AHP Evaluation Algorithm based on Balanced Data Mining Algorithm for Kindergarten Teachers Equipped with Intelligent Hardware

• Published in: 2022 International Conference on Edge Computing and Applications (ICECAA) pp. 221 - 224
• Main Author: Yue, Huilan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.10.2022
• Subjects: AHP Evaluation Algorithm; Analytical models; Balanced Data Mining; Clustering algorithms; Education; Hardware; Heuristic algorithms; Intelligent Hardware; Kindergarten Teachers; Prediction algorithms; Time series analysis
• DOI: 10.1109/ICECAA55415.2022.9936367

In order to make the teaching quality evaluation model of colleges and universities more democratic, more scientific and reasonable, and to eliminate the interference of human subjective factors, a reasonable model of teaching evaluation can be established through the analytic hierarchy process (AHP). The key factors affecting the staffing of kindergarten teachers are analyzed from three aspects: the social level, the government level and the kindergarten level. Finally, the guarantee measures to promote the reasonable allocation of kindergarten teachers are put forward: breaking the dual structure of urban and rural education, and coordinating the development of urban and rural preschool education. The load intensity of these clusters is predicted according to the time series, and then the EF88 algorithm is used to dynamically allocate them to each real cluster system. server, so that the entire system load balance. The experimental and analytical results show the effectiveness of the method.