Due to the poor effect of the traditional vocational education platform, it is difficult to meet the requirements of the current Internet education platform. To solve this problem, this paper designs a vocational education platform system based on blockchain and Internet of Things (IoT) technology. First, the system access control strategy based on blockchain technology is introduced into the teaching system so as to improve the security of the teaching system. Then, in order to solve the single point of failure and efficient transmission problems of access control in the IoT environment, this paper proposes an access control strategy based on main side chain cooperation. This strategy introduces a side chain to expand the blockchain and improve the data carrying capacity of the blockchain. To meet the requirements of high-speed data transmission, this paper designs an access control model suitable for the main side chain blockchain structure. Experimental results show that compared with other algorithms, the data request speed of the system in this paper is faster, and the storage space is smaller. Therefore, the proposed technology based on blockchain and IoT can meet the use requirements of vocational education platforms.
Preschool education (PE) is the initial stage of life education, and early childhood is an unrepeatable process. PE has the same importance as other education stages because of the significant impact it can have on later childhood development. Furthermore, from the perspective of educational equity theory, every child has the right to receive PE, the right to obtain the same high-quality educational resources, and the right to fair final results. Therefore, the research on the quality of PE has theoretical value and practical significance. In order to strengthen the quality of PE, this paper designs a PE quality assessment system to evaluate teachers’ teaching achievements. In this regard, the performance of each functional module in the system is tested, and the test results show that the module access is successful at more than 97%, indicating that the system meets the operating requirements. This paper uses the characteristics of the KNN algorithm classification in the machine learning algorithm to classify the teaching quality (TQ) of 7 pre-school teachers, and obtains the membership degrees of teachers in the four categories of grades, indicating that the KNN algorithm is more suitable for the classification of TQ assessment results than the general classification algorithm.
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