn.

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“…e quality of music teaching is the lifeline of music education and the key to the sustainable and healthy development of music institutions of higher learning. For a long time, the assessment of music teaching quality is mostly based on mathematical models of evaluation systems, such as hierarchical analysis, fuzzy comprehensive evaluation method, gray system, cluster analysis and so on [11]. ese methods can fully consider various evaluation factors and reflect experts' empirical knowledge, but these methods can hardly exclude various randomness and subjectivity in the evaluation process, ignore the nonlinear relationship between each evaluation index and teaching effect, and the results are highly subjective, which is difficult to truly reflect the teaching quality condition.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Machine Learning-Based Evaluation for University Music Teaching

Xia

2022

Mathematical Problems in Engineering

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Teaching evaluation (TE) is an operational means of music education and management, a value judgment on university-related music teaching activities, with the fundamental purpose of promoting and improving the overall development of the cultivated subjects. With the deepening of quality education, the types and contents of music teaching evaluation (MTE) are being updated and changed, and many universities are gradually introducing music classroom teaching quality evaluation systems. Teaching quality evaluation plays an important role in the development of music teaching and affects the quality of educational evaluation, and is an important guarantee for the continuous reform and development of university music teaching (UMT), as well as an important means for scientific and reasonable management of schools, which plays an irreplaceable role in the development of schools. The evaluation of the quality of university music classroom teaching is a systematic process of analyzing and evaluating the teaching programs, teaching effects, and teaching processes of the music classroom within the school, which is conducive to improving the quality of university music classroom teaching. This article will focus on the overview of university music classroom teaching quality evaluation, analyze its main problems and causes, develop effective improvement measures to solve them, and build a machine learning (ML)-based UMT quality evaluation system. The model quantifies the concept of MTE indexes into definite data as the input of the network, and the comprehensive evaluation results as the output. The method overcomes the subjective factors of the evaluation subject in the evaluation process, but also obtains satisfactory evaluation results with wide applicability.

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“…Because of the unique nature of music education, it is one of the most significant forms of aesthetic instruction. Music classes are more significant in strengthening students' aesthetic sensibility than in helping them to appreciate excellent musical works and master basic knowledge and skills [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence Technology in Music Education Supported by Wireless Network

Jiang

2022

Mathematical Problems in Engineering

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With the increasing application and use of information technology in college music classrooms getting deeper and deeper, the role of music informatization teaching classrooms becomes more and more prominent. In this context, it will be an important research topic to strengthen information technology education, promote the innovation and development of music education in colleges and universities, and build a rich information technology music classroom. Artificial intelligence technology’s implementation in the area of music education is developing and becoming more common, and to some extent, it has become a stimulant of music teaching, although there are certain negatives. The arrival of wireless networks, especially 5G, has drawn strong attention from all walks of life, and its characteristics of “large bandwidth, ultralow latency, and connectivity of everything” have been applied and explored in the field of music education, making the teaching mode, teaching methods, and interactive experience develop in a better direction. At the same time, VR, AR, AI, big data, and other technologies based on a wireless network have also been optimized and applied to the field of music education, which will further promote the development of intelligent music education. This study examines the impact of informatization on music education creativity in academic institutions and then goes on to address informatization tactics in music education innovation practice in order to develop a new model of music education digitization that is current. Focusing on music education, the applications of artificial intelligence in music education are listed, and a music education framework based on artificial intelligence algorithms and wireless networks is proposed. Finally, the proposed framework’s usefulness is demonstrated in relevant tests, and the prospects for artificial intelligence technology in the realm of music are examined.

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Exploring Key Competencies and Professional Development of Music Teachers in Primary Schools in the Era of Artificial Intelligence

Cited by 10 publications

References 31 publications

Correlations Between Teaching and Scientific Research Ability and Professional Development of College Teachers

Correlations Between Teaching and Scientific Research Ability and Professional Development of College Teachers

Design and Application of Machine Learning-Based Evaluation for University Music Teaching

Application of Artificial Intelligence Technology in Music Education Supported by Wireless Network

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