With the prevalence of artificial intelligence technologies, big data has been utilized to higher extent in many cross-domain fields. This paper concentrates on the digital assessment of vocational education level in some specific areas, and proposes a graph neural network-based assessment model for this purpose. Assume that all vocational colleges inside a specific region are with a social graph, in which each college is a node and the relations among them are the edges. The graph neural network (GNN) model is formulated to capture global structured features of all the nodes together. The GNN is then employed for the sequential modeling pattern, and the evolving characteristics of all the colleges can be captured. Some experiments are also conducted to evaluate the performance of the proposed GNN-VEL. It is compared with two typical forecasting methods under evaluation of two metrics. The results show that it performs better than other two methods. 

A Graph Neural Network-based Digital Assessment Method for Vocational Education Level of Specific Regions

The educational resources of colleges and universities(CU) are an important manifestation of the country's comprehensive strength.In order to make all aspects of the school better serve teaching, provide the people with high-quality, efficient, high-quality and high-level learning environment, and improve students' sense of autonomy, it is necessary to achieve scientific and reasonable allocation of educational resources in CU.This paper mainly uses digital modeling and system testing to compare and analyze the data of digital management(DM) of college education(CE) resource allocation.The experimental data shows that the CPU utilization of the database reaches a minimum of 12% when the response time is the longest, and exceeds 20% when the initial capacity is 50. 

pdf/digital-management-of-educational-resource-allocation-in-1mgg0strbw.pdf

Digital Management of Educational Resource Allocation in Colleges and Universities Based on PSO Algorithm

Human resource planning is the first thing an organization or company must do when a company seeks better human resource management. All organizations or companies must determine their future with different plans according to the demands of the times. Nowadays changes are rapid, so organizations or companies must be able to keep up with changes quickly and precisely. Organizations or companies must be able to anticipate and adapt to change. Therefore, personnel planning is becoming increasingly important for companies, due to the globalization of new technology and organizational change processes overshadowing organizational life repeatedly. Organizations that are not supported by employees which is in accordance with the quantity, quality, strategy and function that is good, of course the organization will find it difficult to maintain and develop its existence in the future, so that personal planning is successful. Influencing factors hi environmental factors, organizational decisions, employee supply factors. 

https://journal.ypidathu.or.id/index.php/jiem/article/download/69/28

Human Resource Management (Human Resource Planning)

Human resources are limited, precious, irreplaceable and unable to imitate. They are the basis of an organization’s competitive advantage. By employing and training talented employees and improving their contribution to the company, human resource management can create a sustainable competitive advantage for the company. Under the background of the new economy, various forces such as knowledge, informatization and globalization are affecting the actual connotation of human resources. Human resources managers are required to stand at the height of organizational development strategy, analyze and diagnose the situation of human resources, and provide valuable information for decision-making. Therefore, human resources management presents the characteristics of strategy. However, many enterprises do not know how to formulate human resources strategy, and lack appropriate tools to obtain the necessary information to support decision-making. With the advancement of campus informatization, colleges and universities have generally established human resource information systems and accumulated a large number of employee data and business data. However, many colleges and universities only use these data to complete some necessary administrative tasks, such as salary calculation and personnel records, but do not make use of the huge potential value contained behind the data. If we use human resource data in the process of decision-making, extract useful information about employees’ behavior and potential, and convert these data into useful information to support decision-making, it will be conducive to the realization of the organization’s strategy. 

Application of Data Mining in Human Resource Management in Colleges and Universities

Human resources training is considered an effective solution in empowering human resources. Organizations try to have effective educational planning for this precious resource by identifying shortcomings through a need assessment. This study provides a model based on organizational data analysis to achieve a unique and appropriate training planning for each staff. Therefore, job performance, organizational promotion and lay-off have become the basis for staff training planning. For this purpose, the tax assessor’s information was investigated. Then, the CRISP-DM methodology was selected, and the project was implemented. Furthermore, a decision tree model was selected to extract unknown rules and patterns in the educational decision-making staff; the neural network model was selected as the predictive model to predict the target variables. The results revealed the decision tree for predicting job performance variables and organizational promotion status, and the neural network model was more effective in predicting service lay-off variables.

A novel prediction model for educational planning of human resources with data mining approach: a national tax administration case study

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A novel prediction model for educational planning of human resources with data mining approach: a national tax administration case study