Innovative Mode of Human Resource Management of University Teachers Based on Intelligent Big Data Analysis

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.

Big Data Analysis in Human Resources Decision Making: Optimizing Workforce Management

“…Big Data significantly contributes to employee recruitment by improving selection methods (Kumar et al, 2021). Big Data analytics can identify employee success patterns, predict future performance, and provide deep insight into the candidate characteristics that best suit an organization's needs (Bai, 2022).…”

“…Organizations can use Big Data to gain richer insights about each candidate, including their skills, experience, and values (Nocker & Sena, 2019). In-depth analysis of previous employee performance data can help detail patterns of success, thereby increasing the accuracy of predictions for candidates who have the potential to make maximum contributions (Bai, 2022).…”

“…Applying Big Data in Human Resource Decision Making can build organizational performance based on HR decision-making by providing a solid basis for human resource management strategies that are more effective, adaptive, and oriented toward business goals (Azman Shah et al, 2021). By analyzing big data related to employee performance, satisfaction, and operational efficiency, organizations can make more intelligent and more informed HR decisions (Bai, 2022). Big Data enables a deep understanding of performance factors, enabling more informed decisions regarding employee placement and development (Nocker & Sena, 2019).…”

Implementasi Manajemen Tenaga Pendidik dan Kependidikan dalam Meningkatkan Prestasi Siswa Akademik dan Non Akademik

“…Salah satu komponen yang mampu dan berhasil meningkatkan kemajuan madrasah adalah pendidik dan tenaga kependidikan. Pendidik bertanggung jawab untuk mengajar, membimbing, serta mengevaluasi peserta didik baik jenjang dasar, menengah dan keatas (Bai, 2022;Burhan, 2024;Hardiyanto, 2020;Lai, 2019;Rajeswaran, 2023;Shaw, 2019). Manajemen tenaga pendidik dan kependidikan adalah aktivitas yang harus dilakukan mulai dari tenaga pendidik dan kependidikan, organisasi pendidik sampai akhirnya berhenti melalui proses perencanaan SDM, perekrutan, seleksi, penempatan, pemberian kompensasi, penghargaan, pengembangan dan pemberhentian (Suarga, 2019).…”

A Resource-based view Assessment of Big Data Analysis and its Impact on Strategic Human Resources Quality Management Systems

“…By taking massive data flow in structured and unstructured forms, companies have a chance to discover various patterns, trends, and links of employees' performance, engagement, and commission. Such discoveries place HR professionals in a strong position to make relevant decisions, individualize employee experiences and optimize HR programs so that they fully exhibit organizational vision [3]. While the big data capability of an organization to build and enact an HR QMS determines its success, the effectiveness with which resources are deployed to do so is of paramount importance.…”