ORCID Profile
0000-0002-3200-583X
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Publisher: Conscientia Beam
Date: 25-05-2022
Abstract: In an era where teachers are increasingly expected to perform at their level best, issues of self-efficacy and professional development continue to be an area of continuous debate. A random s le of primary and secondary school teachers in five cities in mainland China was selected to respond to the self-evaluation questionnaire, and 300 valid questionnaires were collected. The research was carried out to (1) investigate the levels of self-efficacy of music teachers in primary and secondary schools, (2) determine the factors that influenced music teachers' self-efficacy, and (3) explore the relationship between teachers’ self-efficacy and professional development. The data were analysed using descriptive statistics, T-test, analysis of variance, and regression. First, the results showed that the mean value of music teachers’ self-efficacy was 7.16. Second, the middle school teachers reported lower self-efficacy compared to teachers in the primary schools. Third, although teachers aged between 20 to 50 years old reached their highest level of self-efficacy, it decreased to a lower value after an extended period. Additionally, the study also revealed a significant difference in self-efficacy among teachers with different teaching experiences. Although educational backgrounds, gender, and training hours did not influence self-efficacy, a positive correlation was reported between self-efficacy and professional development. Prospective research might better consider developing music teachers’ self-efficacy and education across primary and secondary schools.
Publisher: Association for Information Communication Technology Education and Science (UIKTEN)
Date: 29-05-2023
DOI: 10.18421/TEM122-55
Abstract: Although music learners’ metacognition could regulate and improve their practice and performance, respectively, metacognitive measurement tools for music domains remain scarce. Hence, this study aimed to develop and validate a Metacognitive Questionnaire for Music Learning (MQML). Through item analysis, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA), two rounds of research were conducted on 513 valid questionnaires to analyze the item discrimination, reliability, and validity of the questionnaire. This study developed a questionnaire consisting of two subscales, eight factors, and 35 items. The MQML was explored and validated with a Cronbach’s alpha value of 0.960 for the overall scale, subscale Cronbach’s alpha values were 0.914 and 0.947. An eight-factor metacognitive structural model was validated for the first time in the music field. Essentially, MQML could provide empirical data for music students’ metacognition strategies and expand the current body of literature on music cognition.
Publisher: Hindawi Limited
Date: 16-04-2022
DOI: 10.1155/2022/3913235
Abstract: At present, machine learning can bring new research ideas to researchers on existing problems in some fields and lower the application threshold in this field, such as data mining and database fields. It has become the most cutting-edge research direction of artificial intelligence. For one, this article delves into a data mining model based on machine learning. Data intelligence combines the way of thinking that mimics the human brain with data mining theory, which has a great effect on data enhancement, and applies the theory of data intelligence search model to practical problems. The concept of the number of intelligent objects with the same data is proposed, and the intelligent search model of continuous data in this field is established to provide help to solve the intelligent search problem under the background of big data. This paper also studies the music database management and control system. Nowadays, there are many kinds and numbers of music resources, which has caused confusion in management. In addition, the existing database system is difficult to expand and reuse. In response to the above situation, this paper designs a standard-based universal custom music database system. Based on the comprehensive research and analysis of system requirements and functions, it completes the formulation of the music data element, public data element standard, and the development and realization of the universal music database management and control system. Finally, passed a complete functional test and interface test. Based on the research of machine learning, this paper examines the data intelligent mining model and the music database management and control system, respectively, creates an intelligent management and control system and a search model, and lays the foundation for the development of intelligence.
No related grants have been discovered for Wen Li.