مقاله انگلیسی رایگان در مورد حالت نوآوری مدیریت منابع انسانی استادان دانشگاه – هینداوی ۲۰۲۲

Abstract

     In the era of knowledge economy, the competition between countries and enterprises is increasingly manifested in the competition of talents and education system. In this article, aiming at the drawbacks of the traditional HRM model of university teachers, we design and construct a management innovation model and decision-making model based on intelligent big data analysis. This article introduces DM technology. It also introduces the related knowledge of DM and the analysis and design process of HRM decision system. In this system, DM technology is used to analyze and process the existing data, predict the future situation, and provide auxiliary support for decision-making. Through simulation, the decision-making accuracy of this model can reach 95.68%, which is about 10.02% higher than other systems. It has certain practicability and reliability. This article makes a useful attempt for the application of DM technology in HRM. The research in this article is expected to play an important decision-making reference and service support for HRM of university teachers and further promote the development of HRM innovation of university teachers.

Introduction

     In the modern environment, the demand for knowledge and intellectual capital in society is stronger than in any previous era, which leads to the aggravation of talent shortage. In some ways, economic growth mainly depends on the quality of labor force. Therefore, human resources are increasingly becoming strategic resources for economic and social development [1]. The competition between countries and enterprises is increasingly manifested as the competition of talents and the competition of educational system. As the quality of teaching in institution of higher learning directly determines the level of knowledge and personal quality of talents, the society has paid more attention to the management of college teachers who train senior talents [2]. However, the traditional management mode is increasingly showing many drawbacks. For example, the ideas of managers and managed people lag behind; the management mode is relatively closed and rigid; operating mechanisms such as competition mechanism, incentive mechanism, interest mechanism, responsibility mechanism, and flow mechanism are not well introduced into the management of teachers; the overall quality of teachers needs to be further improved; and teachers’ resources have not been effectively allocated and developed. College teachers are the direct undertaker of college production activities and the main body and core of college human resources. The quantity, quality, and structure of teachers’ resources directly determine the educational quality and school-running efficiency of institution of higher learning and then affect the national human resources and the level of economic and social development [3]. The purpose of human resource management of teachers in institution of higher learning is to mobilize the enthusiasm and creativity of every faculty member to the maximum extent and make more contributions to the school. Under the background of economic globalization, in order to realize the scientific task of human resource management in institution of higher learning, we must actively and effectively reform and innovate the human resource management of college teachers.

Conclusions

     Human resources, in comparison to other economic resources, have a distinct purpose, subjective initiative, and unique creativity. Traditional human resource management modes and information acquisition methods limit the development of human resource management in higher education institutions. In light of the growing drawbacks of traditional HRM, this article investigates various issues in HRM in higher education institutions using intelligent big data analysis technology and related HRM and DM theories. This article develops and implements an innovative mode and decision-making model for university teachers’ human resource management. This system employs DM technology to analyze and process existing data and forecast future events and provide decision-making support. The outcome of the experiment is in line with the hypothesis. This model has a decision-making accuracy of 95.68%, which is 10.02% higher than other systems. This system can significantly reduce the workload of intelligent scheduling of university teachers’ human resources based on big data analysis as well as the time it takes to fuse and calculate stage task data. It is feasible and practicable in some ways. This research has yielded some results, but due to my limited knowledge and research time, there are still some areas that could be improved. The model structure will be improved in future work, as will the model performance, and a quantitative analysis scheme will be proposed to solve the HRM problems of university teachers.