A Study on Satisfaction and Influencing Factors of Educational Practice of Preschool Education Majors in Higher Vocational Colleges.

In September 2020, the Ministry of Education and other nine departments issued the notice of "Action Plan for Improving the Quality of Vocational Education (2020-2023)" which emphasizes strong practical teaching. The state pays more and more attention to the quality of practical practice in higher vocational colleges. From the perspective of student satisfaction, this study explores the satisfaction of preschool education students on educational practice and related influencing factors, so as to carry out better educational practice and improve the quality of students' educational practice in colleges and universities. This study uses the questionnaire method, according to the existing literature and theory to build the results of self-compiled "higher vocational college students majoring in preschool education practice satisfaction and influencing factors questionnaire." A total of 463 students majoring in preschool education in higher vocational colleges were investigated. The following research results are obtained. 1. The overall satisfaction of students majoring in preschool education in higher vocational colleges to educational practice is above the average. In descending order of satisfaction question scores, other factors influencing student's satisfaction are the tutors in the educational practice park, the educational practice base, the content of educational practice, the management of educational practice, the tutors in the school of educational practice, and the time of educational practice. 2. For constructing the influencing factor model of educational practice satisfaction, it is found that the influencing factors of educational practice satisfaction are university management rational factors, support factors from the educational base, and students' own factors.

Design of a Smart Art Classroom System Based on Internet of Things.

With the rapid development of the Internet of Things and to improve the teaching efficiency of the art classroom, a smart art classroom system based on the Internet of Things is proposed, which can effectively assist in teaching. First, we give the general design of the smart art classroom, including the composition of the hardware and software, and the construction method of the application system. Based on existing technologies such as RFID, smart camera, smart voice, smart terminal, and smart screen interaction, an all-around smart art classroom is constructed. Further, we present the design of an intelligent camera-based classroom assistance system based on face detection and facial expression recognition, which can effectively determine the status of students in class and can be used to assist in reminding teachers of their teaching tasks. Among them, face detection and facial expression recognition algorithms are designed based on different convolutional neural network architectures. Finally, experimental data sets are constructed to verify the accuracy of the used algorithms. The experimental results show that the detection accuracy of classroom faces is better than 95% and the accuracy of expression recognition is 88%, which can meet the application needs of intelligent art classrooms.

Music Feature Extraction Method Based on Internet of Things Technology and Its Application.

Due to the influence of factors such as strong music specialization, complex music theory knowledge, and various variations, it is difficult to identify music features. We have developed a music characteristic identification system using the Internet-based method. The physical sensing layer of our designed system deploys audio sensors on various coordinates to capture the raw audio signal and performs audio signal processing and analysis using the TMS320VC5402 digital signal processor; the Internet transport layer places audio sensors at various locations to capture the raw audio signal. The TMS320VC5402 digital signal processor is used for audio signal diagnosis and treatment. The network transport layer transmits the finished audio signal to the data base of song signal in the application layer of the system; the song characteristic analysis block in the application layer adopts dynamics. The music characteristic analysis block in the applied layer adopts dynamic time warping algorithm to acquire the maximal resemblance between the test template and the reference template to achieve music signal characteristic identification and identify music tunes and music modes based on the identification results. The application layer music feature analysis block adopts dynamic time regularization algorithm and mel-frequency cepstrum coefficient to achieve music signal feature recognition and identify music tunes and music patterns based on the recognition results. We have verified through experiments, and the results show that the system operates consistently, can obtain high-quality music samples, and can extract good music characteristics.